Competition, Persuasion, and Search
Teddy Mekonnen, Bobak Pakzad-Hurson

TL;DR
This paper examines how market competition among information brokers influences surplus creation and division in sequential search environments, revealing that high search costs make competition more beneficial for agents but less for total surplus.
Contribution
It extends repeated-games theory to stopping problems and characterizes equilibrium payoffs based on market structure and search costs.
Findings
Low search costs lead to negligible effects of market structure on surplus.
High search costs make competition advantageous for agents but reduce total surplus.
Breaking up monopolist brokers can benefit agents but decrease overall surplus.
Abstract
How does competition in markets for information affect the creation and division of surplus? We study this question in a search environment in which an agent searches sequentially for a high-quality good and learns about the quality of sampled goods by repeatedly purchasing signals from profit-maximizing information brokers. Brokers design and price signals but can commit only to spot contracts. We characterize the equilibrium payoff set as a function of the market structure -- the number of competing brokers. When search costs are low, market structure affects neither surplus generation nor its division. When costs are high, however, competition benefits the agent but reduces total surplus relative to monopoly. The analysis yields a regulatory lesson: breaking up an information monopolist can benefit the searching agent while reducing total surplus. Methodologically, we extend…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsGame Theory and Applications · Experimental Behavioral Economics Studies · Auction Theory and Applications
MethodsSparse Evolutionary Training
