[Paper] Measuring the benefits of lying in MARA under egalitarian social welfare

Source: arXiv - 2601.09354v1

Overview

The paper investigates how agents can profit from misrepresenting their preferences when a resource‑allocation mechanism aims for egalitarian social welfare—i.e., it tries to maximize the utility of the worst‑off participant. By running extensive experiments with genetic algorithms, the authors quantify “the benefit of lying” across a variety of scenarios, shedding light on when strategic deception actually improves an agent’s outcome.

Key Contributions

• Formal analysis of strategic lying in egalitarian allocation problems, highlighting the tension between fairness and incentive compatibility.
• Genetic‑algorithm‑based simulation framework that efficiently explores large, combinatorial preference spaces where exact analysis would be infeasible.
• Empirical quantification of the utility gains agents can achieve by misreporting, under multiple resource‑distribution settings (different numbers of agents, resource values, and preference structures).
• Identification of structural patterns (e.g., resource heterogeneity, number of agents) that amplify or diminish the advantage of lying.
• Guidelines for mechanism designers on when egalitarian objectives are most vulnerable to manipulation.

Methodology

1. Problem Formalization – The authors model the allocation as a classic assignment problem: a set of indivisible resources must be assigned to agents, each with a private utility vector. The egalitarian objective selects the allocation that maximizes the minimum utility among agents.
2. Strategic Misreporting – Agents are allowed to submit any utility vector (not necessarily their true preferences). The “benefit of lying” is defined as the difference between an agent’s utility under a truthful report and under an optimal deceptive report.
3. Genetic Algorithm (GA) Engine
   • Encoding: Each chromosome encodes a full profile of reported utilities for all agents.
   • Fitness Function: The egalitarian welfare of the resulting allocation, plus a term that rewards higher utility for a target “lying” agent.
   • Evolutionary Operators: Standard crossover and mutation, tuned to preserve feasibility (e.g., non‑negative utilities).
   • Search Strategy: Multiple GA runs per scenario to avoid local optima, with statistical aggregation of results.
4. Experimental Scenarios – Varying numbers of agents (5–30), resource counts, utility distributions (uniform, skewed), and correlation levels among agents’ true preferences.

Results & Findings

• Non‑trivial Gains: In many settings, a lying agent can increase its utility by 10–35 % compared to truthful reporting, even though the mechanism is designed to be fairness‑oriented.
• Resource Heterogeneity Matters: When resources have highly disparate values, the incentive to lie spikes because securing a high‑value item dramatically lifts the minimum utility.
• Agent Count Effect: Smaller groups (≤10 agents) exhibit larger relative benefits, while larger groups dilute the impact of a single deceptive report.
• Preference Correlation: Low correlation (agents value different items) creates more “room” for manipulation; high correlation reduces the advantage because the egalitarian allocation already aligns with most agents’ top choices.
• Robustness of GA: The evolutionary search consistently found near‑optimal deceptive reports, confirming that the problem is computationally tractable for realistic instance sizes.

Practical Implications

• Design of Fair Allocation Systems: Platforms that allocate tasks, compute resources, or public goods using egalitarian criteria (e.g., load balancers, cloud spot‑instance markets) need to incorporate strategy‑proofness checks; otherwise, participants may game the system for personal gain.
• Policy‑Level Safeguards: Regulators and system architects can use the authors’ GA framework as a stress‑testing tool to evaluate how vulnerable a proposed allocation rule is to manipulation before deployment.
• Incentive‑Aligned Mechanism Design: The findings motivate hybrid objectives (e.g., combining egalitarian with utilitarian or Nash‑welfare components) that retain fairness while reducing exploitable gaps.
• Developer Toolkits: Open‑source implementations of the GA can be integrated into simulation pipelines for multi‑agent systems, enabling rapid prototyping of “what‑if” scenarios where agents may lie.

Limitations & Future Work

• Synthetic Preferences: Experiments rely on generated utility distributions; real‑world preference data could reveal different manipulation dynamics.
• Single‑Agent Deception Focus: The study primarily examines one lying agent; coordinated collusion among multiple agents remains unexplored.
• Scalability Beyond 30 Agents: While the GA scales reasonably, very large systems (hundreds of agents) may require more sophisticated heuristics or parallelization.
• Extension to Dynamic Settings: Future research could investigate repeated allocations where agents learn and adapt their lying strategies over time.

Bottom line: Even fairness‑driven mechanisms like egalitarian social welfare are not immune to strategic deception. By quantifying the “benefit of lying,” this work equips developers and system designers with the empirical evidence needed to build more robust, manipulation‑resistant allocation platforms.

Authors

• Jonathan Carrero
• Ismael Rodriguez
• Fernando Rubio

Paper Information

• arXiv ID: 2601.09354v1
• Categories: cs.GT, cs.NE
• Published: January 14, 2026
• PDF: Download PDF