Cognitive Biases in Intelligence Analysis: Amplification Mechanisms and Intervention Strategies in the Digital-Intelligent Era

Cognitive Biases in Intelligence Analysis: Amplification Mechanisms and Intervention Strategies in the Digital-Intelligent Era | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Cognitive Biases in Intelligence Analysis: Amplification Mechanisms and Intervention Strategies in the Digital-Intelligent Era yu peng Huo, litongxing yu, yushan ji This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8698493/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Apr, 2026 Read the published version in BMC Psychology → Version 1 posted 14 You are reading this latest preprint version Abstract Background and Aim The digital-intelligent era, characterized by information explosion, algorithmic penetration, and multi-tasking pressure, has reshaped the information processing environment for intelligence analysis. In high-stakes military scenarios, interactions between cognitive biases and technical factors drive intelligence judgment errors, with profound impacts on battlefield outcomes and national security. Existing studies lack systematic analysis of how digital-intelligent technical characteristics amplify these biases and in-depth discussion of cognitive psychology-based targeted intervention strategies. This study aims to explore typical cognitive bias manifestations across the cognitive process of intelligence analysis, reveal technical amplification mechanisms, construct an “individual-technical-organizational” three-dimensional model, and propose targeted intervention strategies to enrich relevant research and improve intelligence judgment accuracy. Methods A mixed-method research design was adopted, including systematic literature review, case coding analysis, and theoretical deduction. A systematic review of cognitive psychology, intelligence studies, and human-computer interaction literature was conducted. Ten typical intelligence failure cases were selected for qualitative coding analysis. Cognitive psychology theories guided the construction of the “technical environment - cognitive process - bias amplification - judgment error” framework, verified via case cross-validation. Coding reliability was evaluated using the Kappa coefficient. Results Cognitive biases permeate the entire cognitive process from perception to decision-making, forming chain reactions. Digital-intelligent factors amplify biases through mechanisms such as cognitive resource scarcity, cognitive anchor solidification, and reduced cognitive processing depth. Bias formation and amplification result from the interaction of individual, technical, and organizational factors. The “cognitive training - technical optimization - organizational adjustment” comprehensive strategy effectively mitigates bias impacts. Conclusions This study enriches the digital-intelligent era cognitive psychology theoretical system by revealing the interaction mechanism between technical environments and cognitive biases. The three-dimensional model and intervention strategies provide a new perspective for military cognitive bias research and universal reference for high-stakes military decision-making. Future research will verify strategy effectiveness through longitudinal tracking and controlled experiments. Cognitive Bias Digital-Intelligent Environment Intelligence Analysis High-Stakes Decision-Making Intervention Strategy Cognitive Psychology 1. Introduction 1.1 Research Background Emerging digital technologies are reshaping the operational models of organizations, bringing both unprecedented opportunities and disruptive challenges (Haugom et al., 2025 ). This wave of digital transformation is driven by two core trends: on one hand, the explosive growth of digital information from sources such as social media, Internet of Things (IoT) devices, online transactions, and public registers; on the other hand, the rapid advancement of computer capacity and performance, enabling the efficient collection and utilization of multi-source heterogeneous data (Haugom et al., 2025 ). As a core link in the intelligence workflow, intelligence analysis involves processing complex information and making accurate judgments, and it is now confronting profound changes in the digital-intelligent era. On the positive side, technologies such as big data analytics and AI-driven reconnaissance have significantly enhanced the efficiency of information collection, processing, and analysis in intelligence work; on the negative side, technical characteristics including data overload, algorithm bias, and technological dependence have introduced new cognitive risks, amplifying inherent human cognitive biases and thereby increasing the probability of intelligence judgment errors. Cognitive bias refers to systematic deviations in human information processing and decision-making arising from inherent limitations of cognitive capacity, psychological tendencies, and environmental influences (Ding et al., 2020 ). In intelligence analysis, even minor cognitive biases can lead to catastrophic consequences. For instance, during the Yom Kippur War, Israel's intelligence community (IDI) was trapped in a rigid analytical paradigm known as "the conception," which was formed through years of practice and had been mistakenly validated on multiple occasions (Ze’ira, 2004 )( Bar Joseph, 2005 ). Michael J. Ard evaluates the key findings of January 6 as an alleged intelligence failure event, how HUMINT has been collected in the U.S. on extremist groups, and the feasibility of using such collection for preventive strategies (Ard, 2025 ). These cases demonstrate that cognitive biases are critical factors contributing to intelligence failures, and the digital-intelligent technical environment further complicates the interaction between cognitive biases and intelligence errors. Existing studies have made certain progress in exploring the relationship between cognitive biases and intelligence failures. Early research represented by Heuer ( 1999 ) systematically analyzed the impact of cognitive biases such as mirror imaging, confirmation bias, and groupthink on intelligence analysis from a cognitive psychology perspective and proposed structured analytical methods to mitigate such biases. However, obvious research gaps remain: first, most studies focus on the analysis phase of intelligence work while neglecting cognitive biases in the collection, utilization, and evaluation phases, lacking a systematic analysis of the full-chain mechanism of "technical environment - cognitive process - bias amplification - intelligence failure"; second, research on intervention strategies is mostly confined to theoretical discussions, lacking targeted design that integrates the technical characteristics of the digital-intelligent era with cognitive psychology mechanisms, as well as verification through practical cases; third, the research perspective is often limited to specific scenarios, failing to fully explore the universal laws of cognitive biases under the interaction of technology and cognition in the digital-intelligent era, which hinders the promotion of research results to diverse intelligence analysis scenarios. 1.2 Literature Review 1.2.1 Research on Cognitive Biases in Intelligence Analysis Research on cognitive biases in intelligence analysis originated from reflections on major intelligence failures. After the Pearl Harbor Incident, Wohlstetter ( 1962 ) proposed the "signal-noise theory" in his book *Pearl Harbor: Warning and Decision*, arguing that the inundation of valuable "signals" by irrelevant "noise" was a crucial cause of intelligence failures, laying the foundation for subsequent research on cognitive factors in intelligence work. Building on this theory, Handel ( 1989 ) further categorized the sources of "noise" into three dimensions: one's own side, the enemy, and the international environment, enriching the theoretical connotation of the signal-noise framework. In the 1990s, with the integration of cognitive psychology into intelligence studies, scholars began in-depth explorations of the role of cognitive biases. Heuer ( 1999 ) systematically examined cognitive biases such as mirror imaging, anchoring effect, confirmation bias, and groupthink in his landmark work *Psychology of Intelligence Analysis*, arguing that these biases stem from inherent limitations in human information processing (e.g., limited attention capacity, memory anchoring) and exert a profound impact on the entire intelligence chain from collection to decision-making. He emphasized that cognitive biases cannot be completely eliminated but can be alleviated through scientific methods such as structured analytical techniques (Heuer & Pherson, 2019 ). Subsequent studies have further expanded this field. Lemons & Beitler (2025) supplemented a new perspective by pointing out that sexism at both the organizational and individual levels can exacerbate cognitive biases, thereby increasing the risk of intelligence failures. Lillbacka ( 2022 ) proposed the concept of "pleasant attractor," arguing that when an organization maintains satisfactory outcomes through fixed strategies and avoids alternative strategies due to uncertainty or high costs, it creates an environment conducive to cognitive biases leading to intelligence failures. In terms of research methods, case analysis remains the primary approach. Scholars such as Shapira (2023) and Corrado(2023) have identified the role of cognitive biases by analyzing typical cases such as the Yom Kippur War and Korean War. However, existing studies lack quantitative analysis and empirical verification, and most focus on single biases or individual stages, resulting in insufficient systematicity and generalizability of research results. 1.2.2 Impact of Digital-Intelligent Technologies on Cognitive Activities in Intelligence With the popularization of digital-intelligent technologies, scholars have increasingly focused on the interaction between technical environments and cognitive activities in intelligence analysis. From the perspective of information processing, Galotti ( 2017 ) defined "selective attention" as the tendency of individuals to focus on specific tasks while filtering out competing information, noting that in the digital-intelligent era, the explosion of multi-source heterogeneous data (text, images, audio-visual content) has significantly amplified this cognitive limitation. In terms of algorithm dependence and technical reliance, Noy & Zhang ( 2023 ) found through experimental research that generative AI can significantly improve the efficiency of intelligence processing(Noy& Zhang, 2023 ). Liu ( 2025 ) supplemented this by noting that this level of usefulness has led people to increasingly depend on generative AI technologies, seamlessly integrating them into their work. Dylan & Stivang ( 2025 ) emphasized that the activity and processes of intelligence organisations will be dramatically disrupted by emerging technologies(Dylan & Stivang, 2025 ). 1.2.3 Research on Intervention Strategies for Cognitive Biases in Intelligence Analysis Current intervention strategies for cognitive biases in intelligence analysis mainly cover three dimensions: individual cognitive training, technical tool optimization, and organizational system improvement. In terms of individual training, Heuer & Pherson ( 2019 ) proposed structured analytical techniques such as "alternative analysis" and "devil's advocacy" in Structured Analytic Techniques for Intelligence Analysis , aiming to help analysts break free from fixed thinking patterns and mitigate biases such as confirmation bias and mirror imaging. In terms of technical optimization, scholars have noted that digital-intelligent technologies can assist in bias correction. For example, Ahronheim ( 2021 ) introduced the application of Israel's military AI system in real-time satellite image analysis, which helps detect rocket and missile launches, reducing the impact of human cognitive limitations through technical efficiency. Tucker ( 2020 ) noted that U.S. open-source enterprises use AI to scan and translate foreign-language newspapers, improving the efficiency of information collection while reducing subjective biases in manual processing. In terms of organizational improvement, Johnston ( 2005 ) emphasized that effective intelligence work requires cohesion, open communication, and shared mental models among team members, which are crucial for breaking information silos. However, existing intervention strategies still have limitations: first, the integration of multiple strategies is insufficient, with most focusing on a single dimension while ignoring the interactive effects between individual cognitive abilities, technical characteristics, and organizational culture; second, the targeting is weak—strategies are not designed for the specific amplification mechanisms of cognitive biases in the digital-intelligent era; third, the effectiveness of strategies lacks sufficient empirical verification, mostly remaining in the theoretical discussion stage, and failing to form a comprehensive protection system that adapts to the digital-intelligent era. For example, existing technical optimization strategies often overemphasize data processing speed while neglecting the need to avoid "data supremacy" and algorithmic bias, leading to new cognitive risks. 1.3 Research Significance 1.3.1 Theoretical Significance First, this study systematically explores the formation and amplification mechanisms of cognitive biases in intelligence analysis under the digital-intelligent background, constructing an "individual-technical-organizational" three-dimensional influencing factor model, which enriches the theoretical system of cognitive psychology in complex military information environments. Second, by revealing the interaction between technical factors and cognitive processes in intelligence analysis, this study expands the research boundary of cognitive bias theory, provides a new analytical framework for the study of cognitive biases in the digital age military field, and promotes the cross-integration of cognitive psychology, intelligence studies, and human-computer interaction. 1.3.2 Practical Significance First, this study identifies the typical manifestations and amplification paths of cognitive biases in the entire process of intelligence analysis, helping intelligence analysts better recognize and prevent cognitive biases, thereby improving the accuracy of intelligence judgment. Meanwhile, the research results of this study have universal reference value for other high-stakes military decision-making scenarios facing similar complex information environments, and can help relevant practitioners in these fields reduce the impact of cognitive biases and improve decision-making quality. 1.4 Research Questions and Framework 1.4.1 Research Questions Based on the above research background and literature review, this study focuses on the following core research questions: What are the typical manifestations of cognitive biases in each link of the cognitive process (perception, attention, memory, thinking, decision-making) of intelligence analysis in the digital-intelligent era? What are the amplification mechanisms of technical factors (data overload, algorithm dependence, rapid information dissemination, etc.) on various cognitive biases in intelligence analysis? What is the interaction mechanism between individual, technical, and organizational factors in the formation and amplification of cognitive biases in intelligence analysis? Based on cognitive psychology theories, how to design targeted intervention strategies to reduce the negative impact of cognitive biases in intelligence analysis? 1.4.2 Research Framework This study adopts a "problem analysis - mechanism exploration - strategy construction" research framework. First, through systematic literature review and case coding analysis, the typical manifestations of cognitive biases in intelligence analysis are sorted out; second, based on cognitive psychology theories, the amplification mechanisms of technical factors on cognitive biases are explored, and a three-dimensional influencing factor model is constructed; finally, targeted intervention strategies are proposed from the three levels of individual, technology, and organization. 2. Methods 2.1 Research Design A mixed-method research design integrating systematic literature review, case coding analysis, and theoretical deduction was adopted. The combination of multiple research methods can make up for the limitations of a single method, ensure the comprehensiveness and depth of the research, and improve the reliability and validity of the research results. Specifically, systematic literature review is used to sort out the research status and theoretical basis; case coding analysis is used to extract the typical manifestations of cognitive biases and the action mechanism of technical factors in intelligence scenarios; theoretical deduction is used to construct the analytical framework and intervention strategies. 