Search: game-theoretic - ai.jp.net

Paper #Public Health, Game Theory, Epidemic Modeling 🔬 ResearchAnalyzed: Jan 3, 2026 19:07

Economic Status, Authority, and Behavior in Disease Control

Published:Dec 29, 2025 03:57

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1 min read

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ArXiv

Analysis

This paper is significant because it moves beyond simplistic models of disease spread by incorporating nuanced human behaviors like authority perception and economic status. It uses a game-theoretic approach informed by real-world survey data to analyze the effectiveness of different public health policies. The findings highlight the complex interplay between social distancing, vaccination, and economic factors, emphasizing the importance of tailored strategies and trust-building in epidemic control.

Key Takeaways

•Economic status and perception of authority significantly influence individual responses to public health policies.
•Social distancing and vaccination decisions are interconnected, creating trade-offs.
•Adaptive guidelines targeting infected individuals can be more effective than blanket policies.
•Lowering vaccination costs can incentivize vaccination, but is most effective when combined with social distancing.
•Trust-building is crucial for successful epidemic mitigation.

Reference

“Adaptive guidelines targeting infected individuals effectively reduce infections and narrow the gap between low- and high-income groups.”

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Research Paper #Cybersecurity, AI, Agentic AI, Resilience 🔬 ResearchAnalyzed: Jan 3, 2026 16:19

Agentic AI for Cyber Resilience: A New Security Paradigm

Published:Dec 28, 2025 11:17

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1 min read

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ArXiv

Analysis

This paper proposes a significant shift in cybersecurity from prevention to resilience, leveraging agentic AI. It highlights the limitations of traditional security approaches in the face of advanced AI-driven attacks and advocates for systems that can anticipate, adapt, and recover from disruptions. The focus on autonomous agents, system-level design, and game-theoretic formulations suggests a forward-thinking approach to cybersecurity.

Key Takeaways

•Proposes a shift from prevention-centric to resilience-focused cybersecurity.
•Advocates for the use of agentic AI for autonomous sensing, reasoning, action, and adaptation.
•Introduces a system-level framework for designing agentic AI workflows.
•Emphasizes game-theoretic formulations for designing autonomy, information flow, and temporal composition.
•Presents case studies in automated penetration testing, remediation, and cyber deception.

Reference

“Resilient systems must anticipate disruption, maintain critical functions under attack, recover efficiently, and learn continuously.”

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Research Paper #Cybersecurity, AI, Networking 🔬 ResearchAnalyzed: Jan 3, 2026 16:21

Cyber Resilience in Next-Generation Networks

Published:Dec 27, 2025 23:00

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1 min read

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ArXiv

Analysis

This paper addresses the critical need for cyber resilience in modern, evolving network architectures. It's particularly relevant due to the increasing complexity and threat landscape of SDN, NFV, O-RAN, and cloud-native systems. The focus on AI, especially LLMs and reinforcement learning, for dynamic threat response and autonomous control is a key area of interest.

Key Takeaways

•Addresses the evolving threat landscape in modern network architectures.
•Explores advanced resilience paradigms like zero trust and self-healing.
•Highlights the role of AI, including LLMs, in enhancing cyber resilience.

Reference

“The core of the book delves into advanced paradigms and practical strategies for resilience, including zero trust architectures, game-theoretic threat modeling, and self-healing design principles.”

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Research Paper #Neural Network Pruning, Game Theory, Sparsity 🔬 ResearchAnalyzed: Jan 3, 2026 16:31

Pruning Neural Networks as a Game: An Equilibrium Approach

Published:Dec 26, 2025 18:25

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1 min read

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ArXiv

Analysis

This paper introduces a novel perspective on neural network pruning, framing it as a game-theoretic problem. Instead of relying on heuristics, it models network components as players in a non-cooperative game, where sparsity emerges as an equilibrium outcome. This approach offers a principled explanation for pruning behavior and leads to a new pruning algorithm. The focus is on establishing a theoretical foundation and empirical validation of the equilibrium phenomenon, rather than extensive architectural or large-scale benchmarking.

Key Takeaways

•Proposes a game-theoretic framework for neural network pruning.
•Sparsity emerges as an equilibrium outcome.
•Offers a principled explanation for pruning.
•Develops a new equilibrium-driven pruning algorithm.
•Achieves competitive sparsity-accuracy trade-offs.

Reference

“Sparsity emerges naturally when continued participation becomes a dominated strategy at equilibrium.”

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Research #Probability 🔬 ResearchAnalyzed: Jan 10, 2026 07:44

Minimax Duality Explored in Game-Theoretic Probability

Published:Dec 24, 2025 07:48

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1 min read

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ArXiv

Analysis

This article discusses a highly specialized topic within the field of probability theory, specifically focusing on the application of minimax duality. The research, available on ArXiv, suggests potentially complex mathematical implications.

Key Takeaways

•The article focuses on minimax duality.
•The research is published on ArXiv.
•The subject matter is within the realm of game-theoretic probability.

Reference

“The source is ArXiv.”

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Research #Review 🔬 ResearchAnalyzed: Jan 10, 2026 10:35

Strategic Coauthor Nominations: A Mathematical Analysis of ICLR 2026 Reciprocal Review

Published:Dec 17, 2025 01:21

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1 min read

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ArXiv

Analysis

This ArXiv paper likely presents a novel mathematical framework for optimizing coauthor nominations within the context of the ICLR 2026 reciprocal review policy, aiming to maximize review quality or acceptance probability. The analysis likely delves into game-theoretic aspects, considering strategic interactions among authors.

