Game Theory and Decision Theory Analysis and Applications
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D2: Operations Research and Fuzzy Decision Making".
Deadline for manuscript submissions: 30 April 2026 | Viewed by 32
Special Issue Editors
Interests: game theory; multi-agent decision-making; control systems; deep reinforcement learning; robotics
Interests: autonomous robots; human-robot interaction; robotic learning; deep reinforcement learning; optimal control; cooperative control of multi-robot systems
Special Issue Information
Dear Colleagues,
Game theory and decision theory form the mathematical foundation for analyzing strategic interactions and optimal choices under uncertainty. Over the past decades, these disciplines have evolved from abstract constructs to powerful tools applied across economics, engineering, computer science, political science, and beyond. This Special Issue brings together a curated collection of articles that showcase recent advances in both the theoretical development and practical application of game-theoretic and decision-theoretic frameworks.
The selected contributions span a broad spectrum—from classical Nash equilibria and Bayesian decision models to emerging domains such as algorithmic game theory, behavioral decision analysis, and learning in strategic environments. A key theme throughout this issue is the integration of traditional theory with modern computational and data-driven methods. Whether through reinforcement learning, stochastic modeling, or mechanism design, these papers reflect the growing synergy between analytical rigor and applied relevance.
Authors explore applications in multi-agent systems, security and resource allocation, human–machine interaction, and economic policy design, offering insights into the development of formal models.
Dr. Zejian Zhou
Dr. Chao Jiang
Guest Editors
Manuscript Submission Information
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Keywords
- game theory
- decision theory
- mechanism design
- multi-agent systems
- reinforcement learning
- Bayesian decision models
- strategic learning
- stochastic modeling
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