# Incentivizing Collaboration in Heterogeneous Teams via Common-Pool Resource Games

arXiv: https://arxiv.org/abs/1908.03938 · HTML: https://mlsanity.com/paper/1908.03938/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2019.

## Built on by (most cited first, 3 of 3)

- [Generalized Mission Planning for Heterogeneous Multi-Robot Teams via LLM-constructed Hierarchical Trees](https://mlsanity.com/paper/2501.16539/) (2025)
- [KGLAMP: Knowledge Graph-guided Language model for Adaptive Multi-robot Planning and Replanning](https://mlsanity.com/paper/2602.04129/) (2026)
- [Scale-Plan: Scalable Language-Enabled Task Planning for Heterogeneous Multi-Robot Teams](https://mlsanity.com/paper/2603.08814/) (2026)

## Abstract (arXiv)

We consider a team of heterogeneous agents that is collectively responsible for servicing, and subsequently reviewing, a stream of homogeneous tasks. Each agent has an associated mean service time and a mean review time for servicing and reviewing the tasks, respectively. Agents receive a reward based on their service and review admission rates. The team objective is to collaboratively maximize the number of "serviced and reviewed" tasks. We formulate a Common-Pool Resource (CPR) game and design utility functions to incentivize collaboration among heterogeneous agents in a decentralized manner. We show the existence of a unique Pure Nash Equilibrium (PNE), and establish convergence of best response dynamics to this unique PNE. Finally, we establish an analytic upper bound on three measures of inefficiency of the PNE, namely the price of anarchy, the ratio of the total review admission rate, and the ratio of latency, along with an empirical study.