Value of History in Social Learning: Applications to Markets for History

TL;DR

This paper explores how the value of historical information in social learning environments can be maximized and how a data seller can optimize information disclosure to influence market dynamics and agent incentives.

Contribution

It characterizes optimal information structures for maximizing the value of history and analyzes the market for history with a focus on seller incentives and information design.

Findings

Maximum value of history occurs under mixed full and no information structures.

Seller's dynamic pricing equals the value of history for agents.

Seller's optimal information disclosure is less than socially optimal.

Abstract

In social learning environments, agents acquire information from both private signals and the observed actions of predecessors, referred to as history. We define the value of history as the gain in expected payoff from accessing both the private signal and history, compared to relying on the signal alone. We first characterize the information structures that maximize this value, showing that it is highest under a mixture of full information and no information. We then apply these insights to a model of markets for history, where a monopolistic data seller collects and sells access to history. In equilibrium, the seller's dynamic pricing becomes the value of history for each agent. This gives the seller incentives to increase the value of history by designing the information structure. The seller optimal information discloses less information than the socially optimal level.