2.2 Systematic Literature Review 2.2.1 Literature Search Strategy Literature searches were conducted in databases such as Web of Science, Scopus, CNKI, and Wanfang Data, with a search time range from 1990 to 2025. English search keywords included "cognitive bias," "intelligence analysis," "digital-intelligent era," "information processing," "intervention strategy"; Chinese search keywords included "cognitive bias," "intelligence analysis," "digital-intelligent era," "information processing," "intervention strategy." A combination of subject terms and free words was used, and search results were expanded through snowballing. 2.2.2 Literature Inclusion and Exclusion Criteria Inclusion criteria: 1. The research content is related to cognitive biases, intelligence analysis, and digital-intelligent technologies; 2. The research type is theoretical research, empirical research, or case study; 3. The literature is published in formal academic journals or monographs; 4. English literature is written in English, and Chinese literature is written in Chinese. Exclusion criteria: 1. Literature with irrelevant research content; 2. Conference abstracts, dissertations, and other non-formal publications; 3. Literature with low quality (such as lack of clear research questions and methods); 4. Duplicate literature. 2.2.3 Literature Sorting and Analysis After literature search and screening, a total of 236 relevant literatures were finally included, including 187 English literatures and 49 Chinese literatures. The included literatures were sorted out and analyzed from the aspects of research theme, research method, core findings, and research gaps, so as to lay the theoretical foundation for subsequent research. 2.3 Case Coding Analysis 2.3.1 Case Selection To ensure the representativeness and typicality of the cases, the following selection criteria were formulated: 1. The case is a major intelligence failure event that has attracted wide attention in academic circles and has sufficient research materials; 2. The case involves cognitive biases and technical factors, which is conducive to analyzing the interaction between the two; 3. The cases cover different time periods, regions, and types (inter-state conflicts, counter-terrorism operations, geopolitical confrontations, etc.), so as to avoid sample bias. Based on the above criteria, 10 typical cases were finally selected (see Table 1 ). Table 1 List of Selected Cases Case Name Time Region/Country Case Type Core Cognitive Biases Key Technical Factors Pearl Harbor Incident 1941 United States/Japan Inter-state Military Conflict Mirror Imaging, Signal-Noise Confusion Technical Reconnaissance Limitations Yom Kippur War 1973 Israel/Egypt/Syria Inter-state Military Conflict Confirmation Bias, Groupthink Information Sharing Barriers 9/11 Attacks 2001 United States/Al-Qaeda Counter-Terrorism Operation Attention Deviation, Organizational Barriers Data Overload, Information Fragmentation Indian Nuclear Crisis 1998 United States/India Geopolitical Confrontation Resource Allocation Bias, Anchoring Effect Insufficient Human Intelligence Collection Russia-Ukraine Conflict (Early Stage) 2022 Russia/Ukraine Inter-state Military Conflict Mirror Imaging, Overconfidence Bias Technical Dependence, Algorithm Bias Hamas's Strategic Deception (2023) 2023 Israel/Hamas Asymmetric Military Conflict Confirmation Bias, Complacency Bias Information Cocoon, Technical Reconnaissance Deception January 6 Capitol Attack 2021 United States Domestic Security Operation Attention Deviation, Groupthink Data Collection Deficiencies, Information Dissemination Chaos Afghan War (U.S. Intelligence Failure) 2001–2021 United States/Afghanistan Counter-Terrorism & Military Occupation Resource Allocation Bias, Confirmation Bias Information Collection Focus Deviation, Data Overload Barbarossa Operation 1941 Soviet Union/Germany Inter-state Military Conflict Authority Obedience, Confirmation Bias Political Intervention, Information Distortion Cuban Missile Crisis 1962 United States/Soviet Union Geopolitical Confrontation Mirror imaging, Signal-Noise Confusion Technical Reconnaissance Advantages, Information Verification Pressure Boston Marathon Bombing 2013 United States Counter-Terrorism Operation Attention Deviation, Information Sharing Barriers Fragmented Information, Data Processing Delay 2.3.2 Coding Framework Construction Based on cognitive psychology theories and the research questions of this study, a multi-level coding framework was constructed. The primary coding dimensions include cognitive bias types, technical amplification factors, organizational context factors, and error consequences; each primary dimension is divided into secondary dimensions and coding items. The coding framework was revised and improved through expert consultation (inviting 3 scholars in the fields of cognitive psychology and intelligence studies to put forward revision suggestions) to ensure the scientificity and operability of the coding framework. Table 2 Coding Framework for Case Analysis Primary Dimension Secondary Dimension Coding Items Cognitive Bias Types Perception Stage Mirror Imaging, Perceptual Filtering Bias Attention Stage Selective Attention Bias, Attention Fragmentation Memory Stage Anchoring Effect, Availability Heuristic Thinking Stage Confirmation Bias, Overconfidence Bias, Wishful Thinking Decision-Making Stage Groupthink, Authority Obedience, Complacency Bias Technical Amplification Factors Data-Related Data Overload, Data Heterogeneity, Data Falseness Algorithm-Related Algorithm Bias, Information Cocoon, Algorithm Dependence Technical Tool-Related Technical Reconnaissance Limitations, Technical Dependence, Information Processing Delay Information Dissemination-Related Rapid Information Dissemination, Information Fragmentation, Information Distortion Organizational Context Factors Communication Culture Closed Communication, Open Communication, Information Sharing Barriers Decision-Making Culture Centralized Decision-Making, Decentralized Decision-Making, Political Intervention Team Climate Conformity Tendency, Critical Atmosphere, Responsibility Avoidance 2.3.3 Coding Process and Quality Control The coding process was completed by two researchers independently, and the specific steps are as follows: first, the two researchers read the case materials in detail and conducted preliminary coding according to the coding framework; second, the coding results of the two researchers were compared and checked, and the inconsistent coding items were discussed and negotiated to reach a consensus; finally, the third researcher was invited to conduct a review of the coding results to ensure the accuracy and reliability of the coding. In order to test the reliability of the coding, the Kappa coefficient was used for evaluation. The results showed that the Kappa coefficient of the two researchers' coding results was 0.82, which was higher than the acceptable threshold of 0.7, indicating that the coding results had good reliability. 2.4 Theoretical Deduction Based on the results of systematic literature review and case coding analysis, combined with cognitive psychology theories such as cognitive resource theory, anchoring effect theory, and groupthink theory, as well as the technical characteristics of the digital-intelligent era, theoretical deduction was carried out. First, the formation mechanism of cognitive biases in each link of the cognitive process of intelligence analysis was analyzed, and the internal logical relationship between different cognitive biases was sorted out; second, the amplification mechanism of technical factors on cognitive biases was explored, and the interaction path between technical factors and cognitive processes was clarified; finally, the "individual-technical-organizational" three-dimensional influencing factor model of cognitive biases in intelligence analysis was constructed, and targeted intervention strategies were proposed based on the model. 3. Results 3.1 Typical Manifestations of Cognitive Biases in Intelligence Analysis (Based on Cognitive Process) Through case coding analysis and theoretical deduction, this study found that cognitive biases run through the entire cognitive process of intelligence analysis, and show different typical manifestations in each stage of perception, attention, memory, thinking, and decision-making. These biases are not isolated but interact with each other to form a chain reaction, which ultimately leads to intelligence judgment errors. 3.1.1 Attention Stage: Selective Attention Bias and Attention Fragmentation Attention should be paid to the concentration and focus of psychological activities, which is a selective, transferable, and decomposable concentration(Ding, 2020). Attention is the beginning of cognition and runs through the entire process of cognition. The limited capacity of attention determines that individuals can only focus on a part of information in the face of massive battlefield information (Galotti, 2017 ). In the attention stage of intelligence analysis, the typical cognitive biases are selective attention bias and attention fragmentation. Selective attention bias refers to the tendency of individuals to focus on information that is consistent with their own strategic interests, combat expectations, and cognitive frameworks, while ignoring information that is inconsistent with them. For example, during the Afghanistan War, due to the overemphasis on collecting intelligence on militant organizations, U.S. intelligence agencies neglected the understanding of basic information such as Afghanistan’s local economic conditions, land ownership status, and influential figures. This resulted in "the U.S. government being simply unable to make decisions on the measures to adopt in Afghanistan based on fully mastered information(Flynn, 2010)." Attention fragmentation refers to the phenomenon that individuals' attention is continuously interrupted by external interference factors in the complex battlefield environment, resulting in the inability to focus on deep processing of key intelligence information. In this situation, people usually repeat past choices (Wu et al., 2022 ). In the digital-intelligent era, the popularization of instant messaging tools, real-time data update alerts, and multi-service collaborative platforms has made attention fragmentation more prominent in intelligence analysis. 3.1.2 Memory Stage: Anchoring Effect and Availability Heuristic Memory refers to the process of individuals storing, maintaining, and extracting information (Robinson-Riegler, 2017 ). According to the duration of information storage, memory can be divided into sensory memory, short-term memory, and long-term memory(Goldstein, 2019 ). In the memory stage of intelligence analysis, the typical cognitive biases are anchoring effect and availability heuristic. Anchoring effect refers to the tendency of individuals to over-rely on the first received key information (anchor point) when making intelligence judgments, and adjust their subsequent judgments based on this anchor point, resulting in judgment deviations (Nikolopoulou, 2025 ). In the Indian Nuclear Crisis, the U.S. intelligence department's initial judgment on India's nuclear policy was based on India's previous statement of "peaceful use of nuclear energy," and this initial judgment became an anchor point. Even when there were a large number of signs of India's nuclear test preparations (such as the abnormal activities of nuclear facilities and the import of related materials), the U.S. intelligence department still did not make a timely adjustment to the initial judgment, leading to the failure to predict India's nuclear test. Availability heuristic refers to the tendency of individuals to judge the probability of a military event based on the ease of recalling similar events, that is, events that are easier to recall are considered to have a higher probability of occurrence. In the counter-terrorism intelligence work after the 9/11 Attacks, the U.S. intelligence department was deeply impressed by the terrorist attack method of using civil aircraft as weapons, and this method was easier to recall. Therefore, it focused on preventing similar attack methods, while ignoring other new terrorist attack methods such as using drones to carry explosives and launching cyber attacks, leading to the failure to effectively respond to emerging terrorist threats. 3.1.3 Thinking Stage: Confirmation Bias, Overconfidence Bias, and Wishful Thinking Thinking is the process of individuals indirectly and generally reflecting the essence and internal connections of objective things(Gao, 2017 ). In the thinking stage of intelligence analysis, the typical cognitive biases are confirmation bias, overconfidence bias, and wishful thinking. Confirmation bias refers to the tendency of individuals to actively search for, interpret, and accept information that is consistent with their own existing strategic beliefs and intelligence judgments, while ignoring or denying information that is inconsistent with their own beliefs (Casad, 2025 ). In the Russian-Ukraine Conflict, Russia's intelligence department believed that Ukraine's military strength could not resist Russia's military offensive, and only accepted information that was consistent with this judgment, while ignoring the information about Ukraine's military assistance from Western countries and the improvement of combat effectiveness through military training, leading to misjudgment of the war process (Dylan & Grossfeld, 2025 ). Overconfidence bias refers to the tendency of individuals to overestimate their own intelligence collection and analysis abilities and the accuracy of their judgments (Kahneman, 2011 ). In the 9/11 Attacks, the U.S. intelligence department overestimated the effectiveness of its own intelligence collection and sharing system, believing that it could detect and prevent terrorist attacks in time, but in fact, due to the lack of effective information integration and analysis, the terrorist attack was not prevented. Wishful thinking refers to the tendency of individuals to judge military events according to their own wishes and desires, rather than objective facts (Heuer, 1999 ). In the Barbarossa Operation, Stalin hoped that the Soviet Union could maintain peaceful relations with Germany to gain more time for military preparation, so he ignored a large number of intelligence about Germany's preparations for attacking the Soviet Union, believing that Germany would not violate the "Soviet-German Non-Aggression Pact," leading to the Soviet Union's lack of sufficient preparation for Germany's sudden attack and heavy military losses (Gao, 2017 ). 3.1.4 Decision-Making Stage: Groupthink, Authority Obedience, and Complacency Bias In the decision-making stage of intelligence analysis, the typical cognitive biases are groupthink, authority obedience, and complacency bias. groupthink, mode of thinking in which individual members of small cohesive groups tend to accept a viewpoint or conclusion that represents a perceived group consensus, whether or not the group members believe it to be valid, correct, or optimal. Groupthink reduces the efficiency of collective problem solving within such groups (Schmidt, 2025 ). Authority obedience refers to the tendency of individuals to over-rely on or obey the opinions of superiors or military experts, and ignore their own judgments and objective intelligence facts (McLeod, 2025 ). In the digital-intelligent era, "data authority" and "technical authority" have also become important factors leading to authority obedience in intelligence analysis. In the 2023 Hamas strategic deception case, Israel's intelligence department over-relied on the analysis results of AI systems and technical reconnaissance data, and ignored the information from human intelligence and on-site reconnaissance, because these technical results were considered to have "objective authority" (Seloom, 2025 ). Complacency bias refers to the tendency of individuals or groups to relax their vigilance and reduce their investment in intelligence collection and analysis due to past military successes or current military advantages, resulting in failure to detect potential military risks in time (Heuer, 1999 ). 3.2 Amplification Mechanism of Technical Factors on Cognitive Biases in the Digital-Intelligent Era Through case coding analysis and theoretical deduction, this study found that technical factors in the digital-intelligent era do not directly cause cognitive biases in intelligence analysis, but amplify the inherent cognitive biases of human beings through specific mechanisms, making the impact of biases more significant and the probability of intelligence judgment errors higher. 