2021/02/04 by Yi-Chun Chen, Chen, Yi-Chun, Manuel Mueller-Frank +3 · 1 citation
Computer Science · Decision Sciences · #Auction Theory and Applications #Computer Science and Game Theory (cs.GT) #Data Stream Mining Techniques #FOS: Computer and information sciences #FOS: Economics and business #Mobile Crowdsensing and Crowdsourcing #Theoretical Economics (econ.TH)
paper · pdf · doi:10.48550/arxiv.2102.02666
openalex publication_date 2021/02/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
We propose a new simple procedure called Population-Mean-Based Aggregation (PMBA) that enables a principal to "aggregate" information about an unknown state of the world from agents without understanding the information structure among them. PMBA only requires agents to communicate their beliefs about the state, and some agents to communicate their expectations of the population average belief. In a large population, for any finite number of possible states, and under weak assumptions on the information structure, allowing individual agents' beliefs to be misspecified, we show that PMBA infers the true state (in probability or almost surely under the stated conditions). We show how PMBA can be reinterpreted as a linear regression procedure, and how it can be used to aggregate information from a finite number of agents, allowing us to reuse existing results on inference in linear models. We conduct a novel experiment to show that the real-world performance of our procedure exceeds that of existing methods.