Model-Based Wisdom of the Crowd for Sequential Decision-Making Tasks

In this project, we (myself and Holly Westfall, Jeff Coon, and Michael Lee, all from the Lee Lab) demonstrate a unique application of building cognitive models (Bayesian or otherwise). We show how classic Wisdom of the Crowd is difficult to apply in sequential decision making tasks, and demonstrate how cognitive models of decison making behavior can be used to compensate for these difficulties by allowing you to aggregate hypothetical behavior predicted from the cognitive models, rather than requiring individuals to actual demonstrate their behavior in all possible situations. This approach could be applied to other types of decision making tasks as well and we are super interested in hearing about interesting applications of this approach. Paper, & all data and code are available openly.

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Code & Data