Key Takeaways

•The paper analyzes coauthor nomination strategies under the ICLR 2026 reciprocal review policy.
•It likely uses mathematical modeling or game theory to determine optimal nomination choices.
•The findings could inform researchers on how to strategically nominate coauthors to improve their chances of paper acceptance or enhance review quality.

Reference

“The paper focuses on the ICLR 2026 reciprocal reviewer nomination policy.”

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Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 11:58

Imitation Game: Reproducing Deep Learning Bugs Leveraging an Intelligent Agent

Published:Dec 17, 2025 00:50

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1 min read

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ArXiv

Analysis

This article, sourced from ArXiv, likely discusses a novel approach to identifying and replicating bugs in deep learning models. The use of an intelligent agent suggests an automated or semi-automated method for probing and exploiting vulnerabilities. The title hints at a game-theoretic or adversarial perspective, where the agent attempts to 'break' the model.

Key Takeaways

Reference

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Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 10:34

Generative AI as Digital Representatives in Collective Decision-Making: A Game-Theoretical Approach

Published:Dec 14, 2025 07:35

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1 min read

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ArXiv

Analysis

This article explores the use of generative AI in collective decision-making, employing a game-theoretical framework. The focus is on how AI can act as digital representatives. The research likely analyzes the strategic interactions and outcomes when AI agents participate in decision-making processes. The use of game theory suggests a focus on modeling and predicting the behavior of these AI representatives and the overall system dynamics.

Key Takeaways

Reference

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Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 07:36

Attacking and Securing Community Detection: A Game-Theoretic Framework

Published:Dec 12, 2025 08:17

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1 min read

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ArXiv

Analysis

This article from ArXiv likely presents a novel approach to community detection, a common task in network analysis. The use of a game-theoretic framework suggests a focus on adversarial scenarios, where the goal is to understand how to both attack and defend against manipulations of community structure. The research likely explores the vulnerabilities of community detection algorithms and proposes methods to make them more robust.

Key Takeaways

Reference

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Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 08:04

Probabilistic Neuro-Symbolic Reasoning for Sparse Historical Data: A Framework Integrating Bayesian Inference, Causal Models, and Game-Theoretic Allocation

Published:Dec 1, 2025 14:35

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1 min read

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ArXiv

Analysis

This article presents a research framework. The title clearly states the core components: probabilistic neuro-symbolic reasoning, Bayesian inference, causal models, and game-theoretic allocation. The focus is on handling sparse historical data, suggesting a potential application in areas where data is limited or incomplete. The integration of these diverse techniques indicates a complex and potentially powerful approach to data analysis and decision-making.

Key Takeaways

•Focus on probabilistic neuro-symbolic reasoning.
•Integration of Bayesian inference, causal models, and game-theoretic allocation.
•Addresses the challenge of sparse historical data.

Reference

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Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 08:47

Game-Theoretic Approach for Adversarial Information Fusion in Distributed Sensor Networks

Published:Nov 28, 2025 09:47

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1 min read

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ArXiv

Analysis

This article presents a research paper focusing on a game-theoretic approach to address adversarial attacks in distributed sensor networks. The core idea is to use game theory to model the interactions between sensors and adversaries, aiming to improve the robustness and reliability of information fusion. The research likely explores how to design strategies that can mitigate the impact of malicious data injection or manipulation.

Key Takeaways

•Applies game theory to improve the resilience of distributed sensor networks.
•Addresses adversarial attacks on information fusion.
•Focuses on strategies to mitigate malicious data manipulation.

Reference

“The article is a research paper, so a direct quote is not readily available without accessing the full text. The focus is on a game-theoretic approach.”

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Research #Agent 🔬 ResearchAnalyzed: Jan 10, 2026 14:06

Game-Theoretic Framework for Multi-Agent Theory of Mind

Published:Nov 27, 2025 15:13

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1 min read

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ArXiv

Analysis

This research explores a novel approach to understanding multi-agent interactions using game theory. The framework likely aims to improve how AI agents model and reason about other agents' beliefs and intentions.

Key Takeaways

•Focuses on developing a computable framework.
•Applies game theory principles.
•Addresses multi-agent theory of mind.

Reference

“The research is available on ArXiv.”

Permalink ArXiv

Economic Status, Authority, and Behavior in Disease Control

Analysis

Key Takeaways

Agentic AI for Cyber Resilience: A New Security Paradigm

Analysis

Key Takeaways

Cyber Resilience in Next-Generation Networks

Analysis

Key Takeaways

Pruning Neural Networks as a Game: An Equilibrium Approach

Analysis

Key Takeaways

Minimax Duality Explored in Game-Theoretic Probability

Analysis

Key Takeaways

Strategic Coauthor Nominations: A Mathematical Analysis of ICLR 2026 Reciprocal Review

Analysis

Key Takeaways

Imitation Game: Reproducing Deep Learning Bugs Leveraging an Intelligent Agent

Analysis

Key Takeaways

Generative AI as Digital Representatives in Collective Decision-Making: A Game-Theoretical Approach

Analysis

Key Takeaways

Attacking and Securing Community Detection: A Game-Theoretic Framework

Analysis

Key Takeaways

Probabilistic Neuro-Symbolic Reasoning for Sparse Historical Data: A Framework Integrating Bayesian Inference, Causal Models, and Game-Theoretic Allocation

Analysis

Key Takeaways

Game-Theoretic Approach for Adversarial Information Fusion in Distributed Sensor Networks

Analysis

Key Takeaways

Game-Theoretic Framework for Multi-Agent Theory of Mind

Analysis

Key Takeaways

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