Network Intervention by Polling Strategic Agents

2026-10-06

AI Summary

This study addresses the challenges of private information dependence, strategic misreporting, and computational intractability in network planning by proposing Poll, an optimal pricing algorithm based on centrality decomposition. The algorithm reveals the centrality decomposition structure of the welfare core and achieves distributed, non-discriminatory pricing through local sampling updates. Experimental results demonstrate that Poll reduces communication overhead by three orders of magnitude on networks comprising over 300,000 nodes. Furthermore, it effectively incentivizes truthful reporting and converges to welfare-maximizing prices. By substantially enhancing both computational and communication efficiency, this work advances mechanism design for large-scale network games.

Abstract

A planner in a network of strategic agents faces three entangled challenges: the optimum depends on agents' private information, queried agents may misreport to steer the outcome, and exact computation does not scale. We study these challenges in multi-activity network games with heterogeneous private technologies, in which the planner sets non-discriminatory prices. We show that the optimal prices admit a centrality-based decomposition of the welfare kernel: each agent's contribution scales with its squared centrality in a network reweighted by agents' preferences across activities. This decomposition motivates Poll, a polling algorithm in which the planner samples one agent per round, walks briefly through the agent's neighborhood, and updates the price from a local report. From the same decomposition flow three forms of efficiency: computationally, Poll uses significantly fewer operations than exact computation and other distributed methods, requiring up to three orders of magnitude less communication on a real-world network with over 300,000 agents; statistically, its query complexity scales with topology and preference heterogeneity rather than explicitly with population size; and economically, it converges to welfare-maximizing prices while admitting behavior-specific implementations that induce truthful reports and detect adversarial deviations.

Problem

• Network Intervention
• Strategic Agents
• Mechanism Design
• Multi-activity Network Games
• Welfare Maximization

Innovation

• Network Intervention
• Centrality-based Decomposition
• Polling Algorithm
• Strategic Agents
• Query Complexity