arXiv AI
Jun 17
Using Cognitive Models to Improve Language Model Simulation of Human Persuasion Games
arXiv:2606. 17657v1 Announce Type: new Abstract: People make decisions differently in strategic interactions.
Game-Theoretic Solvers with Bayesian Opponent Modeling and Moral Rhetoric Framing for GLEE
The paper presents an agent designed for the GLEE Competition that engages in bargaining, negotiation, and persuasion games against human and AI opponents. The agent combines a classical game‑theoretic solver, an online Bayesian layer for estimating opponent archetypes, and a language model for generating messages. Experiments include 13,200 Monte Carlo games against 11 synthetic archetypes, an ablation study of the opponent‑modeling layer, and an analysis of 9,337 live‑game messages showing a strong correlation between moral rhetoric framing and buyer purchase decisions.
Related stories
arXiv AI
Sep 21
Why Do LLMs Struggle in Strategic Play? Broken Links Between Observations, Beliefs, and Actions
The paper investigates why large language models (LLMs) struggle in strategic decision-making under incomplete information. It identifies two key gaps: an observation‑belief gap where LLMs’ internal representations of game states are accurate but brittle, and a belief‑action gap where converting these internal beliefs into actions is weak, leading to suboptimal payoffs. Experiments with Llama 3.1, Qwen3, and gpt‑oss confirm that acting optimally on decoded beliefs would improve outcomes in most games, highlighting a bottleneck in belief‑to‑action conversion.
arXiv AI
Aug 18
The Fragility of Strategic Thinking in Large Language Models
arXiv:2510. 10813v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly applied to domains that require reasoning about other agents' behavior, such as negotiation, policy design, and market simulation.
arXiv AI
Aug 25
Effects of Theory of Mind and Prosocial Beliefs on Steering Human-Aligned Behaviors of LLMs in Ultimatum Games
The study examines how Theory of Mind (ToM) reasoning and prosocial beliefs influence large language models (LLMs) in the ultimatum game. By initializing LLM agents with Greedy, Fair, or Selfless beliefs and applying chain‑of‑thought or varying levels of ToM reasoning, the authors ran 2,700 simulations across several models, including o3‑mini and DeepSeek‑R1 Distilled Qwen 32B. Results show that ToM‑enhanced LLMs align more closely with human decision patterns, exhibit greater consistency, and achieve better negotiation outcomes, with Llama 3.3 70B producing the most belief‑consistent reasoning. whyItMatters":"The findings clarify the importance of incorporating Theory of Mind into LLMs to improve their alignment with human norms in cooperative decision‑making tasks."
arXiv AI
Jul 2
Large language models replicate and predict human cooperation across experiments in game theory
arXiv:2511. 04500v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed as decision-making agents in high-stakes domains and as imitators of human behavior in the social and behavioral sciences.
arXiv Computation and Language
2d ago
Implicit Beliefs as Scalable Process Rewards for Strategic Persuasion
arXiv:2610.33527v1 Announce Type: cross Abstract: Training large language models (LLMs) for multi-turn strategic persuasion faces two coupled obstacles: (i) realistic simulation of psychologically gr...
arXiv AI
Oct 15
Choosing LLMs for Strategic Agent Interactions
arXiv:2610.18354v1 Announce Type: new Abstract: Game-theoretic benchmarks claim that large language model (LLM) strategic decision-making varies across game types, interaction structures, and capabil...
arXiv Machine Learning
Jul 15
When and Why Does Multi-Agent Debate Fail and Does It Really Underperform?
arXiv:2510. 20963v2 Announce Type: replace Abstract: Multi-agent debate (MAD) was proposed as a promising approach for ensembling the wisdom of multiple large language models (LLMs) to improve reasoning and provide effective supervision to superhuman LLMs.
arXiv Machine Learning
Aug 10
Solver-Guided Reasoning for Mixed-Equilibrium Strategies
arXiv:2608. 06741v1 Announce Type: new Abstract: Reasoning in large language models (LLMs) is often grounded in human text, human demonstrations, and human-generated rationales.
arXiv AI
Aug 28
Assessing mentalization in humans and large language models
The study evaluates mentalization—the capacity to infer others’ beliefs and intentions—in large language models (LLMs) using two economic games and cognitive computational modeling. Researchers tested 2,099 LLM agents from four model families (DeepSeek, GPT‑4.1, GPT‑5, Gemini 2.0 Flash) against opponents of varying sophistication, comparing their performance to 251 human participants. Results show that LLMs exhibit distinct mentalizing behaviors that vary by model provider and size, with strategic prompting generally enhancing performance; notably, GPT‑5 agents adapt their recursive reasoning depth to match opponent sophistication, outperforming humans in one task.
arXiv Computation and Language
Oct 8
The Confidence Game: Strategic Miscalibration in Human-AI Delegation
arXiv:2610.09371v1 Announce Type: cross Abstract: Calibrated uncertainty quantification is essential to ensuring AI agents are trustworthy and reliable. However, when agents seek to maximize user eng...
arXiv AI
Aug 17
From Passive Delegates to Strategic Negotiators: Reinforcing Social Reasoning in Small Language Models with SocialRL
arXiv:2608. 13787v1 Announce Type: new Abstract: AI agents increasingly act on their users' behalf, handling tasks such as scheduling meetings, comparing offers, and haggling over prices.