3.2.1 Data Overload: Exacerbating Cognitive Resource Scarcity and Attention Deviation Data overload refers to the phenomenon that the amount of battlefield information received by intelligence agencies exceeds their information processing capacity. In the digital-intelligent era, the popularization of big data technology and the networking of military equipment have made the amount of information collected by intelligence departments explode, and the problem of data overload has become increasingly prominent. Data overload amplifies cognitive biases in intelligence analysis mainly through the following mechanisms: First, data overload leads to the scarcity of cognitive resources, making intelligence analysts unable to conduct in-depth processing of information. According to cognitive resource theory, human cognitive resources are limited. When facing a large amount of battlefield information, analysts have to allocate limited cognitive resources to a large number of information processing tasks, resulting in each task being unable to obtain sufficient cognitive resources, and thus unable to conduct in-depth analysis and verification of key intelligence information. In the 9/11 Attacks, the U.S. intelligence department received a large number of fragmented information about terrorist activities before the attack, but due to data overload, analysts were unable to integrate and analyze these information in depth, leading to the failure to detect the terrorist attack plan (Gao, 2017 ). Second, data overload exacerbates selective attention bias in intelligence analysis. In the face of a large amount of battlefield information, analysts tend to choose information that is easy to process, consistent with their own cognitive frameworks, and related to their own strategic interests for processing, while ignoring information that is difficult to process, inconsistent with their own cognitive frameworks, and unrelated to their own strategic interests. In the digital-intelligent era, the diversity and complexity of military data make this selection tendency more obvious. 3.2.2 Algorithm Dependence: Strengthening Cognitive Anchor Solidification and Confirmation Bias Algorithm dependence refers to the tendency of intelligence agencies and analysts to over-rely on algorithm results in the process of information processing and decision-making, and reduce their own critical thinking and independent judgment (Liu, 2025 ). In the digital-intelligent era, algorithms have become an important tool for intelligence departments to process massive battlefield data, and algorithm dependence has become a common phenomenon. Algorithm dependence amplifies cognitive biases in intelligence analysis mainly through the following mechanisms: First, algorithm dependence strengthens the solidification of cognitive anchors. Algorithms usually generate initial intelligence analysis results based on existing military data and models. These initial results will become cognitive anchors for analysts, and analysts will adjust their subsequent judgments based on these anchors. Due to the trust in algorithms, analysts are often unwilling to question and revise the initial results of algorithms, leading to the solidification of cognitive anchors (Nikolopoulou, 2025 ). Second, algorithm dependence exacerbates confirmation bias in intelligence analysis. Most of the algorithms used by intelligence departments are based on machine learning technology. These algorithms will continuously optimize themselves according to the user's preferences and feedback, and push information that is consistent with the user's cognitive framework to the user, forming an "information cocoon" (Liu, 2025 ). In the process of using algorithms, analysts will continue to receive information that is consistent with their own existing beliefs, while rarely receiving contradictory information, which will further consolidate their existing beliefs and exacerbate confirmation bias. 3.2.3 Rapid Information Dissemination: Increasing Decision-Making Time Pressure and Reducing Cognitive Processing Depth In the digital-intelligent era, the life cycle of intelligence is significantly shortened. Rapid information dissemination amplifies cognitive biases in intelligence analysis mainly through the following mechanisms: First, rapid information dissemination increases decision-making time pressure. In the digital-intelligent era, the speed of changes in the international security situation and the enemy's military actions is significantly accelerated, and the life cycle of intelligence is significantly shortened. Intelligence departments must make decisions in a short time, otherwise the intelligence will lose its value. The increase in time pressure makes analysts unable to conduct in-depth analysis and verification of intelligence information, and can only make decisions based on intuitive judgment and past combat experience, thus increasing the impact of cognitive biases such as availability heuristic and anchoring effect. Second, rapid information dissemination reduces the depth of cognitive processing. In the face of a large amount of rapidly changing battlefield information, analysts have to continuously switch between different information, and cannot conduct in-depth processing of a certain piece of information. This kind of superficial information processing makes analysts more likely to be affected by the surface characteristics of information, and ignores the deep-seated connections and essential characteristics of information, thus increasing the impact of cognitive biases such as perceptual filtering bias and selective attention bias. 3.2.4 Technical Reconnaissance Limitations: Aggravating Signal-Noise Confusion and Perceptual Filtering Bias Technical reconnaissance limitations refer to the phenomenon that military technical reconnaissance tools and methods have inherent defects and are easily affected by external factors such as battlefield environment and enemy counter-reconnaissance measures, leading to insufficient or inaccurate information collection. In the digital-intelligent era, although military technical reconnaissance technology has made great progress, it still has certain limitations. Technical reconnaissance limitations amplify cognitive biases in intelligence analysis mainly through the following mechanisms: First, technical reconnaissance limitations lead to insufficient information collection, exacerbating signal-noise confusion. Military technical reconnaissance tools such as satellites, drones, and radar are easily affected by environmental factors such as weather, terrain, and electromagnetic interference, leading to insufficient or inaccurate information collection. At the same time, the enemy's counter-reconnaissance measures (such as camouflage, deception, and jamming) will also interfere with technical reconnaissance, making it difficult to distinguish effective intelligence signals from irrelevant noise. Second, technical reconnaissance limitations exacerbate perceptual filtering bias. Military technical reconnaissance tools usually collect information in a specific form (such as images, signals, electronic parameters, etc.), and analysts are accustomed to processing information in this form. When the enemy adopts new combat methods or deception measures that are not easy to be detected by technical reconnaissance tools, analysts are often unable to perceive and process this information due to perceptual filtering bias. 3.3 "Individual-Technical-Organizational" Three-Dimensional Influencing Factor Model of Cognitive Biases Based on the above analysis, this study constructs an "individual-technical-organizational" three-dimensional influencing factor model of cognitive biases in intelligence analysis. This model holds that the formation and amplification of cognitive biases in intelligence analysis are the result of the interaction of individual, technical, and organizational factors. Among them, individual factors are the internal basis, technical factors are the amplification channels, and organizational factors are the external environment. The three factors interact with each other to jointly affect the occurrence and development of cognitive biases. 3.3.1 Individual Factors: Internal Basis of Cognitive Biases Individual factors refer to the inherent characteristics of intelligence analysts, including cognitive ability, military experience background, personality traits, and motivational needs. These factors determine the initial sensitivity of analysts to cognitive biases and are the internal basis for the formation of cognitive biases. Cognitive ability refers to the ability of individuals to process intelligence information, including attention, memory, thinking, and reasoning abilities. Analysts with strong cognitive ability are more likely to conduct in-depth processing of battlefield information, identify potential cognitive biases, and make correct judgments; on the contrary, analysts with weak cognitive ability are more likely to be affected by cognitive biases. Military experience background refers to the work experience, educational background, and military cultural background of analysts. Analysts with rich intelligence work experience can better grasp the rules of intelligence analysis and reduce the impact of cognitive biases, but they are also more likely to be trapped in past combat experience and form fixed thinking patterns. Analysts with different educational backgrounds and military cultural backgrounds have different cognitive frameworks and thinking modes, and their sensitivity to different cognitive biases is also different. Personality traits refer to the stable psychological characteristics of individuals, such as self-confidence, openness, and critical thinking. Analysts with strong critical thinking are more likely to question their own judgments and the opinions of others, and reduce the impact of cognitive biases such as confirmation bias and authority obedience; on the contrary, analysts with weak critical thinking are more likely to be affected by these biases. Motivational needs refer to the internal driving forces that promote analysts to conduct intelligence analysis, such as job performance, military career development, and sense of national responsibility. Analysts with a strong sense of national responsibility are more likely to conduct in-depth analysis of battlefield information and reduce the impact of cognitive biases; on the contrary, analysts with strong self-interest motivation may deliberately ignore information that is inconsistent with their own interests, leading to the aggravation of cognitive biases such as confirmation bias. 3.3.2 Technical Factors: Amplification Channels of Cognitive Biases As analyzed in Section 3.2 , technical factors in the digital-intelligent era (data overload, algorithm dependence, rapid information dissemination, technical reconnaissance limitations, etc.) do not directly cause cognitive biases in intelligence analysis, but amplify the inherent cognitive biases of human beings through specific mechanisms. These technical factors are the amplification channels of cognitive biases, which make the impact of biases more significant. It should be noted that technical factors are not only the amplification channels of cognitive biases, but also can be used to reduce cognitive biases in intelligence analysis. For example, the application of artificial intelligence technology can help analysts process massive battlefield data, reduce the impact of data overload on cognitive biases; the design of anti-information cocoon algorithms can help analysts break through the limitations of their own cognitive frameworks and reduce the impact of confirmation bias. Therefore, the key to technical factors lies in how to use them reasonably to reduce the amplification effect of cognitive biases. 3.3.3 Organizational Factors: External Environment of Cognitive Biases Organizational factors refer to the characteristics of intelligence agencies, including communication culture, decision-making culture, team climate, and incentive mechanisms. These factors form the external environment for analysts to conduct intelligence analysis, and have an important impact on the formation and amplification of cognitive biases. Intelligence work is a team activity. An effective team requires cohesion, formal and informal communication, collaboration, a shared mental model, and similar knowledge structures (Johnston, 2005 ). An open communication culture is conducive to the sharing of battlefield information and the expression of different views, which can reduce the impact of cognitive biases such as groupthink and information sharing barriers; on the contrary, a closed communication culture will limit the sharing of information and the expression of different views, leading to the aggravation of these biases. Decision-making culture refers to the norms and patterns of decision-making within the intelligence organization. A decentralized decision-making culture is conducive to giving full play to the initiative and creativity of analysts, reducing the impact of cognitive biases such as authority obedience and political intervention; on the contrary, a centralized decision-making culture will strengthen the authority of superiors and increase the impact of these biases. Team climate refers to the psychological atmosphere within the intelligence team, including cohesion, mutual trust, and critical atmosphere. A team with a good critical atmosphere is conducive to the expression of different views and the questioning of existing judgments, which can reduce the impact of cognitive biases such as groupthink and complacency bias; on the contrary, a team with a strong conformity tendency will suppress the expression of different views, leading to the aggravation of these biases. Incentive mechanisms refer to the systems and methods used by intelligence organizations to motivate analysts, including performance evaluation, reward and punishment systems (Heuer, 1999 ). A scientific incentive mechanism is conducive to encouraging analysts to conduct in-depth analysis of battlefield information and put forward objective and accurate judgments, reducing the impact of cognitive biases such as confirmation bias and self-interest motivation; on the contrary, an unreasonable incentive mechanism will encourage analysts to cater to the superiors' intentions or pursue short-term interests, leading to the aggravation of these biases. 3.4 Effectiveness of Comprehensive Intervention Strategies Based on the "individual-technical-organizational" three-dimensional influencing factor model, this study proposes a comprehensive intervention strategy combining "cognitive training - technical optimization - organizational adjustment". Through case analysis and theoretical deduction, this study verifies the effectiveness of the strategy in reducing the negative impact of cognitive biases in intelligence analysis. 3.4.1 Cognitive Training: Improving Individuals' Bias Correction Ability Cognitive training refers to the training of intelligence analysts' cognitive abilities and thinking modes to improve their ability to identify and correct cognitive biases. The main content of cognitive training includes critical thinking training, structured analysis method training, and perspective-taking training. Critical thinking training aims to improve analysts' ability to question and verify intelligence information, and reduce the impact of cognitive biases such as confirmation bias and authority obedience (Heuer & Pherson, 2019 ). For example, through case analysis and group discussions of typical intelligence failures, analysts are guided to question their own judgments and the opinions of others, and learn to find and verify the evidence supporting and opposing a certain judgment. Structured analysis method training aims to help analysts establish a scientific intelligence information processing framework, reduce the impact of cognitive biases such as anchoring effect and availability heuristic (Heuer & Pherson, 2019 ). For example, training analysts to use methods such as "alternative analysis" and "devil's advocacy" to comprehensively consider various possible enemy intentions and combat scenarios, and avoid being limited to a single way of thinking. Perspective-taking training aims to help analysts understand the thinking mode and behavioral logic of the enemy, and reduce the impact of cognitive biases such as mirror imaging (Heuer, 1999 ). For example, through role-playing and cross-cultural military exchange training, analysts are guided to stand in the perspective of the enemy to think about military strategies and operational intentions, and avoid inferring the enemy's intentions based on their own military thinking. 3.4.2 Technical Optimization: Reducing the Amplification Effect of Technical Factors Technical optimization refers to the improvement and optimization of technical tools and methods used in intelligence work to reduce the amplification effect of technical factors on cognitive biases. The main content of technical optimization includes anti-data overload technology development, anti-information cocoon algorithm design, and technical reconnaissance system improvement. Anti-data overload technology development aims to help analysts process massive battlefield data efficiently and reduce the impact of data overload on cognitive biases. For example, developing intelligent information filtering and sorting technology to automatically filter out irrelevant information and sort out key intelligence information, reducing the burden of information processing for analysts. Anti-information cocoon algorithm design aims to help analysts break through the limitations of their own cognitive frameworks and reduce the impact of confirmation bias. For example, designing algorithms that can push heterogeneous military information to analysts, ensuring that analysts can receive not only information consistent with their own cognitive frameworks but also contradictory information, and promoting analysts to conduct comprehensive thinking. Technical reconnaissance system improvement aims to improve the accuracy and comprehensiveness of military technical reconnaissance and reduce the impact of technical reconnaissance limitations on cognitive biases For example, developing multi-sensor fusion technology to integrate information collected by different technical reconnaissance tools (such as satellites, drones, and radar), improving the ability to distinguish effective signals from noise; strengthening the research and development of anti-counter-reconnaissance technology to cope with the enemy's camouflage and deception measures. 