Friend-Based Ranking

TL;DR

This paper investigates how social network structures influence the ability to create incentive-compatible and efficient rankings of individuals based on local information, identifying specific network conditions that enable complete rankings.

Contribution

It characterizes the social network conditions, especially the windmill network, under which a planner can always produce a complete, incentive-compatible ranking.

Findings

A planner can construct an ex post incentive compatible and efficient ranking if and only if every pair of friends has a mutual friend.

The windmill network is identified as the sparsest network allowing a complete ranking.

The study links social network topology to the feasibility of ranking mechanisms.

Abstract

We analyze the design of a mechanism to extract a ranking of individuals according to a unidimensional characteristic, such as ability or need. Individuals, connected on a social network, only have local information about the ranking. We show that a planner can construct an ex post incentive compatible and efficient mechanism if and only if every pair of friends has a friend in common. We characterize the windmill network as the sparsest social network for which the planner can always construct a complete ranking.