Incentives and Efficiency in Uncertain Collaborative Environments

TL;DR

This paper analyzes collaborative systems with reward sharing, demonstrating near-optimal value approximation under local sharing rules and revealing how effort costs impact overall efficiency.

Contribution

It introduces a model for collaborative environments with local reward sharing and proves near-optimality of simple rules even with incomplete information.

Findings

Local sharing rules approximate maximum value well.

Achieves 95% optimality at equilibrium in natural instances.

Efficiency decreases with linear effort costs as the number of players grows.

Abstract

We consider collaborative systems where users make contributions across multiple available projects and are rewarded for their contributions in individual projects according to a local sharing of the value produced. This serves as a model of online social computing systems such as online Q&A forums and of credit sharing in scientific co-authorship settings. We show that the maximum feasible produced value can be well approximated by simple local sharing rules where users are approximately rewarded in proportion to their marginal contributions and that this holds even under incomplete information about the player's abilities and effort constraints. For natural instances we show almost 95% optimality at equilibrium. When players incur a cost for their effort, we identify a threshold phenomenon: the efficiency is a constant fraction of the optimal when the cost is strictly convex and decreases with the number of players if the cost is linear.