TY - GEN
T1 - Universally Robust Information Aggregation for Binary Decisions
AU - Arieli, Itai
AU - Babichenko, Yakov
AU - Talgam-Cohen, Inbal
AU - Zabarnyi, Konstantin
N1 - Publisher Copyright: © 2023 Owner/Author(s).
PY - 2023/7/9
Y1 - 2023/7/9
N2 - We study a setting with a decision maker making a binary decision by aggregating information from symmetric agents. Each agent provides the decision maker a recommendation depending on her private signal about the hidden state. We assume that agents are truthful - an agent recommends guessing the more likely state based on her information. This assumption is natural if the agents are unaware of how the decision-maker will aggregate their recommendations. While the decision maker has a prior distribution over the hidden state and knows the marginal distribution of each agent's private signal, the correlation between these signals is chosen adversarially. The decision maker's goal is choosing an information aggregation rule that is robustly optimal.
AB - We study a setting with a decision maker making a binary decision by aggregating information from symmetric agents. Each agent provides the decision maker a recommendation depending on her private signal about the hidden state. We assume that agents are truthful - an agent recommends guessing the more likely state based on her information. This assumption is natural if the agents are unaware of how the decision-maker will aggregate their recommendations. While the decision maker has a prior distribution over the hidden state and knows the marginal distribution of each agent's private signal, the correlation between these signals is chosen adversarially. The decision maker's goal is choosing an information aggregation rule that is robustly optimal.
UR - http://www.scopus.com/inward/record.url?scp=85168096461&partnerID=8YFLogxK
U2 - https://doi.org/10.1145/3580507.3597710
DO - https://doi.org/10.1145/3580507.3597710
M3 - منشور من مؤتمر
T3 - EC 2023 - Proceedings of the 24th ACM Conference on Economics and Computation
SP - 118
BT - EC 2023 - Proceedings of the 24th ACM Conference on Economics and Computation
T2 - 24th ACM Conference on Economics and Computation, EC 2023
Y2 - 9 July 2023 through 12 July 2023
ER -