# Cooperative and Noncooperative Paradigms for Game-Theoretic Control of Socio-Technical Systems

arXiv: https://arxiv.org/abs/2605.17886 · HTML: https://mlsanity.com/paper/2605.17886/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2026.

## Built on by (most cited first, 1 of 1)

- [Internet of Agentic Things: Networked AI Agents for Closed-Loop IoT Orchestration](https://mlsanity.com/paper/2607.12662/) (2026)

## Abstract (arXiv)

This tutorial presents cooperative and noncooperative game-theoretic frameworks for modeling, learning, and control in socio-technical systems, where human behavior, incentives, institutions, and social interactions are coupled with cyber-physical and networked infrastructures. The paper reviews strategic, dynamic, cooperative, matching, learning, and feedback-control approaches for analyzing how local decision-making, adaptation, and strategic interactions shape collective system outcomes. The tutorial further develops feedback-learning and incentive-design perspectives that connect equilibrium analysis with adaptation, distributed control, and mechanism design under information and coordination constraints. We also examine resilience and security challenges arising from adversarial behavior, misinformation, disruptions, and cascading failures in interconnected socio-technical networks. Finally, we discuss emerging research directions at the intersection of game theory, control, learning, and network science for resilient and adaptive socio-technical systems.