Scholarlink

Abstract <p>This paper examines games on networks with linear best responses, which allow for the analysis of how interaction structures influence agents’ strategic behavior. Special attention is given to intervention issues in such models, particularly in selecting optimal intervention strategies aimed at maximizing the central planner’s objective function. Two main control policies are analyzed: individual agent incentives and modifications of the interaction structure. The concept of a representative agent is introduced to simplify equilibrium analysis and control problems in games on networks. Both aggregate outcome maximization problems and adversarial scenarios between competing central planners are considered. Analytical conditions are derived to determine whether controlling the interaction structure is more effective than influencing individual incentives. Numerical experiments confirm the theoretical results and demonstrate their applicability to different types of network structures.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Comparative Analysis of Incentive-Based and Structural Control in Games on Networks with Linear Best Response

  • 期刊论文

  • 发表日期：

    2025年12月1日

查看全文
  • I. V. Petrov

摘要

Abstract

This paper examines games on networks with linear best responses, which allow for the analysis of how interaction structures influence agents’ strategic behavior. Special attention is given to intervention issues in such models, particularly in selecting optimal intervention strategies aimed at maximizing the central planner’s objective function. Two main control policies are analyzed: individual agent incentives and modifications of the interaction structure. The concept of a representative agent is introduced to simplify equilibrium analysis and control problems in games on networks. Both aggregate outcome maximization problems and adversarial scenarios between competing central planners are considered. Analytical conditions are derived to determine whether controlling the interaction structure is more effective than influencing individual incentives. Numerical experiments confirm the theoretical results and demonstrate their applicability to different types of network structures.