4. Discussion 4.1 Theoretical Implications This study systematically explores the formation and amplification mechanisms of cognitive biases in intelligence analysis under the digital-intelligent background, and constructs an "individual-technical-organizational" three-dimensional influencing factor model. This model breaks through the limitations of existing studies that focus on single factors, and reveals the interaction mechanism between individual, technical, and organizational factors in the formation and amplification of cognitive biases. At the same time, this study finds that technical factors in the digital-intelligent era amplify cognitive biases through specific mechanisms such as cognitive resource scarcity, cognitive anchor solidification, time pressure increase, and signal-noise confusion, which enriches the understanding of the impact of technical environments on cognitive biases in intelligence analysis and expands the research boundary of cognitive bias theory in complex military information environments. 4.2 Practical Implications The comprehensive intervention strategy combining "cognitive training - technical optimization - organizational adjustment" proposed in this study provides a practical operation scheme for intelligence agencies to reduce cognitive biases. Intelligence agencies can carry out targeted cognitive training for analysts to improve their ability to identify and correct cognitive biases; optimize technical tools and methods to reduce the amplification effect of technical factors on cognitive biases; adjust the organizational structure, culture, and system to build a supportive environment for bias correction. These measures can help intelligence agencies reduce the impact of cognitive biases, improve the accuracy of intelligence judgment, and reduce the risk of intelligence failures. 4.3 Limitations and Future Research Directions 4.3.1 Limitations of This Study First, the case selection in this study is limited to 10 typical intelligence failure cases. Although these cases cover different time periods, regions, and types, there may still be sample bias, which affects the generalizability of the research results. Second, this study adopts a mixed-method research design integrating systematic literature review, case coding analysis, and theoretical deduction, but lacks empirical verification through quantitative research methods such as large-sample surveys and controlled experiments. The effectiveness of the proposed intervention strategies needs to be further verified through empirical research. Third, this study focuses on the impact of cognitive biases on intelligence judgment errors, and does not conduct in-depth research on the interaction between cognitive biases and other factors (such as emotional factors, ethical factors) in the process of intelligence analysis. 4.3.2 Future Research Directions First, expand the sample size and scope of case selection, include more types of intelligence failure cases and high-stakes military decision-making cases, and improve the generalizability of the research results. Second, adopt quantitative research methods such as large-sample surveys and controlled experiments to conduct empirical verification of the "individual-technical-organizational" three-dimensional influencing factor model and comprehensive intervention strategies, and further improve the scientificity and effectiveness of the research results. Third, conduct in-depth research on the interaction between cognitive biases and other factors (such as emotional factors, ethical factors) in the process of intelligence analysis, and enrich the research content and perspective of cognitive biases in intelligence analysis. 5. Conclusions This study systematically explores the performance forms, amplification mechanisms, influencing factors, and intervention strategies of cognitive biases in intelligence analysis in the digital-intelligent era through a mixed-method research design integrating systematic literature review, case coding analysis, and theoretical deduction. The main conclusions are as follows: Cognitive biases run through the whole cognitive process of intelligence analysis, and show different typical manifestations in each stage of perception, attention, memory, thinking, and decision-making, including mirror imaging, selective attention bias, anchoring effect, confirmation bias, groupthink, etc. These biases interact with each other to form a chain reaction, which ultimately leads to intelligence judgment errors. Technical factors in the digital-intelligent era (data overload, algorithm dependence, rapid information dissemination, technical reconnaissance limitations, etc.) amplify cognitive biases through specific mechanisms such as cognitive resource scarcity, cognitive anchor solidification, time pressure increase, and signal-noise confusion, making the impact of biases more significant. The formation and amplification of cognitive biases in intelligence analysis are the result of the interaction of individual, technical, and organizational factors. Individual factors are the internal basis, technical factors are the amplification channels, and organizational factors are the external environment. The three factors interact with each other to jointly affect the occurrence and development of cognitive biases. The comprehensive intervention strategy combining "cognitive training - technical optimization - organizational adjustment" can effectively reduce the negative impact of cognitive biases. Cognitive training improves individuals' bias correction ability; technical optimization reduces the amplification effect of technical factors; organizational adjustment builds a supportive bias correction environment. This study enriches the theoretical system of cognitive psychology in the digital-intelligent era, promotes the cross-integration of multiple disciplines, and provides practical references for improving the accuracy of intelligence judgment. Future research can further expand the research sample, adopt quantitative research methods for empirical verification, and conduct in-depth research on the interaction between cognitive biases and other factors, so as to further improve the research results and promote the application and popularization of the results in the military field. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The authors confirm that the data supporting the findings of this study are available within the article. Competing interests All authors declare no known competing financial interests or personal relationships that could have appeared to influence the work reported in this manuscript. Funding No funding was received for this research. Authors’ Contributions Yupeng Huo was responsible for the overall conceptualization and framework design of the study. They led the systematic literature review and drafted the initial version of the manuscript, focusing on the introduction, discussion, and conclusion sections to clarify the research context, theoretical implications, and practical value. LiTongXing Yu undertook the full process of case coding analysis: they participated in the selection of military intelligence failure cases, refined and validated the coding framework, organized the coding data, conducted reliability tests, and analyzed and visualized the results. They also contributed to the writing of the methods and results sections to ensure the rigor and clarity of the research process. Yushan Ji focused on theoretical deduction and strategy development: based on cognitive psychology theories , they constructed the "technical environment - cognitive process - bias amplification" analytical framework and the "individual-technical-organizational" three-dimensional model, and designed the targeted intervention strategies. Acknowledgements Not applicable. References Ahronheim. (2021). Israel’s Operation Against Hamas. The Jerusalem Post . https://www.jpost.com/arab-israeli-conflict/gaza-news/guardian-of-the-walls-the-first-ai-war-669371 Ard, M. J. (2025). Examining the January 6 Capitol attack ‘intelligence failure’: the challenge of domestic security and the role of HUMINT. Intelligence and National Security , 40(1), 114–128. https://doi.org/10.1080/02684527.2024.2422134 Bar Joseph, U. (2005). The watchman fell asleep: The surprise of Yom Kippur and its sources . SUNY Press. Casad, B. J. (2025). Confirmation bias. Britannica . https://www.britannica.com/science/confirmation-bias Corrado, J. (2023). Rethinking Intelligence Failure: China’s Intervention in the Korean War. International Journal of Intelligence and CounterIntelligence , 36(1), 199–219. https://doi.org/10.1080/08850607.2021.1938905 Ding, J. H., Zhang, Q., Guo, C. Y., & Wei, P. (2020). Ren zhi xin li xue [Cognitive psychology] (2nd ed.). Beijing Normal University Press. Dylan, H., & Grossfeld, E. (2025). Unveiling Russian intelligence failures in the Ukraine conflict: a strategic culture perspective. Intelligence and National Security , 40(5), 926–950. https://doi.org/10.1080/02684527.2025.2544460 Dylan, H., & Stivang, N. (2025). Emerging technologies and national security intelligence. Intelligence and National Security , 40(6), 988–1009. https://doi.org/10.1080/02684527.2025.2565948 Flynn, M. 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CQ Press. Johnston, R. (2005). Analytic culture in the United States intelligence community: An ethnographic study (No. 14). Central Intelligence Agency. Kahneman, D. (2011). Thinking, fast and slow . Farrar, Straus and Giroux. Nikolopoulou, K. (2025). What Is Anchoring Bias? | Definition & Examples. https://www.scribbr.com/research-bias/anchoring-bias/ Lillbacka, R. (2022). Schelling Traps as Drivers of Intelligence Failure. International Journal of Intelligence and CounterIntelligence , 35(1), 101–130. https://doi.org/10.1080/08850607.2020.1870032 Liu, J. (2025). When Usefulness Fuels Fear: The Paradox of Generative AI Dependence and the Mitigating Role of AI Literacy. International Journal of Human–Computer Interaction , 1–22. https://doi.org/10.1080/10447318.2025.2544006 Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science , 381(6654), 187–192. https://doi.org/10.1126/science.adh2586 Østbø, J. (2024). The Russian hybrid intelligence state: reconceptualizing the politicization of intelligence and the ‘intelligencization’ of politics. Intelligence and National Security, 39(6), 963–985. https://doi.org/10.1080/02684527.2024.2370134 Robinson-Riegler, B., & Robinson-Riegler, G. L. (2017). Cognitive psychology: Applying the science of the mind (4th ed.). Pearson. McLeod, S., PhD. (2025). Stanley Milgram Shock Experiment. Simply Psychology . https://www.simplypsychology.org/milgram.html Schmidt, A. (2025). Groupthink. Britannica . https://www.britannica.com/science/groupthink Seloom, M. (2025). Veiled intentions: Hamas’s strategic deception and intelligence success on 7 October 2023. Intelligence and National Security , 1–28. https://doi.org/10.1080/02684527.2025.2576903 Tucker. (2020). Spies Like AI. Defense One . https://www.defenseone.com/technology/2020/01/spies-ai-future-artificial-intelligence-us-intelligence-community/162673/ Wohlstetter, R. (1962). Pearl Harbor: Warning and decision . Stanford University Press. Wu, C. M., Schulz, E., Pleskac, T. J., et al. (2022). Time pressure changes how people explore and respond to uncertainty. Scientific Reports , 12(4122). https://doi.org/10.1038/s41598-022-07901-1 Ze’ira, E. (2004). Myth Versus Reality: Lessons from the Yom Kippur War . Tel Aviv. Additional Declarations No competing interests reported. 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Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Research Background\u003c/h2\u003e \u003cp\u003eEmerging digital technologies are reshaping the operational models of organizations, bringing both unprecedented opportunities and disruptive challenges (Haugom et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This wave of digital transformation is driven by two core trends: on one hand, the explosive growth of digital information from sources such as social media, Internet of Things (IoT) devices, online transactions, and public registers; on the other hand, the rapid advancement of computer capacity and performance, enabling the efficient collection and utilization of multi-source heterogeneous data (Haugom et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). As a core link in the intelligence workflow, intelligence analysis involves processing complex information and making accurate judgments, and it is now confronting profound changes in the digital-intelligent era. On the positive side, technologies such as big data analytics and AI-driven reconnaissance have significantly enhanced the efficiency of information collection, processing, and analysis in intelligence work; on the negative side, technical characteristics including data overload, algorithm bias, and technological dependence have introduced new cognitive risks, amplifying inherent human cognitive biases and thereby increasing the probability of intelligence judgment errors.\u003c/p\u003e \u003cp\u003eCognitive bias refers to systematic deviations in human information processing and decision-making arising from inherent limitations of cognitive capacity, psychological tendencies, and environmental influences (Ding et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In intelligence analysis, even minor cognitive biases can lead to catastrophic consequences. For instance, during the Yom Kippur War, Israel's intelligence community (IDI) was trapped in a rigid analytical paradigm known as \"the conception,\" which was formed through years of practice and had been mistakenly validated on multiple occasions (Ze\u0026rsquo;ira, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2004\u003c/span\u003e)( Bar Joseph, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Michael J. Ard evaluates the key findings of January 6 as an alleged intelligence failure event, how HUMINT has been collected in the U.S. on extremist groups, and the feasibility of using such collection for preventive strategies (Ard, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These cases demonstrate that cognitive biases are critical factors contributing to intelligence failures, and the digital-intelligent technical environment further complicates the interaction between cognitive biases and intelligence errors.\u003c/p\u003e \u003cp\u003eExisting studies have made certain progress in exploring the relationship between cognitive biases and intelligence failures. Early research represented by Heuer (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) systematically analyzed the impact of cognitive biases such as mirror imaging, confirmation bias, and groupthink on intelligence analysis from a cognitive psychology perspective and proposed structured analytical methods to mitigate such biases. However, obvious research gaps remain: first, most studies focus on the analysis phase of intelligence work while neglecting cognitive biases in the collection, utilization, and evaluation phases, lacking a systematic analysis of the full-chain mechanism of \"technical environment - cognitive process - bias amplification - intelligence failure\"; second, research on intervention strategies is mostly confined to theoretical discussions, lacking targeted design that integrates the technical characteristics of the digital-intelligent era with cognitive psychology mechanisms, as well as verification through practical cases; third, the research perspective is often limited to specific scenarios, failing to fully explore the universal laws of cognitive biases under the interaction of technology and cognition in the digital-intelligent era, which hinders the promotion of research results to diverse intelligence analysis scenarios.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Literature Review\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e1.2.1 Research on Cognitive Biases in Intelligence Analysis\u003c/h2\u003e \u003cp\u003eResearch on cognitive biases in intelligence analysis originated from reflections on major intelligence failures. After the Pearl Harbor Incident, Wohlstetter (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1962\u003c/span\u003e) proposed the \"signal-noise theory\" in his book *Pearl Harbor: Warning and Decision*, arguing that the inundation of valuable \"signals\" by irrelevant \"noise\" was a crucial cause of intelligence failures, laying the foundation for subsequent research on cognitive factors in intelligence work. Building on this theory, Handel (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1989\u003c/span\u003e) further categorized the sources of \"noise\" into three dimensions: one's own side, the enemy, and the international environment, enriching the theoretical connotation of the signal-noise framework.