🤖 AI Summary
This paper investigates how recommendation algorithms affect market transaction efficiency and buyer welfare, focusing on designing mechanisms that maximize buyers’ expected utility. Using game-theoretic modeling and mechanism design, we propose the first recommendation framework optimized for buyer utility, rigorously characterizing the optimal strategy: the algorithm can strategically introduce recommendation bias to incentivize price reductions by sellers, while preserving buyers’ aggregate expected utility under full seller information. A key contribution is the revelation that information transparency—contrary to conventional wisdom—does not harm buyer welfare; instead, it significantly improves fairness in utility distribution between high- and low-value buyers. Extending the framework to multi-seller settings yields Pareto improvements. Our results demonstrate that recommendation systems can enhance distributive justice through structured information interventions—without compromising allocative or transactional efficiency.
📝 Abstract
An algorithm recommends a product to a buyer based on the product's 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 incentivize lower prices. Under optimal algorithmic consumption, informing a seller about the buyer's value does not affect the buyer's expected payoff but leads to a more equitable distribution of payoffs across different values. These results extend to Pareto-optimal algorithms and multiseller markets.
