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A neural network model of personality is presented. The model has two goal systems: an approach system (BAS) and an avoidance system (BIS), as well as a system that governs the level of disinhibition/constraint (IS) in the two goal systems and the behavior system. Furthermore, within both goal systems, agentic and communal goals are specified. By tweaking the parameters of this system (e.g., chronic activation of goals, sensitivity of systems), and randomly or systematically varying situational arrays, distinct patterns of “behavior” by Virtual Personalities (VPs) across “situations” emerge that fit with classic distinctions (e.g., Big 5, temperaments). Various simulations demonstrate that VPs provide an exciting vehicle for integrating disparate approaches to personality to better understand the dynamics, situational responsiveness, and consistency of persons in situations.
Read et al. (Fri,) studied this question.