On Information Design with Spillovers
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Yifan Guo
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Abstract
An information designer assigns experiments to agents on a directed network. The information transmission has spillover, encoded by the network: an agent observes the experiments assigned to him, and also other agents’ experiments that might spill over to him. We study how optimal information design is affected by the mode of spillovers (local or global) and lossy information transmission. Under global spillover, its complexity is governed by the network structure: the optimal information structure is tractable when each agent follows at most one other agent but immediately becomes NP-hard otherwise. Under local spillover, the complexity is a subtle interplay of the network and communication technology: optimal information design is tractable so long as each agent does not follow many agents and the communication technology is simple, but becomes intractable with any deviation from these two factors. Finally, we showcase an application of our framework to information design in supply chains.
Major revision at ACM Transactions on Economics and Computation.
Authors
Ph.D. Student in Operations, Information, and Technology
I am a Ph.D. student in Operations, Information, and Technology at the Stanford Graduate School of Business and a Data Science Scholar at Stanford Human-Centered AI.
I study the intersection of economics, operations, and AI. My research uses game theory, mechanism design, and information design to improve human–AI collaboration and decision-making. I received my B.S. in Mathematics with highest honor from the School of the Gifted Young at the University of Science and Technology of China.