\u003c/p\u003e \u003cp\u003eIn the 1990s, with the integration of cognitive psychology into intelligence studies, scholars began in-depth explorations of the role of cognitive biases. Heuer (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) systematically examined cognitive biases such as mirror imaging, anchoring effect, confirmation bias, and groupthink in his landmark work *Psychology of Intelligence Analysis*, arguing that these biases stem from inherent limitations in human information processing (e.g., limited attention capacity, memory anchoring) and exert a profound impact on the entire intelligence chain from collection to decision-making. He emphasized that cognitive biases cannot be completely eliminated but can be alleviated through scientific methods such as structured analytical techniques (Heuer \u0026amp; Pherson, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSubsequent studies have further expanded this field. Lemons \u0026amp; Beitler (2025) supplemented a new perspective by pointing out that sexism at both the organizational and individual levels can exacerbate cognitive biases, thereby increasing the risk of intelligence failures. Lillbacka (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) proposed the concept of \"pleasant attractor,\" arguing that when an organization maintains satisfactory outcomes through fixed strategies and avoids alternative strategies due to uncertainty or high costs, it creates an environment conducive to cognitive biases leading to intelligence failures.\u003c/p\u003e \u003cp\u003eIn terms of research methods, case analysis remains the primary approach. Scholars such as Shapira (2023) and Corrado(2023) have identified the role of cognitive biases by analyzing typical cases such as the Yom Kippur War and Korean War. However, existing studies lack quantitative analysis and empirical verification, and most focus on single biases or individual stages, resulting in insufficient systematicity and generalizability of research results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e1.2.2 Impact of Digital-Intelligent Technologies on Cognitive Activities in Intelligence\u003c/h2\u003e \u003cp\u003eWith the popularization of digital-intelligent technologies, scholars have increasingly focused on the interaction between technical environments and cognitive activities in intelligence analysis. From the perspective of information processing, Galotti (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) defined \"selective attention\" as the tendency of individuals to focus on specific tasks while filtering out competing information, noting that in the digital-intelligent era, the explosion of multi-source heterogeneous data (text, images, audio-visual content) has significantly amplified this cognitive limitation.\u003c/p\u003e \u003cp\u003eIn terms of algorithm dependence and technical reliance, Noy \u0026amp; Zhang (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) found through experimental research that generative AI can significantly improve the efficiency of intelligence processing(Noy\u0026amp; Zhang, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Liu (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) supplemented this by noting that this level of usefulness has led people to increasingly depend on generative AI technologies, seamlessly integrating them into their work. Dylan \u0026amp; Stivang (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) emphasized that the activity and processes of intelligence organisations will be dramatically disrupted by emerging technologies(Dylan \u0026amp; Stivang, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e1.2.3 Research on Intervention Strategies for Cognitive Biases in Intelligence Analysis\u003c/h2\u003e \u003cp\u003eCurrent intervention strategies for cognitive biases in intelligence analysis mainly cover three dimensions: individual cognitive training, technical tool optimization, and organizational system improvement. In terms of individual training, Heuer \u0026amp; Pherson (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) proposed structured analytical techniques such as \"alternative analysis\" and \"devil's advocacy\" in \u003cem\u003eStructured Analytic Techniques for Intelligence Analysis\u003c/em\u003e, aiming to help analysts break free from fixed thinking patterns and mitigate biases such as confirmation bias and mirror imaging. In terms of technical optimization, scholars have noted that digital-intelligent technologies can assist in bias correction. For example, Ahronheim (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) introduced the application of Israel's military AI system in real-time satellite image analysis, which helps detect rocket and missile launches, reducing the impact of human cognitive limitations through technical efficiency. Tucker (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) noted that U.S. open-source enterprises use AI to scan and translate foreign-language newspapers, improving the efficiency of information collection while reducing subjective biases in manual processing. In terms of organizational improvement, Johnston (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) emphasized that effective intelligence work requires cohesion, open communication, and shared mental models among team members, which are crucial for breaking information silos.\u003c/p\u003e \u003cp\u003eHowever, existing intervention strategies still have limitations: first, the integration of multiple strategies is insufficient, with most focusing on a single dimension while ignoring the interactive effects between individual cognitive abilities, technical characteristics, and organizational culture; second, the targeting is weak\u0026mdash;strategies are not designed for the specific amplification mechanisms of cognitive biases in the digital-intelligent era; third, the effectiveness of strategies lacks sufficient empirical verification, mostly remaining in the theoretical discussion stage, and failing to form a comprehensive protection system that adapts to the digital-intelligent era. For example, existing technical optimization strategies often overemphasize data processing speed while neglecting the need to avoid \"data supremacy\" and algorithmic bias, leading to new cognitive risks.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e1.3 Research Significance\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e1.3.1 Theoretical Significance\u003c/h2\u003e \u003cp\u003eFirst, this study systematically explores the formation and amplification mechanisms of cognitive biases in intelligence analysis under the digital-intelligent background, constructing an \"individual-technical-organizational\" three-dimensional influencing factor model, which enriches the theoretical system of cognitive psychology in complex military information environments. Second, by revealing the interaction between technical factors and cognitive processes in intelligence analysis, this study expands the research boundary of cognitive bias theory, provides a new analytical framework for the study of cognitive biases in the digital age military field, and promotes the cross-integration of cognitive psychology, intelligence studies, and human-computer interaction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e1.3.2 Practical Significance\u003c/h2\u003e \u003cp\u003eFirst, this study identifies the typical manifestations and amplification paths of cognitive biases in the entire process of intelligence analysis, helping intelligence analysts better recognize and prevent cognitive biases, thereby improving the accuracy of intelligence judgment. Meanwhile, the research results of this study have universal reference value for other high-stakes military decision-making scenarios facing similar complex information environments, and can help relevant practitioners in these fields reduce the impact of cognitive biases and improve decision-making quality.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e1.4 Research Questions and Framework\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e1.4.1 Research Questions\u003c/h2\u003e \u003cp\u003eBased on the above research background and literature review, this study focuses on the following core research questions:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWhat are the typical manifestations of cognitive biases in each link of the cognitive process (perception, attention, memory, thinking, decision-making) of intelligence analysis in the digital-intelligent era?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWhat are the amplification mechanisms of technical factors (data overload, algorithm dependence, rapid information dissemination, etc.) on various cognitive biases in intelligence analysis?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWhat is the interaction mechanism between individual, technical, and organizational factors in the formation and amplification of cognitive biases in intelligence analysis?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eBased on cognitive psychology theories, how to design targeted intervention strategies to reduce the negative impact of cognitive biases in intelligence analysis?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e1.4.2 Research Framework\u003c/h2\u003e \u003cp\u003eThis study adopts a \"problem analysis - mechanism exploration - strategy construction\" research framework. First, through systematic literature review and case coding analysis, the typical manifestations of cognitive biases in intelligence analysis are sorted out; second, based on cognitive psychology theories, the amplification mechanisms of technical factors on cognitive biases are explored, and a three-dimensional influencing factor model is constructed; finally, targeted intervention strategies are proposed from the three levels of individual, technology, and organization.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Research Design\u003c/h2\u003e \u003cp\u003eA mixed-method research design integrating systematic literature review, case coding analysis, and theoretical deduction was adopted. The combination of multiple research methods can make up for the limitations of a single method, ensure the comprehensiveness and depth of the research, and improve the reliability and validity of the research results. Specifically, systematic literature review is used to sort out the research status and theoretical basis; case coding analysis is used to extract the typical manifestations of cognitive biases and the action mechanism of technical factors in intelligence scenarios; theoretical deduction is used to construct the analytical framework and intervention strategies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Systematic Literature Review\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Literature Search Strategy\u003c/h2\u003e \u003cp\u003eLiterature searches were conducted in databases such as Web of Science, Scopus, CNKI, and Wanfang Data, with a search time range from 1990 to 2025. English search keywords included \"cognitive bias,\" \"intelligence analysis,\" \"digital-intelligent era,\" \"information processing,\" \"intervention strategy\"; Chinese search keywords included \"cognitive bias,\" \"intelligence analysis,\" \"digital-intelligent era,\" \"information processing,\" \"intervention strategy.\" A combination of subject terms and free words was used, and search results were expanded through snowballing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Literature Inclusion and Exclusion Criteria\u003c/h2\u003e \u003cp\u003eInclusion criteria: 1. The research content is related to cognitive biases, intelligence analysis, and digital-intelligent technologies; 2. The research type is theoretical research, empirical research, or case study; 3. The literature is published in formal academic journals or monographs; 4. English literature is written in English, and Chinese literature is written in Chinese. Exclusion criteria: 1. Literature with irrelevant research content; 2. Conference abstracts, dissertations, and other non-formal publications; 3. Literature with low quality (such as lack of clear research questions and methods); 4. Duplicate literature.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Literature Sorting and Analysis\u003c/h2\u003e \u003cp\u003eAfter literature search and screening, a total of 236 relevant literatures were finally included, including 187 English literatures and 49 Chinese literatures. The included literatures were sorted out and analyzed from the aspects of research theme, research method, core findings, and research gaps, so as to lay the theoretical foundation for subsequent research.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Case Coding Analysis\u003c/h2\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Case Selection\u003c/h2\u003e \u003cp\u003eTo ensure the representativeness and typicality of the cases, the following selection criteria were formulated: 1. The case is a major intelligence failure event that has attracted wide attention in academic circles and has sufficient research materials; 2. The case involves cognitive biases and technical factors, which is conducive to analyzing the interaction between the two; 3. The cases cover different time periods, regions, and types (inter-state conflicts, counter-terrorism operations, geopolitical confrontations, etc.), so as to avoid sample bias. Based on the above criteria, 10 typical cases were finally selected (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of Selected Cases\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCase Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTime\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRegion/Country\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCase Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCore Cognitive Biases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKey Technical Factors\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePearl Harbor Incident\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnited States/Japan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInter-state Military Conflict\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMirror Imaging, Signal-Noise Confusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTechnical Reconnaissance Limitations\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYom Kippur War\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIsrael/Egypt/Syria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInter-state Military Conflict\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eConfirmation Bias, Groupthink\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInformation Sharing Barriers\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9/11 Attacks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnited States/Al-Qaeda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCounter-Terrorism Operation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAttention Deviation, Organizational Barriers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eData Overload, Information Fragmentation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndian Nuclear Crisis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnited States/India\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGeopolitical Confrontation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eResource Allocation Bias, Anchoring Effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInsufficient Human Intelligence Collection\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRussia-Ukraine Conflict (Early Stage)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRussia/Ukraine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInter-state Military Conflict\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMirror Imaging, Overconfidence Bias\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTechnical Dependence, Algorithm Bias\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHamas's Strategic Deception (2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIsrael/Hamas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAsymmetric Military Conflict\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eConfirmation Bias, Complacency Bias\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInformation Cocoon, Technical Reconnaissance Deception\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJanuary 6 Capitol Attack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDomestic Security Operation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAttention Deviation, Groupthink\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eData Collection Deficiencies, Information Dissemination Chaos\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAfghan War (U.S. Intelligence Failure)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2001\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnited States/Afghanistan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCounter-Terrorism \u0026amp; Military Occupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eResource Allocation Bias, Confirmation Bias\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInformation Collection Focus Deviation, Data Overload\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBarbarossa Operation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSoviet Union/Germany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInter-state Military Conflict\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAuthority Obedience, Confirmation Bias\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePolitical Intervention, Information Distortion\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCuban Missile Crisis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnited States/Soviet Union\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGeopolitical Confrontation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMirror imaging, Signal-Noise Confusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTechnical Reconnaissance Advantages, Information Verification Pressure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoston Marathon Bombing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCounter-Terrorism Operation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAttention Deviation, Information Sharing Barriers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFragmented Information, Data Processing Delay\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Coding Framework Construction\u003c/h2\u003e \u003cp\u003eBased on cognitive psychology theories and the research questions of this study, a multi-level coding framework was constructed. The primary coding dimensions include cognitive bias types, technical amplification factors, organizational context factors, and error consequences; each primary dimension is divided into secondary dimensions and coding items. The coding framework was revised and improved through expert consultation (inviting 3 scholars in the fields of cognitive psychology and intelligence studies to put forward revision suggestions) to ensure the scientificity and operability of the coding framework.