Can AI with High Reasoning Ability Replicate Human-like Decision Making in Economic Experiments?

Ayato Kitadai, Sinndy Dayana Rico Lugo, Yudai Tsurusaki, Yusuke Fukasawa, Nariaki Nishino · Group Decision and Negotiation · 2025 · experimental-economics · 10.1007/s10726-025-09946-9 · record kitadai2025-can-ai-with-high-reasoning-ability
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Where this record's text came from. faceted 2026-08 by reading the locally held text (pdftext); every evidence string verified as an exact substring of that text before merging. Claim type is null-result rather than method: the paper builds a simulation framework, but its headline finding is that the hypothesised improvement (more reasoning → more human-like behaviour) did not occur, and the authors conclude that LLM agents are currently unwarranted stand-ins for human groups. The paper also attempts to reproduce Lin et al. (2020)'s human ultimatum-game data in silico and fails to match it; that is a simulation of an experiment rather than an independent replication of this paper's own claim, so replication status stays 'untested' — the internal robustness checks (15 and 20 examples, re-drawn random seeds) are the authors' own, not external. A secondary positive claim about personas is present ('The results also suggest that setting the personas of the generative agents may be important for reproducing the results of real economic experiments.') but is hedged and not the paper's central result, so it is not the recorded claim_type. 2026-08-14 and 2026-08-15 schema-0.3 passes: every quote verified as an exact substring of the staged text and anchored at merge time; concept terms verified present; every edge endpoint an existing node.
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Other fields define these differently the same word, defined another way in another discipline
interpretability 3 fields · LLMs 2 fields · confidence interval 2 fields · simulation 2 fields · bounded rationality 2 fields · social context 2 fields

Tags on this paper

Every tag the record carries, each linking to every other paper in the corpus that carries it. A tag appears here whether or not its words occur in the article — the spans below are only the ones a sentence in this paper licenses. Of the 121 concept tags on this paper, all 121 are grounded in a quoted sentence. 115 carry the tagger's gloss of what the term means in this paper, which is not something the article can license.
generative agent economic experiments behavioral realism reasoning ability theoretical solution persona one-shot ultimatum game history-dependent learning reputation effects subgame-perfect equilibrium theoretical benchmark human-AI performance gap stringent test bed artificial correlations weakly dominant strategy perceived unfairness LLM agents traditional economic experiments additional methodological tool opaque biases harmful stereotypes structural inequities ground-truth benchmarks LLM-based simulations boundary conditions outputs of generative agents simulation design choices Generative AI generative models discriminative models underlying data distribution recognition to generation methodological transformation LLMs key subclass Transformer architecture statistical knowledge base social norms strategic heuristics foundation models general-purpose substrates downstream tasks retraining hallucinations learned statistical associations grounded world models black boxes interpretability prompt design few-shot prompting chain-of-thought (CoT) prompting general mitigation techniques scaffolding methods prompting strategies reasoning quality coherence, inference depth, and contextual relevance contextually coherent, multi-step responses inferential structure observable behavioral property experimental variable pragmatist perspective human cognition behavioral patterns methodological tools well-defined simulation contexts empirical tools evaluation protocols methodological safeguards coarse-grained evaluation criteria aggregate distributions individual-level deviations finer-grained behavioral criteria game-theoretic environments failures of alignment behavioral Turing tests latent utility function perceived social harmony material self-interest Persona design behavioral tendencies causal effects reasoning-related prompting strategy confounding variables central research question normative benchmarks of rationality empirically grounded human behavior simulation framework three sequential steps output format 1000 independent decisions formal power analysis robustness check computational cost per generation statistical resolution feasibility in simulation-based research substitutes for human participants agent-based modeling natural language reasoning prompt engineering Monte Carlo methods experimental instructions human-subject experiments Anderson-Darling (AD) Kolmogorov-Smirnov (KS) overall distributional differences maximum cumulative distribution differences social context stand-ins for real human groups treating LLMs as direct substitutes for human subjects flawed approach methodological scaffolding Persona settings contextual variables human decision-making inequity aversion fairness equilibrium bounded rationality social preferences Variability in reasoning ability and persona configurations agent behavior simulation outcomes Persona settings act as a proxy that embeds social preferences and bounded rationality into the agents; without them the agents' improved reasoning drives them to the subgame-perfect solution instead of to human-like fairness behaviour Randomly generated persona attributes in earlier studies injected noise into agent behaviour, which masked the effect of reasoning ability and prevented it from being evaluated systematically Skew in how the agents' offers were distributed across offer values drives the fitted acceptance-rate lines to impossible values above 1.0 or below 0, producing the anomalies seen in the responder-side figures Platonic Representation Hypothesis temperature parameter GPT model acceptance rate Anderson-Darling test Kolmogorov-Smirnov test p-value regression piecewise linear regression confidence interval simulation

