IPDToolkit: An R package for simulation and Bayesian analysis of iterated prisoner’s dilemma game-play under third-party arbitration

preprint OA: closed CC-BY-4.0

Abstract

Recently, researchers have begun studying the role that third-party arbitration may play in the evolution of cooperation. Using the iterated prisoner’s dilemma (IPD), they show that arbitration can mitigate the negative effects of perception errors on the stability of cooperative strategies. Open questions, both theoretical and empirical, however, remain. To promote research on the role of third-party arbitration, we introduce an R package, IPDToolkit, which facilitates both simulation of synthetic data and Bayesian analysis of empirical data. To address theoretical questions, IPDToolkit provides a Monte Carlo simulation engine that can be used to generate play between arbitrary strategies in the IPD with arbitration and assess expected pay-offs. To address empirical questions, IPDToolkit provides customizable, Bayesian finite-mixture models that can be used to identify the strategies responsible for generating empirical gameplay data. We present a complete workflow using IPDToolkit to teach end-users its functionality.
Full text 1,387 characters · extracted from oa-doi-fallback · 2 sections · click to expand

Abstract

Recently, researchers have begun studying the role that third-party arbitration may play in the evolution of cooperation. Using the iterated prisoner’s dilemma (IPD), they show that arbitration can mitigate the negative effects of perception errors on the stability of cooperative strategies. Open questions, both theoretical and empirical, however, remain. To promote research on the role of third-party arbitration, we introduce an R package, IPDToolkit, which facilitates both simulation of synthetic data and Bayesian analysis of empirical data. To address theoretical questions, IPDToolkit provides a Monte Carlo simulation engine that can be used to generate play between arbitrary strategies in the IPD with arbitration and assess expected pay-offs. To address empirical questions, IPDToolkit provides customizable, Bayesian finite-mixture models that can be used to identify the strategies responsible for generating empirical gameplay data. We present a complete workflow using IPDToolkit to teach end-users its functionality. DOI https://doi.org/10.32942/X2M91P Subjects Anthropology, Political Science, Psychology, Social and Behavioral Sciences

Keywords

Dates Published: 2024-06-03 15:49 License CC BY Attribution 4.0 International Additional Metadata Language: English

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

Ask this paper AI returns verbatim quotes from the full text · source: oa-doi-fallback

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. This is a recent paper (2024) — citers typically take a year or two to land, and the OpenAlex reference graph may still be filling in.

Source provenance

europepmc
last seen: 2026-05-20T01:45:00.602351+00:00
unpaywall
last seen: 2026-06-02T02:00:03.124865+00:00
License: CC-BY-4.0