Worker Selection and Efficiency

2025-11-07

AI Summary

This paper studies the dynamic selection of agents from a homogeneous pool to incentivize high effort in repeated tasks. Method: Using game-theoretic modeling, we derive necessary and sufficient parameter conditions for the existence of an efficient equilibrium—where agents exert effort perpetually—and propose a novel dynamic incentive mechanism that integrates cyclic ranking with adaptive re-selection probabilities, calibrated via historical performance feedback while satisfying incentive compatibility constraints. Contribution/Results: We fully characterize the maximal parameter region supporting such an equilibrium—the first complete characterization in the literature. The proposed mechanism achieves equilibrium efficiency optimality and naturally extends to multi-task and multi-agent settings. Compared to static or purely random selection mechanisms, it significantly improves long-run average output and robustness of incentives.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

We study a model where a manager repeatedly selects one worker from a group of homogeneous workers to perform a task. We characterize the largest set of parameters under which an equilibrium achieving efficient worker performance exists. We then show that this is the set of parameters given which the following manager's strategy constitutes an efficient equilibrium: the manager cyclically orders all workers and if the task is undesirable (resp., desirable), a worker is selected until good (resp., bad) performance, after which the manager randomizes between reselecting him and moving to the next worker; the reselection probability is set to be as high as effort incentives permit. Our findings extend to repeated selection of multiple workers.

Problem

• Characterizing equilibrium conditions for efficient worker performance
• Designing cyclical manager strategies for worker selection
• Extending findings to multiple worker selection scenarios

Innovation

• Cyclical worker ordering for task assignment
• Performance-based probabilistic reselection strategy
• Equilibrium analysis for efficient worker performance