Implementing Optimal Outcomes in Social Computing: A Game-Theoretic Approach

TL;DR

This paper explores how to design social computing mechanisms that incentivize strategic contributors to produce optimal outcomes, using game theory to analyze reward structures and the impact of ranking noise on implementation.

Contribution

It demonstrates that optimal outcomes can be implemented in contest settings with strategic agents, especially when rankings are imperfect, and provides methods for learning optimal rewards.

Findings

Contests can implement optimal outcomes with strategic agents when rewards are properly set.

Imperfect ranking systems can facilitate the achievement of optimal outcomes with endogenous effort.

Mechanism design can adapt to unknown utility parameters through learning methods.

Abstract

In many social computing applications such as online Q&A forums, the best contribution for each task receives some high reward, while all remaining contributions receive an identical, lower reward irrespective of their actual qualities. Suppose a mechanism designer (site owner) wishes to optimize an objective that is some function of the number and qualities of received contributions. When potential contributors are strategic agents, who decide whether to contribute or not to selfishly maximize their own utilities, is such a "best contribution" mechanism, M_B, adequate to implement an outcome that is optimal for the mechanism designer? We first show that in settings where a contribution's value is determined primarily by an agent's expertise, and agents only strategically choose whether to contribute or not, contests can implement optimal outcomes: for any reasonable objective, the rewards for the best and remaining contributions in M_B can always be chosen so that the outcome in the unique symmetric equilibrium of M_B maximizes the mechanism designer's utility. We also show how the mechanism designer can learn these optimal rewards when she does not know the parameters of the agents' utilities, as might be the case in practice. We next consider settings where a contribution's value depends on both the contributor's expertise as well as her effort, and agents endogenously choose how much effort to exert in addition to deciding whether to contribute. Here, we show that optimal outcomes can never be implemented by contests if the system can rank the qualities of contributions perfectly. However, if there is noise in the contributions' rankings, then the mechanism designer can again induce agents to follow strategies that maximize his utility. Thus imperfect rankings can actually help achieve implementability of optimal outcomes when effort is endogenous and influences quality.