High Volatility and Action Bias Distinguish LLMs from Humans in Group Coordination

• URL: http://arxiv.org/abs/2604.02578v1
• Date: Thu, 02 Apr 2026 23:21:37 GMT
• Title: High Volatility and Action Bias Distinguish LLMs from Humans in Group Coordination
• Abstract summary: We compare large language models (LLMs) and human performance on a common-interest game with imperfect monitoring: Group Binary Search.<n>Our findings show that, unlike humans who adapt and stabilize their behavior over time, LLMs often fail to improve across games and exhibit excessive switching.
• Score: 3.6704226968275253
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Humans exhibit remarkable abilities to coordinate in groups. As large language models (LLMs) become more capable, it remains an open question whether they can demonstrate comparable adaptive coordination and whether they use the same strategies as humans. To investigate this, we compare LLM and human performance on a common-interest game with imperfect monitoring: Group Binary Search. In this n-player game, participants need to coordinate their actions to achieve a common objective. Players independently submit numerical values in an effort to collectively sum to a randomly assigned target number. Without direct communication, they rely on group feedback to iteratively adjust their submissions until they reach the target number. Our findings show that, unlike humans who adapt and stabilize their behavior over time, LLMs often fail to improve across games and exhibit excessive switching, which impairs group convergence. Moreover, richer feedback (e.g., numerical error magnitude) benefits humans substantially but has small effects on LLMs. Taken together, by grounding the analysis in human baselines and mechanism-level metrics, including reactivity scaling, switching dynamics, and learning across games, we point to differences in human and LLM groups and provide a behaviorally grounded diagnostic for closing the coordination gap.

