Computational modeling of person-situation transactions: How accumulation of situational experiences can shape the distributions of trait scores

The chapter argues that individuals are dynamic systems that continually transact with their situational experiences. Computational modeling may provide a useful set of tools for generating and refining hypotheses regarding general principles of such person-situation transactions. Building computational models encourages conceptual rigor and allows proof-of-principle testing of hypotheses, even though the models do not provide empirical evidence. It has been previously hypothesized that person-environment transactions (systematic situational influences on personality) tend to increase, whereas random influences may decrease the magnitude of individual differences. The chapter introduces a framework (personality space framework; PSF) for building computational models that play through such scenarios. The models suggested that any kind of accumulating situational experiences may tend to make people more alike until variance reaches a plateau, regardless of whether the experiences are random or systematically tied to pre-existing trait levels. The simulations also suggested that person-environment transactions may contribute to the emergence of personality “factor(s)”.