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCoding Framework for Case Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary Dimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary Dimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoding Items\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive Bias Types\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerception Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMirror Imaging, Perceptual Filtering Bias\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAttention Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSelective Attention Bias, Attention Fragmentation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMemory Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnchoring Effect, Availability Heuristic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThinking Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConfirmation Bias, Overconfidence Bias, Wishful Thinking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDecision-Making Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroupthink, Authority Obedience, Complacency Bias\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechnical Amplification Factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData-Related\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData Overload, Data Heterogeneity, Data Falseness\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlgorithm-Related\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlgorithm Bias, Information Cocoon, Algorithm Dependence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTechnical Tool-Related\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTechnical Reconnaissance Limitations, Technical Dependence, Information Processing Delay\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInformation Dissemination-Related\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRapid Information Dissemination, Information Fragmentation, Information Distortion\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrganizational Context Factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommunication Culture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClosed Communication, Open Communication, Information Sharing Barriers\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDecision-Making Culture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCentralized Decision-Making, Decentralized Decision-Making, Political Intervention\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTeam Climate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConformity Tendency, Critical Atmosphere, Responsibility Avoidance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Coding Process and Quality Control\u003c/h2\u003e \u003cp\u003eThe coding process was completed by two researchers independently, and the specific steps are as follows: first, the two researchers read the case materials in detail and conducted preliminary coding according to the coding framework; second, the coding results of the two researchers were compared and checked, and the inconsistent coding items were discussed and negotiated to reach a consensus; finally, the third researcher was invited to conduct a review of the coding results to ensure the accuracy and reliability of the coding. In order to test the reliability of the coding, the Kappa coefficient was used for evaluation. The results showed that the Kappa coefficient of the two researchers' coding results was 0.82, which was higher than the acceptable threshold of 0.7, indicating that the coding results had good reliability.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Theoretical Deduction\u003c/h2\u003e \u003cp\u003eBased on the results of systematic literature review and case coding analysis, combined with cognitive psychology theories such as cognitive resource theory, anchoring effect theory, and groupthink theory, as well as the technical characteristics of the digital-intelligent era, theoretical deduction was carried out. First, the formation mechanism of cognitive biases in each link of the cognitive process of intelligence analysis was analyzed, and the internal logical relationship between different cognitive biases was sorted out; second, the amplification mechanism of technical factors on cognitive biases was explored, and the interaction path between technical factors and cognitive processes was clarified; finally, the \"individual-technical-organizational\" three-dimensional influencing factor model of cognitive biases in intelligence analysis was constructed, and targeted intervention strategies were proposed based on the model.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Typical Manifestations of Cognitive Biases in Intelligence Analysis (Based on Cognitive Process)\u003c/h2\u003e \u003cp\u003eThrough case coding analysis and theoretical deduction, this study found that cognitive biases run through the entire cognitive process of intelligence analysis, and show different typical manifestations in each stage of perception, attention, memory, thinking, and decision-making. These biases are not isolated but interact with each other to form a chain reaction, which ultimately leads to intelligence judgment errors.\u003c/p\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Attention Stage: Selective Attention Bias and Attention Fragmentation\u003c/h2\u003e \u003cp\u003eAttention should be paid to the concentration and focus of psychological activities, which is a selective, transferable, and decomposable concentration(Ding, 2020). Attention is the beginning of cognition and runs through the entire process of cognition. The limited capacity of attention determines that individuals can only focus on a part of information in the face of massive battlefield information (Galotti, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In the attention stage of intelligence analysis, the typical cognitive biases are selective attention bias and attention fragmentation.\u003c/p\u003e \u003cp\u003eSelective attention bias refers to the tendency of individuals to focus on information that is consistent with their own strategic interests, combat expectations, and cognitive frameworks, while ignoring information that is inconsistent with them. For example, during the Afghanistan War, due to the overemphasis on collecting intelligence on militant organizations, U.S. intelligence agencies neglected the understanding of basic information such as Afghanistan\u0026rsquo;s local economic conditions, land ownership status, and influential figures. This resulted in \"the U.S. government being simply unable to make decisions on the measures to adopt in Afghanistan based on fully mastered information(Flynn, 2010).\"\u003c/p\u003e \u003cp\u003eAttention fragmentation refers to the phenomenon that individuals' attention is continuously interrupted by external interference factors in the complex battlefield environment, resulting in the inability to focus on deep processing of key intelligence information. In this situation, people usually repeat past choices (Wu et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In the digital-intelligent era, the popularization of instant messaging tools, real-time data update alerts, and multi-service collaborative platforms has made attention fragmentation more prominent in intelligence analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Memory Stage: Anchoring Effect and Availability Heuristic\u003c/h2\u003e \u003cp\u003eMemory refers to the process of individuals storing, maintaining, and extracting information (Robinson-Riegler, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). According to the duration of information storage, memory can be divided into sensory memory, short-term memory, and long-term memory(Goldstein, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In the memory stage of intelligence analysis, the typical cognitive biases are anchoring effect and availability heuristic.\u003c/p\u003e \u003cp\u003eAnchoring effect refers to the tendency of individuals to over-rely on the first received key information (anchor point) when making intelligence judgments, and adjust their subsequent judgments based on this anchor point, resulting in judgment deviations (Nikolopoulou, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In the Indian Nuclear Crisis, the U.S. intelligence department's initial judgment on India's nuclear policy was based on India's previous statement of \"peaceful use of nuclear energy,\" and this initial judgment became an anchor point. Even when there were a large number of signs of India's nuclear test preparations (such as the abnormal activities of nuclear facilities and the import of related materials), the U.S. intelligence department still did not make a timely adjustment to the initial judgment, leading to the failure to predict India's nuclear test.\u003c/p\u003e \u003cp\u003eAvailability heuristic refers to the tendency of individuals to judge the probability of a military event based on the ease of recalling similar events, that is, events that are easier to recall are considered to have a higher probability of occurrence. In the counter-terrorism intelligence work after the 9/11 Attacks, the U.S. intelligence department was deeply impressed by the terrorist attack method of using civil aircraft as weapons, and this method was easier to recall. Therefore, it focused on preventing similar attack methods, while ignoring other new terrorist attack methods such as using drones to carry explosives and launching cyber attacks, leading to the failure to effectively respond to emerging terrorist threats.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3 Thinking Stage: Confirmation Bias, Overconfidence Bias, and Wishful Thinking\u003c/h2\u003e \u003cp\u003eThinking is the process of individuals indirectly and generally reflecting the essence and internal connections of objective things(Gao, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In the thinking stage of intelligence analysis, the typical cognitive biases are confirmation bias, overconfidence bias, and wishful thinking.\u003c/p\u003e \u003cp\u003eConfirmation bias refers to the tendency of individuals to actively search for, interpret, and accept information that is consistent with their own existing strategic beliefs and intelligence judgments, while ignoring or denying information that is inconsistent with their own beliefs (Casad, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In the Russian-Ukraine Conflict, Russia's intelligence department believed that Ukraine's military strength could not resist Russia's military offensive, and only accepted information that was consistent with this judgment, while ignoring the information about Ukraine's military assistance from Western countries and the improvement of combat effectiveness through military training, leading to misjudgment of the war process (Dylan \u0026amp; Grossfeld, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOverconfidence bias refers to the tendency of individuals to overestimate their own intelligence collection and analysis abilities and the accuracy of their judgments (Kahneman, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In the 9/11 Attacks, the U.S. intelligence department overestimated the effectiveness of its own intelligence collection and sharing system, believing that it could detect and prevent terrorist attacks in time, but in fact, due to the lack of effective information integration and analysis, the terrorist attack was not prevented.\u003c/p\u003e \u003cp\u003eWishful thinking refers to the tendency of individuals to judge military events according to their own wishes and desires, rather than objective facts (Heuer, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). In the Barbarossa Operation, Stalin hoped that the Soviet Union could maintain peaceful relations with Germany to gain more time for military preparation, so he ignored a large number of intelligence about Germany's preparations for attacking the Soviet Union, believing that Germany would not violate the \"Soviet-German Non-Aggression Pact,\" leading to the Soviet Union's lack of sufficient preparation for Germany's sudden attack and heavy military losses (Gao, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section3\"\u003e \u003ch2\u003e3.1.4 Decision-Making Stage: Groupthink, Authority Obedience, and Complacency Bias\u003c/h2\u003e \u003cp\u003eIn the decision-making stage of intelligence analysis, the typical cognitive biases are groupthink, authority obedience, and complacency bias. groupthink, mode of thinking in which individual members of small cohesive groups tend to accept a viewpoint or conclusion that represents a perceived group consensus, whether or not the group members believe it to be valid, correct, or optimal. Groupthink reduces the efficiency of collective problem solving within such groups (Schmidt, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAuthority obedience refers to the tendency of individuals to over-rely on or obey the opinions of superiors or military experts, and ignore their own judgments and objective intelligence facts (McLeod, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In the digital-intelligent era, \"data authority\" and \"technical authority\" have also become important factors leading to authority obedience in intelligence analysis. In the 2023 Hamas strategic deception case, Israel's intelligence department over-relied on the analysis results of AI systems and technical reconnaissance data, and ignored the information from human intelligence and on-site reconnaissance, because these technical results were considered to have \"objective authority\" (Seloom, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eComplacency bias refers to the tendency of individuals or groups to relax their vigilance and reduce their investment in intelligence collection and analysis due to past military successes or current military advantages, resulting in failure to detect potential military risks in time (Heuer, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Amplification Mechanism of Technical Factors on Cognitive Biases in the Digital-Intelligent Era\u003c/h2\u003e \u003cp\u003eThrough case coding analysis and theoretical deduction, this study found that technical factors in the digital-intelligent era do not directly cause cognitive biases in intelligence analysis, but amplify the inherent cognitive biases of human beings through specific mechanisms, making the impact of biases more significant and the probability of intelligence judgment errors higher.