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NSF
How can a social planner share information with people to encourage behaviors that benefit society as a whole? Moreover, what if human behavior affects the state of the world, changing the information the planner can share? This problem is called “endogenous information design:” strategically providing information about the world in situations when the state of the world depends on behavior. This problem appears in many smart infrastructure systems: for example, a smartphone app that warns drivers about a traffic accident encourages cautious driving, which reduces the risk of follow-on accidents. However, this safer driving also changes the traffic conditions, which affects the app’s future warnings. This circular relationship creates complex challenges for decision-makers in systems like transportation networks, public safety, and smart technologies. This project will develop new theoretical models and mathematical tools for analyzing these types of situations. These findings will have impact across many fields including economics, transportation science, and operations research. Furthermore, the project will include a series of traveling summer schools which will visit universities across the US to educate graduate students on these new techniques. In addition to the summer schools, the project will involve outreach to K-12 students, undergraduate researchers, and members of the public. This project will pose, characterize, and apply a novel family of game-theoretic models called “endogenous Bayesian games.” These games substantially extend classical Bayesian game theory by letting the distribution of states-of-the-world be a function of agent behavior, concisely and cleanly capturing the circular informational dependencies inherent in many smart infrastructure systems. The project will apply a three-pronged approach to (1) develop analytical foundations for this new theory, (2) apply these novel tools to a problem of information design in smart and connected transportation networks, and (3) characterize and optimize the robustness of the policy recommendations generated by these models. This project’s findings will enable information design in important settings for which no tools currently exist, filling an important gap in the scientific literature and providing social planners with new analytical capabilities for conducting information design in these settings. Altogether, project results will strengthen scientific understanding of the process of influencing societal behavior and establish a foundation for future work on social-influence mechanisms. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Up to $500K
2030-06-30
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