Agents Require Metacognitive and Strategic Reasoning to Succeed in the Coming Labor Markets
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Abstract (arXiv)
Current labor markets are strongly affected by the economic forces of adverse selection, moral hazard, and reputation, each of which arises due to textitincomplete information. These economic forces will still be influential after AI agents are introduced, and thus, agents must use metacognitive and strategic reasoning to perform effectively. Metacognition is a form of textitinternal reasoning that includes the capabilities for self-assessment, task understanding, and evaluation of strategies. Strategic reasoning is textitexternal reasoning that covers holding beliefs about other participants in the labor market (e.g., competitors, colleagues), making strategic decisions, and learning about others over time. Both types of reasoning are required by agents as they decide among the many textitactions they can take in labor markets, both within and outside their jobs. We discuss current research into metacognitive and strategic reasoning and the areas requiring further development.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 43 of its 79 references that are in the map. First posted on arXiv 26 May 2025.
Builds on
43 of 79 references.
References · 43
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
- 2017Deep reinforcement learning from human preferencescited by 3,912
- 2023Let's Verify Step by Stepcited by 3,789
- 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
- 2022Constitutional AI: Harmlessness from AI Feedbackcited by 3,050
- 2023Voyager: An Open-Ended Embodied Agent with Large Language Modelscited by 2,191
- 2022Language Models (Mostly) Know What They Knowcited by 1,856
- 2022Red Teaming Language Models with Language Modelscited by 1,026
- 2022STaR: Bootstrapping Reasoning With Reasoningcited by 940
- 2024Large Language Model based Multi-Agents: A Survey of Progress and Challengescited by 769
- 2023Are Emergent Abilities of Large Language Models a Mirage?cited by 523
- 2023Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Modelscited by 349
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- 2023Alphazero-like Tree-Search can Guide Large Language Model Decoding and Trainingcited by 295
- 2023Playing repeated games with Large Language Modelscited by 217
- 2024OS-Copilot: Towards Generalist Computer Agents with Self-Improvementcited by 205
- 2025When More is Less: Understanding Chain-of-Thought Length in LLMscited by 183
- 2023Clever Hans or Neural Theory of Mind? Stress Testing Social Reasoning in Large Language Modelscited by 163
- 2023Take a Step Back: Evoking Reasoning via Abstraction in Large Language Modelscited by 159
- 2024Systematic Biases in LLM Simulations of Debatescited by 99
- 2024LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Modelscited by 99
- 2024GameBench: Evaluating Strategic Reasoning Abilities of LLM Agentscited by 95
- 2024Understanding Emergent Abilities of Language Models from the Loss Perspectivecited by 94
- 2024What Did I Do Wrong? Quantifying LLMs' Sensitivity and Consistency to Prompt Engineeringcited by 90
- 2024Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solvingcited by 84
- 2021Consequences of Misaligned AIcited by 80
- 2023ChessGPT: Bridging Policy Learning and Language Modelingcited by 75
- 2022CLAM: Selective Clarification for Ambiguous Questions with Generative Language Modelscited by 69
- 2024Game-theoretic LLM: Agent Workflow for Negotiation Gamescited by 56
- 2023Strategic Behavior of Large Language Models: Game Structure vs. Contextual Framingcited by 51
- 2023Theory of Mind in Large Language Models: Examining Performance of 11 State-of-the-Art models vs. Children Aged 7-10 on Advanced Testscited by 48
- 2024Werewolf Arena: A Case Study in LLM Evaluation via Social Deductioncited by 45
- 2020AI Research Considerations for Human Existential Safety (ARCHES)cited by 33
- 2024Explore Theory of Mind: Program-guided adversarial data generation for theory of mind reasoningcited by 33
- 2024Language Models Represent Beliefs of Self and Otherscited by 31
- 2025Adaptive Tool Use in Large Language Models with Meta-Cognition Triggercited by 30
- 2024TMGBench: A Systematic Game Benchmark for Evaluating Strategic Reasoning Abilities of LLMscited by 24
- 2025Metacognition and Uncertainty Communication in Humans and Large Language Modelscited by 23
- 2025Decompose-ToM: Enhancing Theory of Mind Reasoning in Large Language Models through Simulation and Task Decompositioncited by 17
- 2025LLMs can be easily Confused by Instructional Distractionscited by 13
- 2025Do Large Language Models Know How Much They Know?cited by 11
- 2025A Survey of Theory of Mind in Large Language Models: Evaluations, Representations, and Safety Riskscited by 8
- 2025Shh, don't say that! Domain Certification in LLMscited by 7
- 2025Assessing the Impact of Code Changes on the Fault Localizability of Large Language Modelscited by 4
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