Growing artificial societies: social science from the bottom up
Growing artificial societies: social science from the bottom up
Behavioral game theoretic models: a Bayesian framework for parameter analysis
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
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Agent-based models are a popular way to explore the dynamics of human interactions, but rarely are these models based on empirical observations of actual human behavior. Here we exploit data collected in an experimental setting where over 150 human players played in a series of almost a hundred public goods games. First, we fit a series of deterministic models to the data, finding that a reasonably parsimonious model with just three parameters performs extremely well on the standard test of predicting average contributions. This same model, however, performs extremely poorly when predicting the full distribution of contributions, which is strongly bimodal. In response, we introduce and test a corresponding series of stochastic models, thus identifying a model that both predicts average contribution and also the full distribution. Finally, we deploy this model to explore hypotheses about regions of the parameter space outside of what was experimentally accessible. In particular, we investigate (a) whether a previous conclusion that network topology does not impact contribution levels holds for much larger networks than could be studied in a lab; (b) to what extent observed contributions depend on average network degree and variance in the degree distribution, and (c) the dependency of contributions on degree assortativity as well as the correlation between the generosity of players and the degree of the nodes to which they are assigned.