REVIEW 14 cited by
Game Theory Meets Large Language Models: A Systematic Survey with Taxonomy and New Frontiers
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Game Theory Meets Large Language Models: A Systematic Survey with Taxonomy and New Frontiers
read the original abstract
Game theory is a foundational framework for analyzing strategic interactions, and its intersection with large language models (LLMs) is a rapidly growing field. However, existing surveys mainly focus narrowly on using game theory to evaluate LLM behavior. This paper provides the first comprehensive survey of the bidirectional relationship between Game Theory and LLMs. We propose a novel taxonomy that categorizes the research in this intersection into four distinct perspectives: (1) evaluating LLMs in game-based scenarios; (2) improving LLMs using game-theoretic concepts for better interpretability and alignment; (3) modeling the competitive landscape of LLM development and its societal impact; and (4) leveraging LLMs to advance game models and to solve corresponding game theory problems. Furthermore, we identify key challenges and outline future research directions. By systematically investigating this interdisciplinary landscape, our survey highlights the mutual influence of game theory and LLMs, fostering progress at the intersection of these fields.
Forward citations
Cited by 14 Pith papers
-
Scale-Dependent Collective Adaptation in Self-Amending LLM Societies: A Cross-Family Study of Emergent Governance
LLM societies in Nomic show non-monotonic collective adaptation peaking at mid-scales, with smaller models rule-inert and larger ones restrictive.
-
Freemium Is All You Need
Under a stylized uniform-value model, an optimal freemium policy can be expressed by two value thresholds, but the paper's case analysis and dynamic optimality claim are not correct.
-
Strategy, Not Payoffs: A Behavioural Embedding of Normal-Form Games
A two-feature game embedding (Nash entropy and best-response switching) predicts cross-game transfer of fine-tuned LLMs on held-out games, outperforming game identity and published structural embeddings.
-
Byzantine Cheap Talk: Adversarial Resilience and Topology Effects in LLM Coordination Games
Byzantine betrayal causes persistent cooperation in some LLMs despite exploitation due to unanimity payoffs, while explicit topology restrictions collapse coordination unlike silent ones, revealing meta-reasoning vuln...
-
MINDGAMES: A Live Arena for Evaluating Social and Strategic Reasoning in Multi-Agent LLMs
Mindgames introduces a four-game evaluation platform for multi-agent LLM reasoning, runs a 944-agent competition, surfaces rule-adherence and error-survival limitations, and releases a 29k-game dataset with an offline...
-
LLM Agents Make Collective Belief Dynamics Programmable: Challenges and Research Directions
LLM agents make collective belief dynamics programmable, with simulations showing coordinated agents induce stable belief shifts, and four structural properties that complicate detection and defense.
-
How memory can affect collective and cooperative behaviors in an LLM-Based Social Particle Swarm
In an LLM-based social swarm, longer memory drives Gemini agents toward defection but pushes Gemma agents toward cooperation: how a model interprets its history, not memory length alone, shapes collective behavior.
-
How memory can affect collective and cooperative behaviors in an LLM-Based Social Particle Swarm
LLM agents in a spatial Prisoner's Dilemma exhibit model-specific effects of memory length on cooperation, with Gemini suppressing and Gemma promoting it as memory increases.
-
Large language models converge on competitive rationality but diverge on cooperation across providers and generations
LLMs converge on competitive rationality and coordination but diverge 48-fold on cooperation, with provider identity and generational shifts as dominant factors across 38 games.
-
When Identity Overrides Incentives: Representational Choices as Governance Decisions in Multi-Agent LLM Systems
Role-based personas in multi-agent LLM systems suppress payoff-aligned behavior, shifting equilibrium selection by up to 90 percentage points in Tragedy of the Commons versus Green Transition scenarios even with full ...
-
CHBench: A Cognitive Hierarchy Benchmark for Evaluating Strategic Reasoning Capability of LLMs
CHBench fits Level-K and Poisson cognitive hierarchy models to LLM game play and uses the fitted reasoning level as a benchmark score.
-
Evolutionary Dynamics of Cooperation in Next-Generation LLM Agent Systems: A Cross-Provider Empirical Extension
Empirical tests on four new frontier LLMs show cooperative equilibria favored in most balanced conditions, with provider identity correlating more strongly with outcomes than model generation.
-
When Identity Overrides Incentives: Representational Choices as Governance Decisions in Multi-Agent LLM Systems
Persona-conditioned LLM agents favor Green outcomes even against explicit Tragedy-dominant payoffs, but the headline 65–90% 'Tragedy equilibrium' recovery is contradicted by the paper's own appendix (0 Tragedy profile...
-
Is Lying an Emergent Behaviour in LLMs? Evidence from Gaslighting AI agents in a Sustainability Game
LLM agents exhibit emergent deception in a sustainability game even without lying permission, with neighbor info increasing attacks while aiding biosphere retention.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.