AI in Game Theory: Theory and Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E5: Financial Mathematics".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 1027

Special Issue Editors


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Guest Editor
Department of Engineering, University of Messina, 98100 Messina, Italy
Interests: Game Theory; multi-objective optimization; evolutionary algorithms; patent analysis; environmental sustainability
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
1. Department of Mathematics, University of California, Riverside, CA 92521, USA
2. Department of Economics, University of Messina, 98122 Messina, Italy
Interests: mathematical economics; Game Theory; decision theory; risk management; bargaining theory; finance; econophysics; quantum finance; quantum mechanics; schwartz distribution theory; differential manifolds; relativity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

In Game Theory, every agent tries to maximize their own profit (utility, payoff, …), in possible competition (mutual dependence) with each other, and, therefore, optimal decisions occur in the presence of utility tradeoffs.

In comparison to a simple multi-objective optimization problem, in Game Theory, the payoff functions of the players vary, showing an interdependence, a kind of indirect interaction, since the payoff of one player depends also upon the strategies of the other players; any player directly affects the payoffs of the others. Often, when binding agreements are allowed, the complex nature of the interaction does not allow us to find compromised solutions in closed-forms. In this case, solving the problem consists of computing (or approximating) a representative boundary of Pareto optimal solutions.

Evolutionary algorithms, which are evolutionary AI-based computer applications, are popular approaches that generate Pareto optimal solutions to Game Theory problems. They use mechanisms that are typically associated with biological evolution and solve problems by continuously exploring promising search space.

AI can so improve decision-making in Game Theory thanks to the ability of rapidly analyzing big sets of data while simulating many different scenarios of the strategy spaces, and thanks to the ability of remembering what happened in previous games and taking into account the most convenient search space for improving the solution.

In this Special Issue, we encourage submissions providing possible applications of AI in Game Theory. The topics of interest for this publication include, but are not limited to, the following:

  • Artificial Intelligence in Game Theory.
  • Artificial Intelligence in decision-making.
  • Evolutionary algorithms in Game Theory.

Machine learning in Pareto fronts.

Dr. Alessia Donato
Prof. Dr. David Carfì
Guest Editors

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Keywords

  • artificial intelligence
  • Game Theory
  • decision-making
  • evolutionary algorithm
  • Pareto front
  • machine learning

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Published Papers (1 paper)

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Research

23 pages, 830 KiB  
Article
A Novel Multi-Task Learning Framework for Interval-Valued Carbon Price Forecasting Using Online News and Search Engine Data
by Dinggao Liu, Liuqing Wang, Shuo Lin and Zhenpeng Tang
Mathematics 2025, 13(3), 455; https://doi.org/10.3390/math13030455 - 29 Jan 2025
Viewed by 638
Abstract
The European Union Emissions Trading System (EU ETS) serves as the cornerstone of European climate policy, providing a critical mechanism for mitigating greenhouse gas emissions. Accurate forecasting of the carbon allowance prices within the market is essential for policymakers, enterprises, and investors. To [...] Read more.
The European Union Emissions Trading System (EU ETS) serves as the cornerstone of European climate policy, providing a critical mechanism for mitigating greenhouse gas emissions. Accurate forecasting of the carbon allowance prices within the market is essential for policymakers, enterprises, and investors. To address the need for interval-valued time series modeling and forecasting in the carbon market, this paper proposes a Transformer-based multi-task learning framework that integrates online news and search engine data information to forecast interval-valued EU carbon allowance futures prices. Empirical evaluations demonstrate that the proposed framework achieves superior predictive accuracy for short-term forecasting and remains robust under high market volatility and economic policy uncertainty compared to single-task learning benchmarks. Furthermore, ablation experiments indicate that incorporating news sentiment intensity and search index effectively enhances the framework’s predictive performance. Interpretability analysis highlights the critical role of specific temporal factors, while the time-varying variable importance analysis further underscores the influence of carbon allowance close prices and key energy market variables and also recognizes the contributions of news sentiment. In summary, this study provides valuable insights for policy management, risk hedging, and portfolio decision-making related to interval-valued EU carbon prices and offers a robust forecasting tool for carbon market prediction. Full article
(This article belongs to the Special Issue AI in Game Theory: Theory and Applications)
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