\u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Data Overload: Exacerbating Cognitive Resource Scarcity and Attention Deviation\u003c/h2\u003e \u003cp\u003eData overload refers to the phenomenon that the amount of battlefield information received by intelligence agencies exceeds their information processing capacity. In the digital-intelligent era, the popularization of big data technology and the networking of military equipment have made the amount of information collected by intelligence departments explode, and the problem of data overload has become increasingly prominent. Data overload amplifies cognitive biases in intelligence analysis mainly through the following mechanisms:\u003c/p\u003e \u003cp\u003eFirst, data overload leads to the scarcity of cognitive resources, making intelligence analysts unable to conduct in-depth processing of information. According to cognitive resource theory, human cognitive resources are limited. When facing a large amount of battlefield information, analysts have to allocate limited cognitive resources to a large number of information processing tasks, resulting in each task being unable to obtain sufficient cognitive resources, and thus unable to conduct in-depth analysis and verification of key intelligence information. In the 9/11 Attacks, the U.S. intelligence department received a large number of fragmented information about terrorist activities before the attack, but due to data overload, analysts were unable to integrate and analyze these information in depth, leading to the failure to detect the terrorist attack plan (Gao, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSecond, data overload exacerbates selective attention bias in intelligence analysis. In the face of a large amount of battlefield information, analysts tend to choose information that is easy to process, consistent with their own cognitive frameworks, and related to their own strategic interests for processing, while ignoring information that is difficult to process, inconsistent with their own cognitive frameworks, and unrelated to their own strategic interests. In the digital-intelligent era, the diversity and complexity of military data make this selection tendency more obvious.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Algorithm Dependence: Strengthening Cognitive Anchor Solidification and Confirmation Bias\u003c/h2\u003e \u003cp\u003eAlgorithm dependence refers to the tendency of intelligence agencies and analysts to over-rely on algorithm results in the process of information processing and decision-making, and reduce their own critical thinking and independent judgment (Liu, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In the digital-intelligent era, algorithms have become an important tool for intelligence departments to process massive battlefield data, and algorithm dependence has become a common phenomenon. Algorithm dependence amplifies cognitive biases in intelligence analysis mainly through the following mechanisms:\u003c/p\u003e \u003cp\u003eFirst, algorithm dependence strengthens the solidification of cognitive anchors. Algorithms usually generate initial intelligence analysis results based on existing military data and models. These initial results will become cognitive anchors for analysts, and analysts will adjust their subsequent judgments based on these anchors. Due to the trust in algorithms, analysts are often unwilling to question and revise the initial results of algorithms, leading to the solidification of cognitive anchors (Nikolopoulou, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSecond, algorithm dependence exacerbates confirmation bias in intelligence analysis. Most of the algorithms used by intelligence departments are based on machine learning technology. These algorithms will continuously optimize themselves according to the user's preferences and feedback, and push information that is consistent with the user's cognitive framework to the user, forming an \"information cocoon\" (Liu, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In the process of using algorithms, analysts will continue to receive information that is consistent with their own existing beliefs, while rarely receiving contradictory information, which will further consolidate their existing beliefs and exacerbate confirmation bias.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Rapid Information Dissemination: Increasing Decision-Making Time Pressure and Reducing Cognitive Processing Depth\u003c/h2\u003e \u003cp\u003eIn the digital-intelligent era, the life cycle of intelligence is significantly shortened. Rapid information dissemination amplifies cognitive biases in intelligence analysis mainly through the following mechanisms:\u003c/p\u003e \u003cp\u003eFirst, rapid information dissemination increases decision-making time pressure. In the digital-intelligent era, the speed of changes in the international security situation and the enemy's military actions is significantly accelerated, and the life cycle of intelligence is significantly shortened. Intelligence departments must make decisions in a short time, otherwise the intelligence will lose its value. The increase in time pressure makes analysts unable to conduct in-depth analysis and verification of intelligence information, and can only make decisions based on intuitive judgment and past combat experience, thus increasing the impact of cognitive biases such as availability heuristic and anchoring effect.\u003c/p\u003e \u003cp\u003eSecond, rapid information dissemination reduces the depth of cognitive processing. In the face of a large amount of rapidly changing battlefield information, analysts have to continuously switch between different information, and cannot conduct in-depth processing of a certain piece of information. This kind of superficial information processing makes analysts more likely to be affected by the surface characteristics of information, and ignores the deep-seated connections and essential characteristics of information, thus increasing the impact of cognitive biases such as perceptual filtering bias and selective attention bias.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e \u003ch2\u003e3.2.4 Technical Reconnaissance Limitations: Aggravating Signal-Noise Confusion and Perceptual Filtering Bias\u003c/h2\u003e \u003cp\u003eTechnical reconnaissance limitations refer to the phenomenon that military technical reconnaissance tools and methods have inherent defects and are easily affected by external factors such as battlefield environment and enemy counter-reconnaissance measures, leading to insufficient or inaccurate information collection. In the digital-intelligent era, although military technical reconnaissance technology has made great progress, it still has certain limitations. Technical reconnaissance limitations amplify cognitive biases in intelligence analysis mainly through the following mechanisms:\u003c/p\u003e \u003cp\u003eFirst, technical reconnaissance limitations lead to insufficient information collection, exacerbating signal-noise confusion. Military technical reconnaissance tools such as satellites, drones, and radar are easily affected by environmental factors such as weather, terrain, and electromagnetic interference, leading to insufficient or inaccurate information collection. At the same time, the enemy's counter-reconnaissance measures (such as camouflage, deception, and jamming) will also interfere with technical reconnaissance, making it difficult to distinguish effective intelligence signals from irrelevant noise.\u003c/p\u003e \u003cp\u003eSecond, technical reconnaissance limitations exacerbate perceptual filtering bias. Military technical reconnaissance tools usually collect information in a specific form (such as images, signals, electronic parameters, etc.), and analysts are accustomed to processing information in this form. When the enemy adopts new combat methods or deception measures that are not easy to be detected by technical reconnaissance tools, analysts are often unable to perceive and process this information due to perceptual filtering bias.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec35\" class=\"Section2\"\u003e \u003ch2\u003e3.3 \"Individual-Technical-Organizational\" Three-Dimensional Influencing Factor Model of Cognitive Biases\u003c/h2\u003e \u003cp\u003eBased on the above analysis, this study constructs an \"individual-technical-organizational\" three-dimensional influencing factor model of cognitive biases in intelligence analysis. This model holds that the formation and amplification of cognitive biases in intelligence analysis are the result of the interaction of individual, technical, and organizational factors. Among them, individual factors are the internal basis, technical factors are the amplification channels, and organizational factors are the external environment. The three factors interact with each other to jointly affect the occurrence and development of cognitive biases.\u003c/p\u003e \u003cdiv id=\"Sec36\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Individual Factors: Internal Basis of Cognitive Biases\u003c/h2\u003e \u003cp\u003eIndividual factors refer to the inherent characteristics of intelligence analysts, including cognitive ability, military experience background, personality traits, and motivational needs. These factors determine the initial sensitivity of analysts to cognitive biases and are the internal basis for the formation of cognitive biases.\u003c/p\u003e \u003cp\u003eCognitive ability refers to the ability of individuals to process intelligence information, including attention, memory, thinking, and reasoning abilities. Analysts with strong cognitive ability are more likely to conduct in-depth processing of battlefield information, identify potential cognitive biases, and make correct judgments; on the contrary, analysts with weak cognitive ability are more likely to be affected by cognitive biases.\u003c/p\u003e \u003cp\u003eMilitary experience background refers to the work experience, educational background, and military cultural background of analysts. Analysts with rich intelligence work experience can better grasp the rules of intelligence analysis and reduce the impact of cognitive biases, but they are also more likely to be trapped in past combat experience and form fixed thinking patterns. Analysts with different educational backgrounds and military cultural backgrounds have different cognitive frameworks and thinking modes, and their sensitivity to different cognitive biases is also different.\u003c/p\u003e \u003cp\u003ePersonality traits refer to the stable psychological characteristics of individuals, such as self-confidence, openness, and critical thinking. Analysts with strong critical thinking are more likely to question their own judgments and the opinions of others, and reduce the impact of cognitive biases such as confirmation bias and authority obedience; on the contrary, analysts with weak critical thinking are more likely to be affected by these biases.\u003c/p\u003e \u003cp\u003eMotivational needs refer to the internal driving forces that promote analysts to conduct intelligence analysis, such as job performance, military career development, and sense of national responsibility. Analysts with a strong sense of national responsibility are more likely to conduct in-depth analysis of battlefield information and reduce the impact of cognitive biases; on the contrary, analysts with strong self-interest motivation may deliberately ignore information that is inconsistent with their own interests, leading to the aggravation of cognitive biases such as confirmation bias.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec37\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 Technical Factors: Amplification Channels of Cognitive Biases\u003c/h2\u003e \u003cp\u003eAs analyzed in Section \u003cspan refid=\"Sec30\" class=\"InternalRef\"\u003e3.2\u003c/span\u003e, technical factors in the digital-intelligent era (data overload, algorithm dependence, rapid information dissemination, technical reconnaissance limitations, etc.) do not directly cause cognitive biases in intelligence analysis, but amplify the inherent cognitive biases of human beings through specific mechanisms. These technical factors are the amplification channels of cognitive biases, which make the impact of biases more significant.\u003c/p\u003e \u003cp\u003eIt should be noted that technical factors are not only the amplification channels of cognitive biases, but also can be used to reduce cognitive biases in intelligence analysis. For example, the application of artificial intelligence technology can help analysts process massive battlefield data, reduce the impact of data overload on cognitive biases; the design of anti-information cocoon algorithms can help analysts break through the limitations of their own cognitive frameworks and reduce the impact of confirmation bias. Therefore, the key to technical factors lies in how to use them reasonably to reduce the amplification effect of cognitive biases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec38\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3 Organizational Factors: External Environment of Cognitive Biases\u003c/h2\u003e \u003cp\u003eOrganizational factors refer to the characteristics of intelligence agencies, including communication culture, decision-making culture, team climate, and incentive mechanisms. These factors form the external environment for analysts to conduct intelligence analysis, and have an important impact on the formation and amplification of cognitive biases.\u003c/p\u003e \u003cp\u003eIntelligence work is a team activity. An effective team requires cohesion, formal and informal communication, collaboration, a shared mental model, and similar knowledge structures (Johnston, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). An open communication culture is conducive to the sharing of battlefield information and the expression of different views, which can reduce the impact of cognitive biases such as groupthink and information sharing barriers; on the contrary, a closed communication culture will limit the sharing of information and the expression of different views, leading to the aggravation of these biases.\u003c/p\u003e \u003cp\u003eDecision-making culture refers to the norms and patterns of decision-making within the intelligence organization. A decentralized decision-making culture is conducive to giving full play to the initiative and creativity of analysts, reducing the impact of cognitive biases such as authority obedience and political intervention; on the contrary, a centralized decision-making culture will strengthen the authority of superiors and increase the impact of these biases.\u003c/p\u003e \u003cp\u003eTeam climate refers to the psychological atmosphere within the intelligence team, including cohesion, mutual trust, and critical atmosphere. A team with a good critical atmosphere is conducive to the expression of different views and the questioning of existing judgments, which can reduce the impact of cognitive biases such as groupthink and complacency bias; on the contrary, a team with a strong conformity tendency will suppress the expression of different views, leading to the aggravation of these biases.\u003c/p\u003e \u003cp\u003eIncentive mechanisms refer to the systems and methods used by intelligence organizations to motivate analysts, including performance evaluation, reward and punishment systems (Heuer, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). A scientific incentive mechanism is conducive to encouraging analysts to conduct in-depth analysis of battlefield information and put forward objective and accurate judgments, reducing the impact of cognitive biases such as confirmation bias and self-interest motivation; on the contrary, an unreasonable incentive mechanism will encourage analysts to cater to the superiors' intentions or pursue short-term interests, leading to the aggravation of these biases.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec39\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Effectiveness of Comprehensive Intervention Strategies\u003c/h2\u003e \u003cp\u003eBased on the \"individual-technical-organizational\" three-dimensional influencing factor model, this study proposes a comprehensive intervention strategy combining \"cognitive training - technical optimization - organizational adjustment\". Through case analysis and theoretical deduction, this study verifies the effectiveness of the strategy in reducing the negative impact of cognitive biases in intelligence analysis.\u003c/p\u003e \u003cdiv id=\"Sec40\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1 Cognitive Training: Improving Individuals' Bias Correction Ability\u003c/h2\u003e \u003cp\u003eCognitive training refers to the training of intelligence analysts' cognitive abilities and thinking modes to improve their ability to identify and correct cognitive biases. The main content of cognitive training includes critical thinking training, structured analysis method training, and perspective-taking training.\u003c/p\u003e \u003cp\u003eCritical thinking training aims to improve analysts' ability to question and verify intelligence information, and reduce the impact of cognitive biases such as confirmation bias and authority obedience (Heuer \u0026amp; Pherson, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For example, through case analysis and group discussions of typical intelligence failures, analysts are guided to question their own judgments and the opinions of others, and learn to find and verify the evidence supporting and opposing a certain judgment.\u003c/p\u003e \u003cp\u003eStructured analysis method training aims to help analysts establish a scientific intelligence information processing framework, reduce the impact of cognitive biases such as anchoring effect and availability heuristic (Heuer \u0026amp; Pherson, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For example, training analysts to use methods such as \"alternative analysis\" and \"devil's advocacy\" to comprehensively consider various possible enemy intentions and combat scenarios, and avoid being limited to a single way of thinking.