Dynamic Resource Allocation with Karma: An Experimental Study

2024-04-03

AI Summary

This paper addresses the fairness–efficiency trade-off in dynamic resource allocation under stochastic demand and heterogeneous urgency levels. We propose and empirically evaluate a non-transferable “karma” credit mechanism, implemented via a binary karma-bidding protocol that adapts to time-varying urgency in repeated multi-agent competition for scarce resources. Crucially, we conduct the first online experiment with real human participants—without pretraining—to assess the mechanism’s long-term welfare properties. Results demonstrate sustained welfare gains over baseline mechanisms: aggregate utility increases significantly while individual utilities do not decline for the majority of participants; under sporadic high-urgency scenarios, performance approaches Pareto improvement. Our work establishes a scalable, empirically verifiable paradigm for fairness–efficiency co-optimization in dynamic resource allocation systems.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Humans and AI: Learning Human Values and Preferences

Application Category

• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

A system of non-tradable credits that flow between individuals like karma, hence proposed under that name, is a mechanism for repeated resource allocation that comes with attractive efficiency and fairness properties, in theory. In this study, we test karma in an online experiment in which human subjects repeatedly compete for a resource with time-varying and stochastic individual preferences or urgency to acquire the resource. We confirm that karma has significant and sustained welfare benefits even in a population with no prior training. We identify mechanism usage in contexts with sporadic high urgency, more so than with frequent moderate urgency, and implemented as a simple (binary) karma bidding scheme as particularly effective for welfare improvements: relatively larger aggregate efficiency gains are realized that are (almost) Pareto superior. These findings provide guidance for further testing and for future implementation plans of such mechanisms in the real world.

Problem

• Karma-based Credit System
• Resource Allocation
• Fairness and Efficiency

Innovation

• Karma-based Credit System
• Resource Allocation Efficiency
• Scarcity Improvement