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Affect Control Theory (ACT) is a powerful and general sociological model ofhuman affective interaction. ACT provides an empirically derived mathematicalmodel of culturally shared sentiments as heuristic guides for human decisionmaking. BayesACT, a variant on classical ACT, combines affective reasoning withcognitive (denotative or logical) reasoning as is traditionally found in AI.Bayes\-ACT allows for the creation of agents that are both emotionally guidedand goal-directed. In this work, we simulate BayesACT agents in the IteratedNetworked Prisoner's Dilemma (INPD), and we show four out of five knownproperties of human play in INPD are replicated by these socio-affectiveagents. In particular, we show how the observed human behaviours of networkstructure invariance, anti-correlation of cooperation and reward, and playertype stratification are all clearly emergent properties of the networkedBayesACT agents. We further show that decision hyteresis (Moody ConditionalCooperation) is replicated by BayesACT agents in over $2/3$ of the cases wehave considered. In contrast, previously used imitation-based agents are onlyable to replicate one of the five properties. We discuss the implications ofthese findings in the development of human-agent societies.

Socio-Affective Agents as Models of Human Behaviour in the Networked Prisoner's Dilemma