Decision Making in Star Networks with Incorrect Beliefs

TL;DR

This paper investigates Bayesian decision-making in star networks with agents holding incorrect beliefs, revealing that such beliefs can sometimes reduce the fusion agent's risk and that optimal beliefs resemble human probability reweighting.

Contribution

It demonstrates that incorrect prior beliefs can outperform true priors in minimizing risk and characterizes the asymptotic behavior of optimal beliefs and risks in large networks.

Findings

Incorrect beliefs can lead to lower Bayes risk than true priors.

Optimal beliefs resemble models from cumulative prospect theory.

Risk converges exponentially fast to zero as the number of agents increases.

Abstract

Consider a Bayesian binary decision-making problem in star networks, where local agents make selfish decisions independently, and a fusion agent makes a final decision based on aggregated decisions and its own private signal. In particular, we assume all agents have private beliefs for the true prior probability, based on which they perform Bayesian decision making. We focus on the Bayes risk of the fusion agent and counterintuitively find that incorrect beliefs could achieve a smaller risk than that when agents know the true prior. It is of independent interest for sociotechnical system design that the optimal beliefs of local agents resemble human probability reweighting models from cumulative prospect theory. We also consider asymptotic characterization of the optimal beliefs and fusion agent's risk in the number of local agents. We find that the optimal risk of the fusion agent converges to zero exponentially fast as the number of local agents grows. Furthermore, having an identical constant belief is asymptotically optimal in the sense of the risk exponent. For additive Gaussian noise, the optimal belief turns out to be a simple function of only error costs and the risk exponent can be explicitly characterized.