Information design in one-sided matching problems

For the 'Greater Good': Please Choose A

With Lenka Fiala and Sulagna Dasgupta.

Abstract: How do people trade off individual versus group welfare in the face of uncertainty regarding private benefits of different actions? We propose a partial information revelation (`recommendation’) policy designed to maximize group welfare, and we show its theoretical robustness to well-documented behavioral deviations from the risk neutral, Bayesian, and self-interested benchmark. In a large-scale online experiment with 2600 subjects, we then show that this policy fails to improve upon a full information benchmark even when individual and group objectives are aligned. In this case, information design is theoretically powerful but requires sophisticated inferences from players. Indeed, we show that cognitive limitations reduce the predictive value of the theory. This provides suggestive evidence in favor of simplicity in information design in multi-agent strategic settings.

[in review]