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Congestion games model a wide variety of real-world resource congestionproblems, such as selfish network routing, traffic route guidance in congestedareas, taxi fleet optimization and crowd movement in busy areas. However,existing research in congestion games assumes: (a) deterministic movement ofagents between resources; and (b) perfect rationality (i.e. maximizing theirown expected value) of all agents. Such assumptions are not reasonable indynamic domains where decision support has to be provided to humans. Forinstance, in optimizing the performance of a taxi fleet serving a city,movement of taxis can be involuntary or nondeterministic (decided by thespecific customer who hires the taxi) and more importantly, taxi drivers maynot follow advice provided by the decision support system (due to boundedrationality of humans). To that end, we contribute: (a) a general framework forrepresenting congestion games under uncertainty for populations with assortednotions of rationality. (b) a scalable approach for solving the decisionproblem for perfectly rational agents which are in the mix with boundedlyrational agents; and (c) a detailed evaluation on a synthetic and realworlddata set to illustrate the usefulness of our new approach with respect to keysocial welfare metrics in the context of an assorted human-agent population. Aninteresting result from our experiments on a real-world taxi fleet optimizationproblem is that it is better (in terms of revenue and operational efficiency)for taxi drivers to follow perfectly rational strategies irrespective of thepercentage of drivers not following the advice.

Uncertain Congestion Games with Assorted Human Agent Populations