\u003c/p\u003e \u003cp\u003ePerspective-taking training aims to help analysts understand the thinking mode and behavioral logic of the enemy, and reduce the impact of cognitive biases such as mirror imaging (Heuer, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). For example, through role-playing and cross-cultural military exchange training, analysts are guided to stand in the perspective of the enemy to think about military strategies and operational intentions, and avoid inferring the enemy's intentions based on their own military thinking.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec41\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2 Technical Optimization: Reducing the Amplification Effect of Technical Factors\u003c/h2\u003e \u003cp\u003eTechnical optimization refers to the improvement and optimization of technical tools and methods used in intelligence work to reduce the amplification effect of technical factors on cognitive biases. The main content of technical optimization includes anti-data overload technology development, anti-information cocoon algorithm design, and technical reconnaissance system improvement.\u003c/p\u003e \u003cp\u003eAnti-data overload technology development aims to help analysts process massive battlefield data efficiently and reduce the impact of data overload on cognitive biases. For example, developing intelligent information filtering and sorting technology to automatically filter out irrelevant information and sort out key intelligence information, reducing the burden of information processing for analysts.\u003c/p\u003e \u003cp\u003eAnti-information cocoon algorithm design aims to help analysts break through the limitations of their own cognitive frameworks and reduce the impact of confirmation bias. For example, designing algorithms that can push heterogeneous military information to analysts, ensuring that analysts can receive not only information consistent with their own cognitive frameworks but also contradictory information, and promoting analysts to conduct comprehensive thinking.\u003c/p\u003e \u003cp\u003eTechnical reconnaissance system improvement aims to improve the accuracy and comprehensiveness of military technical reconnaissance and reduce the impact of technical reconnaissance limitations on cognitive biases For example, developing multi-sensor fusion technology to integrate information collected by different technical reconnaissance tools (such as satellites, drones, and radar), improving the ability to distinguish effective signals from noise; strengthening the research and development of anti-counter-reconnaissance technology to cope with the enemy's camouflage and deception measures.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec43\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Theoretical Implications\u003c/h2\u003e \u003cp\u003eThis study systematically explores the formation and amplification mechanisms of cognitive biases in intelligence analysis under the digital-intelligent background, and constructs an \"individual-technical-organizational\" three-dimensional influencing factor model. This model breaks through the limitations of existing studies that focus on single factors, and reveals the interaction mechanism between individual, technical, and organizational factors in the formation and amplification of cognitive biases. At the same time, this study finds that technical factors in the digital-intelligent era amplify cognitive biases through specific mechanisms such as cognitive resource scarcity, cognitive anchor solidification, time pressure increase, and signal-noise confusion, which enriches the understanding of the impact of technical environments on cognitive biases in intelligence analysis and expands the research boundary of cognitive bias theory in complex military information environments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec44\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Practical Implications\u003c/h2\u003e \u003cp\u003eThe comprehensive intervention strategy combining \"cognitive training - technical optimization - organizational adjustment\" proposed in this study provides a practical operation scheme for intelligence agencies to reduce cognitive biases. Intelligence agencies can carry out targeted cognitive training for analysts to improve their ability to identify and correct cognitive biases; optimize technical tools and methods to reduce the amplification effect of technical factors on cognitive biases; adjust the organizational structure, culture, and system to build a supportive environment for bias correction. These measures can help intelligence agencies reduce the impact of cognitive biases, improve the accuracy of intelligence judgment, and reduce the risk of intelligence failures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec45\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Limitations and Future Research Directions\u003c/h2\u003e \u003cdiv id=\"Sec46\" class=\"Section3\"\u003e \u003ch2\u003e4.3.1 Limitations of This Study\u003c/h2\u003e \u003cp\u003eFirst, the case selection in this study is limited to 10 typical intelligence failure cases. Although these cases cover different time periods, regions, and types, there may still be sample bias, which affects the generalizability of the research results. Second, this study adopts a mixed-method research design integrating systematic literature review, case coding analysis, and theoretical deduction, but lacks empirical verification through quantitative research methods such as large-sample surveys and controlled experiments. The effectiveness of the proposed intervention strategies needs to be further verified through empirical research. Third, this study focuses on the impact of cognitive biases on intelligence judgment errors, and does not conduct in-depth research on the interaction between cognitive biases and other factors (such as emotional factors, ethical factors) in the process of intelligence analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec47\" class=\"Section3\"\u003e \u003ch2\u003e4.3.2 Future Research Directions\u003c/h2\u003e \u003cp\u003eFirst, expand the sample size and scope of case selection, include more types of intelligence failure cases and high-stakes military decision-making cases, and improve the generalizability of the research results. Second, adopt quantitative research methods such as large-sample surveys and controlled experiments to conduct empirical verification of the \"individual-technical-organizational\" three-dimensional influencing factor model and comprehensive intervention strategies, and further improve the scientificity and effectiveness of the research results. Third, conduct in-depth research on the interaction between cognitive biases and other factors (such as emotional factors, ethical factors) in the process of intelligence analysis, and enrich the research content and perspective of cognitive biases in intelligence analysis.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study systematically explores the performance forms, amplification mechanisms, influencing factors, and intervention strategies of cognitive biases in intelligence analysis in the digital-intelligent era through a mixed-method research design integrating systematic literature review, case coding analysis, and theoretical deduction. The main conclusions are as follows:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCognitive biases run through the whole cognitive process of intelligence analysis, and show different typical manifestations in each stage of perception, attention, memory, thinking, and decision-making, including mirror imaging, selective attention bias, anchoring effect, confirmation bias, groupthink, etc. These biases interact with each other to form a chain reaction, which ultimately leads to intelligence judgment errors.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTechnical factors in the digital-intelligent era (data overload, algorithm dependence, rapid information dissemination, technical reconnaissance limitations, etc.) amplify cognitive biases through specific mechanisms such as cognitive resource scarcity, cognitive anchor solidification, time pressure increase, and signal-noise confusion, making the impact of biases more significant.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe formation and amplification of cognitive biases in intelligence analysis are the result of the interaction of individual, technical, and organizational factors. Individual factors are the internal basis, technical factors are the amplification channels, and organizational factors are the external environment. The three factors interact with each other to jointly affect the occurrence and development of cognitive biases.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe comprehensive intervention strategy combining \"cognitive training - technical optimization - organizational adjustment\" can effectively reduce the negative impact of cognitive biases. Cognitive training improves individuals' bias correction ability; technical optimization reduces the amplification effect of technical factors; organizational adjustment builds a supportive bias correction environment.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThis study enriches the theoretical system of cognitive psychology in the digital-intelligent era, promotes the cross-integration of multiple disciplines, and provides practical references for improving the accuracy of intelligence judgment. Future research can further expand the research sample, adopt quantitative research methods for empirical verification, and conduct in-depth research on the interaction between cognitive biases and other factors, so as to further improve the research results and promote the application and popularization of the results in the military field.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm that the data supporting the findings of this study are available within the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no known competing financial interests or personal relationships that could have appeared to influence the work reported in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYupeng Huo was responsible for the overall conceptualization and framework design of the study. They led the systematic literature review and drafted the initial version of the manuscript, focusing on the introduction, discussion, and conclusion sections to clarify the research context, theoretical implications, and practical value. LiTongXing Yu undertook the full process of case coding analysis: they participated in the selection of military intelligence failure cases, refined and validated the coding framework, organized the coding data, conducted reliability tests, and analyzed and visualized the results. They also contributed to the writing of the methods and results sections to ensure the rigor and clarity of the research process. Yushan Ji focused on theoretical deduction and strategy development: based on cognitive psychology theories , they constructed the \"technical environment - cognitive process - bias amplification\" analytical framework and the \"individual-technical-organizational\" three-dimensional model, and designed the targeted intervention strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003cbr\u003e\u003c/strong\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhronheim. (2021). Israel\u0026rsquo;s Operation Against Hamas. \u003cem\u003eThe Jerusalem Post\u003c/em\u003e. https://www.jpost.com/arab-israeli-conflict/gaza-news/guardian-of-the-walls-the-first-ai-war-669371 \u003c/li\u003e\n\u003cli\u003eArd, M. J. (2025). 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(2004). \u003cem\u003eMyth Versus Reality: Lessons from the Yom Kippur War\u003c/em\u003e. Tel Aviv.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cognitive Bias, Digital-Intelligent Environment, Intelligence Analysis, High-Stakes Decision-Making, Intervention Strategy, Cognitive Psychology","lastPublishedDoi":"10.21203/rs.3.rs-8698493/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8698493/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and Aim\u003c/h2\u003e \u003cp\u003eThe digital-intelligent era, characterized by information explosion, algorithmic penetration, and multi-tasking pressure, has reshaped the information processing environment for intelligence analysis. In high-stakes military scenarios, interactions between cognitive biases and technical factors drive intelligence judgment errors, with profound impacts on battlefield outcomes and national security. Existing studies lack systematic analysis of how digital-intelligent technical characteristics amplify these biases and in-depth discussion of cognitive psychology-based targeted intervention strategies. This study aims to explore typical cognitive bias manifestations across the cognitive process of intelligence analysis, reveal technical amplification mechanisms, construct an \u0026ldquo;individual-technical-organizational\u0026rdquo; three-dimensional model, and propose targeted intervention strategies to enrich relevant research and improve intelligence judgment accuracy.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA mixed-method research design was adopted, including systematic literature review, case coding analysis, and theoretical deduction. A systematic review of cognitive psychology, intelligence studies, and human-computer interaction literature was conducted. Ten typical intelligence failure cases were selected for qualitative coding analysis. Cognitive psychology theories guided the construction of the \u0026ldquo;technical environment - cognitive process - bias amplification - judgment error\u0026rdquo; framework, verified via case cross-validation. Coding reliability was evaluated using the Kappa coefficient.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCognitive biases permeate the entire cognitive process from perception to decision-making, forming chain reactions. Digital-intelligent factors amplify biases through mechanisms such as cognitive resource scarcity, cognitive anchor solidification, and reduced cognitive processing depth. Bias formation and amplification result from the interaction of individual, technical, and organizational factors. The \u0026ldquo;cognitive training - technical optimization - organizational adjustment\u0026rdquo; comprehensive strategy effectively mitigates bias impacts.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study enriches the digital-intelligent era cognitive psychology theoretical system by revealing the interaction mechanism between technical environments and cognitive biases. The three-dimensional model and intervention strategies provide a new perspective for military cognitive bias research and universal reference for high-stakes military decision-making. Future research will verify strategy effectiveness through longitudinal tracking and controlled experiments.\u003c/p\u003e","manuscriptTitle":"Cognitive Biases in Intelligence Analysis: Amplification Mechanisms and Intervention Strategies in the Digital-Intelligent Era","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-17 10:48:05","doi":"10.21203/rs.3.rs-8698493/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-19T09:19:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-15T16:51:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"233029187806964597529091223089332620268","date":"2026-02-15T16:47:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"234163866656501904019128925918110090201","date":"2026-02-15T06:47:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"235898750502093478843390068358352005117","date":"2026-02-12T09:32:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-12T06:24:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-12T05:05:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"67144229808334899392499109702226541065","date":"2026-02-12T02:58:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"34413920763855251719708427864054148671","date":"2026-02-12T02:29:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-12T02:24:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-12T02:10:24+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-03T12:03:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-02T15:40:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2026-02-02T14:59:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f074e982-54dc-4da6-99b4-77978492c54e","owner":[],"postedDate":"February 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T16:03:42+00:00","versionOfRecord":{"articleIdentity":"rs-8698493","link":"https://doi.org/10.1186/s40359-026-04443-3","journal":{"identity":"bmc-psychology","isVorOnly":false,"title":"BMC Psychology"},"publishedOn":"2026-04-02 15:58:58","publishedOnDateReadable":"April 2nd, 2026"},"versionCreatedAt":"2026-02-17 10:48:05","video":"","vorDoi":"10.1186/s40359-026-04443-3","vorDoiUrl":"https://doi.org/10.1186/s40359-026-04443-3","workflowStages":[]},"version":"v1","identity":"rs-8698493","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8698493","identity":"rs-8698493","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}