Behavior Identification in Two-Stage Games for Incentivizing Citizen Science Exploration

We consider two-stage games in which a leader seeks to direct the activities of independent agents by offering incentives. A good leader’s strategy requires an understanding of the agents’ utilities and the ability to predict agent behavior. Moreover, the optimization of outcomes requires an agent behavior model that can be efficiently incorporated into the leader’s model. Here we address the agent behavior modeling problem and show how it can be used to reduce bias in a challenging citizen science application. Adapting ideas from Discrete Choice Modeling in behavioral economics, we develop a probabilistic behavioral model that takes into account variable patterns of human behavior and suboptimal actions. By modeling deviations from baseline behavior we are able to accurately predict future behavior based on limited, sparse data. We provide a novel scheme to fold the agent model into a bi-level optimization as a single Mixed Integer Program, and scale up our approach by adding redundant constraints, based on novel insights of an easy-hard-easy phase transition phenomenon. We apply our methodology to a game called Avicaching, in collaboration with eBird, a well-established citizen science program that collects bird observations for conservation. Field results show that our behavioral model performs well and that the incentives are remarkably effective at steering citizen scientists’ efforts to reduce bias by exploring under-sampled areas. Moreover, the data collected from Avicaching improves the performance of species distribution models.