Predictive models of human behavior in strategic settings

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The proposed project will develop general models that accurately predict how real people behave in strategic settings. Such models are important because the design of such settings--e.g., the rules that Google establishes to run its auctions for advertising slots alongside search keywords--depends crucially on models of participant behavior. Specifically, a designer chooses rules that yield the best predicted behavior, e.g., optimizing the designer's revenue or the welfare of all participants. Currently, participants are usually modeled as behaving "economically rationally". This assumption is arguably reasonable when all participants are large corporations. However, a wealth of recent experimental evidence shows that it very often fails to hold for individual human beings. Thus, designs said to be optimal are likely to perform suboptimally in practice. If people simply behaved nondeterministically, this might be the end of the story. However, people are not just irrational--they are predictably irrational. Over the last decade or so, behavioral game theorists have catalogued a wide range of systematic ways in which people deviate from rationality. However, this literature doesn't quite tell us how to model how real people behave in arbitrary strategic settings. My long-term research goal is to produce full predictive models of human behavior, and to validate these models using state-of-the-art methods from machine learning. The work described in this proposal will pursue four interrelated threads of work: (1) modeling the effect of salience in human strategic play; (2) leveraging the recent idea of deep learning to automate feature discovery; (3) developing models that predict a person's depth of strategic reasoning; and (4) investigating ways that Google's ad auction design could be optimized for better performance under the sort of behavior predicted by our models. Overall, more accurate predictive models of bidder behavior have immediate implications for improving the operation of Google's keyword auctions--a key revenue driver for small businesses across Canada--and indeed for the optimization of markets across a wide variety of industries.