mlsanityData to 14 Oct 2026

Spontaneous Giving and Calculated Greed in Language Models

arXiv 2502.17720 ↗First posted 24 Feb 2025cs.CL cs.AIcited by 10

On the map: LLM reasoning → Reasoning for agents, planning and code

Abstract (arXiv)

Large language models demonstrate strong problem-solving abilities through reasoning techniques such as chain-of-thought prompting and reflection. However, it remains unclear whether these reasoning capabilities extend to a form of social intelligence: making effective decisions in cooperative contexts. We examine this question using economic games that simulate social dilemmas. First, we apply chain-of-thought and reflection prompting to GPT-4o in a Public Goods Game. We then evaluate multiple off-the-shelf models across six cooperation and punishment games, comparing those with and without explicit reasoning mechanisms. We find that reasoning models consistently reduce cooperation and norm enforcement, favoring individual rationality. In repeated interactions, groups with more reasoning agents exhibit lower collective gains. These behaviors mirror human patterns of "spontaneous giving and calculated greed." Our findings underscore the need for LLM architectures that incorporate social intelligence alongside reasoning, to help address--rather than reinforce--the challenges of collective action.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. Builds on 27 of its 90 references that are in the map. First posted on arXiv 24 Feb 2025.

Builds on

27 of 90 references.

References · 27

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  2. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 8,065
  3. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,330
  4. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
  5. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,720
  6. 2023GPQA: A Graduate-Level Google-Proof Q&A Benchmarkcited by 2,879
  7. 2024Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameterscited by 1,986
  8. 2019Mastering Atari, Go, Chess and Shogi by Planning with a Learned Modelcited by 1,353
  9. 2025s1: Simple test-time scalingcited by 1,341
  10. 2023Large Language Models are not Fair Evaluatorscited by 1,085
  11. 2021Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpuscited by 492
  12. 2023Cognitive Architectures for Language Agentscited by 429
15 more
  1. 2024ReFT: Reasoning with Reinforced Fine-Tuningcited by 239
  2. 2017Multi-agent Reinforcement Learning in Sequential Social Dilemmascited by 209
  3. 2021Large Pre-trained Language Models Contain Human-like Biases of What is Right and Wrong to Docited by 178
  4. 2018Decoupling Strategy and Generation in Negotiation Dialoguescited by 162
  5. 2022Thinking Fast and Slow in Large Language Modelscited by 137
  6. 2024Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agentscited by 125
  7. 2022ProsocialDialog: A Prosocial Backbone for Conversational Agentscited by 109
  8. 2024Nicer Than Humans: How do Large Language Models Behave in the Prisoner's Dilemma?cited by 63
  9. 2019Countering Language Drift via Visual Groundingcited by 55
  10. 2024Shall We Team Up: Exploring Spontaneous Cooperation of Competing LLM Agentscited by 48
  11. 2017Cooperating with Machinescited by 39
  12. 2024Improving Large Language Models via Fine-grained Reinforcement Learning with Minimum Editing Constraintcited by 39
  13. 2019Emergent Linguistic Phenomena in Multi-Agent Communication Gamescited by 36
  14. 2024Cultural Evolution of Cooperation among LLM Agentscited by 32
  15. 2025Actions Speak Louder than Words: Agent Decisions Reveal Implicit Biases in Language Modelscited by 30

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 8.

10 papers.

  1. 2025When Ethics and Payoffs Diverge: LLM Agents in Morally Charged Social Dilemmascited by 15
  2. 2026The Memory Curse: How Expanded Recall Erodes Cooperative Intent in LLM Agentscited by 10
  3. 2026Stop Drawing Scientific Claims from LLM Social Simulations Without Robustness Auditscited by 8
  4. 2026Effects of personality steering on cooperative behavior in Large Language Model agentscited by 2
  5. 2026Representational Similarity and Model Behavior in Multi-Agent Interactioncited by 2
  6. 2025The AI in the Mirror: LLM Self-Recognition in an Iterated Public Goods Gamecited by 1
  7. 2026Predicting Decisions of AI Agents from Limited Interaction through Text-Tabular Modelingcited by 0
  8. 2026Mechanism Plausibility in Generative Agent-Based Modelingcited by 0
  9. 2026Financial Fragility in Societies of LLM Agents: Coordination Failures and Stabilizing Mechanismscited by 0
  10. 2026Beyond Successful Play: Diagnosing Opponent Modeling in Large Language Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Corrupted by Reasoning: Reasoning Language Models Become Free-Riders in Public Goods Games
  2. 2025Emergent Social Dynamics of LLM Agents in the El Farol Bar Problem
  3. 2025Humans expect rationality and cooperation from LLM opponents in strategic games
  4. 2025Social preferences with unstable interactive reasoning: Large language models in economic trust games
  5. 2025Collaboration and Conflict between Humans and Language Models through the Lens of Game Theory
  6. 2025GTAlign: Game-Theoretic Alignment of LLM Assistants for Social Welfare
  7. 2025The Fragility of Strategic Thinking in Large Language Models
  8. 2025Evolution of Cooperation in LLM-Agent Societies: A Preliminary Study Using Different Punishment Strategies