Parameterizing agent-based models using an online game

Abstract

Agent-based models (ABMs) of human systems are often parameterized using real-world data. For some ABMs this is not possible because the reality upon which the models are based does not exist or is not generalizable from one setting to another. In this paper we implement an online decision game to parameterize an agent-based model of pedestrian and cyclist route choice decisions in a neighbourhood. Our conceptual framework is to use an experimental game to log decision-making behaviour, summarize this behaviour into a decision model, and then transfer this model to an ABM. The product of this framework is an ABM with agents informed by human decision making made within the game, rather than the real world. The results of our analysis suggest that the decision model is consistent with some general theory about decision making, but the ABM illustrates some unique and contextually specific patterns of flow. ABMs parameterized with game data may be useful for forecasting the effects of change on urban transportation infrastructure.