A self-contained karma economy for the dynamic allocation of common resources

TL;DR

This paper introduces karma mechanisms for the dynamic, repeated allocation of scarce resources among competing agents, enabling efficient and fair outcomes without monetary pricing by modeling the system as a population game with equilibrium analysis.

Contribution

It proposes a novel karma-based allocation mechanism modeled as a population game, demonstrating equilibrium existence and analyzing its efficiency and fairness properties.

Findings

High efficiency achieved at Nash equilibrium

Fair allocation possible with future-aware agents

Robustness tested against agent heterogeneity

Abstract

This paper presents karma mechanisms, a novel approach to the repeated allocation of a scarce resource among competing agents over an infinite time. Examples include deciding which ride hailing trip requests to serve during peak demand, granting the right of way in intersections or lane mergers, or admitting internet content to a regulated fast channel. We study a simplified yet insightful formulation of these problems where at every instant two agents from a large population get randomly matched to compete over the resource. The intuitive interpretation of a karma mechanism is "If I give in now, I will be rewarded in the future." Agents compete in an auction-like setting where they bid units of karma, which circulates directly among them and is self-contained in the system. We demonstrate that this allows a society of self-interested agents to achieve high levels of efficiency without resorting to a (possibly problematic) monetary pricing of the resource. We model karma mechanisms as dynamic population games and guarantee the existence of a stationary Nash equilibrium. We then analyze the performance at the stationary Nash equilibrium numerically. For the case of homogeneous agents, we compare different mechanism design choices, showing that it is possible to achieve an efficient and ex-post fair allocation when the agents are future aware. Finally, we test the robustness against agent heterogeneity and propose remedies to some of the observed phenomena via karma redistribution.