Buyer-Optimal Algorithmic Recommendations

TL;DR

This paper characterizes buyer-optimal recommendation algorithms that strategically bias suggestions to lower prices, increasing buyer welfare and affecting market price dispersion and surplus distribution.

Contribution

It introduces a characterization of algorithms that maximize buyer payoff and demonstrates their strategic biasing effects in various market settings.

Findings

Buyer-optimal algorithms induce lower prices

Revealing buyer values does not change total payoffs

Algorithms lead to more dispersed prices and equitable surplus distribution

Abstract

In markets where algorithmic data processing is increasingly prevalent, recommendation algorithms can substantially affect trade and welfare. We consider a setting in which an algorithm recommends a product based on its value to the buyer and its price. We characterize an algorithm that maximizes the buyer's expected payoff and show that it strategically biases recommendations to induce lower prices. Revealing the buyer's value to the seller leaves overall payoffs unchanged while leading to more dispersed prices and a more equitable distribution of surplus across buyer types. These results extend to all Pareto-optimal algorithms and to multiseller markets, with implications for AI assistants and e-commerce ranking systems.