Related papers

• From Process Loss to Assembly Bonus: Human-Grounded Diagnosis of Multi-Agent LLM Collaboration [11.269223379412315]
  We compare human group chats with matched LLM deliberation traces on Wason-style deductive reasoning.<n>We show the same assembly bonus asymmetry: discussion improves the average member more often than the best initial member.<n>These results suggest that LLM groups can reproduce some outcome-level patterns of human deliberation while diverging in the mechanisms that generate assembly bonus and process loss.
  arXiv  Detail & Related papers  (2026-09-06T17:58:09Z)
• Persuasive and Compliant Tendencies Predict Group Decision-Making in Humans and Language Models [65.21691850622413]
  Large language models (LLMs) are increasingly involved in group decision-making with humans.<n>We introduce DecisionQE, a questionnaire-based framework for measuring each model's persuasive and compliant tendencies.<n>We show that stronger persuasive tendency does not significantly improve group outcomes, whereas compliant-oriented models show more stable advantages in cooperation.
  arXiv  Detail & Related papers  (2026-08-08T15:50:56Z)
• Group Selection Promotes Prosocial Prompts in Populations of LLM Agents [1.7004466773233589]
  We study whether group selection can promote cooperation in populations of large language model (LLM) agents.<n>Under group selection, prompts from high-performing groups are transmitted, thereby promoting prosociality and stabilizing cooperation.<n>Preliminary findings show that, when informed about the selection mechanism, GPT-5.4 preemptively and gradually adjusts first-generation donations.
  arXiv  Detail & Related papers  (2026-06-22T13:47:19Z)
• Benchmarking Open-Ended Multi-Agent Coordination in Language Agents [48.1363632826625]
  Alem embeds procedurally generated coordination tasks, soft specialisation, communication, and controllable coordination difficulty into a long-horizon survival world.<n>We introduce $alem$, a JAX-based benchmark for open-ended multi-agent coordination built on Craftax-like dynamics.
  arXiv  Detail & Related papers  (2026-06-06T21:13:43Z)
• Tacit Coordination of Large Language Models [19.473278869118346]
  Shelling's theory explains how humans coordinate by relying on focal points.<n>This work studies Large Language Models (LLMs) as players in tacit coordination games.
  arXiv  Detail & Related papers  (2026-01-28T13:34:04Z)
• Beyond Survival: Evaluating LLMs in Social Deduction Games with Human-Aligned Strategies [54.08697738311866]
  Social deduction games like Werewolf combine language, reasoning, and strategy.<n>We curate a high-quality, human-verified multimodal Werewolf dataset containing over 100 hours of video, 32.4M utterance tokens, and 15 rule variants.<n>We propose a novel strategy-alignment evaluation that leverages the winning faction's strategies as ground truth in two stages.
  arXiv  Detail & Related papers  (2025-10-13T13:33:30Z)
• Who is a Better Player: LLM against LLM [53.46608216197315]
  We propose an adversarial benchmarking framework to assess the comprehensive performance of Large Language Models (LLMs) through board games competition.<n>We introduce Qi Town, a specialized evaluation platform that supports 5 widely played games and involves 20 LLM-driven players.
  arXiv  Detail & Related papers  (2025-08-05T06:41:47Z)
• Corrupted by Reasoning: Reasoning Language Models Become Free-Riders in Public Goods Games [87.5673042805229]
  How large language models balance self-interest and collective well-being is a critical challenge for ensuring alignment, robustness, and safe deployment.<n>We adapt a public goods game with institutional choice from behavioral economics, allowing us to observe how different LLMs navigate social dilemmas.<n>Surprisingly, we find that reasoning LLMs, such as the o1 series, struggle significantly with cooperation.
  arXiv  Detail & Related papers  (2025-06-29T15:02:47Z)
• SocialEval: Evaluating Social Intelligence of Large Language Models [70.90981021629021]
  Social Intelligence (SI) equips humans with interpersonal abilities to behave wisely in navigating social interactions to achieve social goals.<n>This presents an operational evaluation paradigm: outcome-oriented goal achievement evaluation and process-oriented interpersonal ability evaluation.<n>We propose SocialEval, a script-based bilingual SI benchmark, integrating outcome- and process-oriented evaluation by manually crafting narrative scripts.
  arXiv  Detail & Related papers  (2025-06-01T08:36:51Z)
• Humans expect rationality and cooperation from LLM opponents in strategic games [0.0]
  We present the results of the first monetarily-incentivised laboratory experiment looking at differences in human behaviour.<n>We show that, in this environment, human subjects choose significantly lower numbers when playing against LLMs than humans.<n>This shift is mainly driven by subjects with high strategic reasoning ability.
  arXiv  Detail & Related papers  (2025-05-16T09:01:09Z)
• Confidence-weighted integration of human and machine judgments for superior decision-making [1.4180680537405237]
  Large language models (LLMs) can surpass humans in certain forecasting tasks.<n>One possibility is that humans, despite performing worse than LLMs, can still add value when teamed with them.<n>A human and machine team can surpass each individual teammate when team members' confidence is well-calibrated.
  arXiv  Detail & Related papers  (2024-08-15T11:16:21Z)
• The Wisdom of Partisan Crowds: Comparing Collective Intelligence in Humans and LLM-based Agents [7.986590413263814]
  "Wisdom of partisan crowds" is a phenomenon known as the "wisdom of partisan crowds" We find that partisan crowds display human-like partisan biases, but also converge to more accurate beliefs through deliberation as humans do. We identify several factors that interfere with convergence, including the use of chain-of-thought prompt and lack of details in personas.
  arXiv  Detail & Related papers  (2023-11-16T08:30:15Z)
• Do LLMs exhibit human-like response biases? A case study in survey design [66.1850490474361]
  We investigate the extent to which large language models (LLMs) reflect human response biases, if at all. We design a dataset and framework to evaluate whether LLMs exhibit human-like response biases in survey questionnaires. Our comprehensive evaluation of nine models shows that popular open and commercial LLMs generally fail to reflect human-like behavior.
  arXiv  Detail & Related papers  (2023-11-07T15:40:43Z)
• Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiation [52.930183136111864]
  We propose using scorable negotiation to evaluate Large Language Models (LLMs) To reach an agreement, agents must have strong arithmetic, inference, exploration, and planning capabilities. We provide procedures to create new games and increase games' difficulty to have an evolving benchmark.
  arXiv  Detail & Related papers  (2023-09-29T13:33:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.