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relation to other workclaim_relationshow this paper stands to another: agrees, contradicts, extends, qualifies
claim typeclaim_typethe shape of the claim: existence, mechanism, method, critique, prediction, null
concept edgeconcept_edgesan asserted relation between two concepts, with the sentence asserting it
conceptconceptsan idea the paper works with, quoted where the paper uses it
content tagcontent_tagsa finding, definition, quantity, statistic or variable named in the paper
controlcontrolsan experiment run to eliminate a rival explanation
limitationlimitationswhat the paper says its evidence cannot support
mechanismmechanismshow the reported effect is said to come about
methodmethodhow the paper produced its evidence
phenomenonphenomenonthe effect or regularity under study
sensesensesa distinct use of a term, quoted where the paper fixes it
transfertransfersa method or concept carried in from another field
unrecordable relationunhoused_relationsa relation the paper asserts that this vocabulary cannot express, with the reason

The paper

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Every tag on this page

claim_relations 3 · claim_type 1 · concept_edges 10 · concepts 121 · content_tags 28 · controls 1 · limitations 7 · mechanisms 3 · method 1 · phenomenon 1 · senses 23 · transfers 1 · unhoused_relations 8

Relations on this page carry a quotation for the claim. Where a relation was separately evidenced — and so tested against the vocabulary for its type — the row says so; 8 of 10 relations here carry that mark. The rest are the tagger’s reading of the sentence quoted beside them.

facettagfieldconf.whose words
claim_typenull-result
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phenomenonLLM generative agents in the ultimatum game
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methodsimulation
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methodthe paper's words
mechanismsmechanisms
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mechanismsmechanisms
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mechanismsmechanisms
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mechanisms[2]the tagger's words
limitationsExcluding persona settings in order to isolate reasoning ability is itself a limitation, since personas are known to shape human-like behaviour.
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what this limits: the transfer to agents with personas: WHILE OUR METHODOLOGICAL CHOICE CLARIFIES THE ROLE OF REASONING ABILITY, IT ALSO REPRESENTS A LIMITATION OF THIS STUDY - the excluded factor is known to matter
limitations[0]the tagger's words
limitationsThe sample size of 1000 decisions per condition was chosen pragmatically rather than by a formal power analysis.
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what this limits: the precision of every estimate: WHILE THIS NUMBER IS NOT DERIVED FROM A FORMAL POWER ANALYSIS, IT WAS SELECTED BASED ON TWO MAIN CONSIDERATIONS
limitations[1]the tagger's words
limitationsScope is restricted to decision-making and negotiation tasks; market-level and institutional applications are left to future work.
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what this limits: the scope of the conclusions: BROADER APPLICATIONS TO MARKET-LEVEL MODELING OR INSTITUTIONAL DESIGN REMAIN IMPORTANT DIRECTIONS FOR FUTURE WORK
limitations[2]the tagger's words
limitationsEarlier claims of success are attributed to the coarseness of the evaluation rather than to the agents: aggregate distributions and binary alignment, with no individual-level analysis.
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what this limits: published findings that generative agents reproduce human behaviour in economic games: this paper states those successes may stem from coarse-grained criteria — aggregate distributions or binary alignment — without individual-level or reasoning-process analysis
limitations[3]1.0the tagger's words
limitationsThe expectation the title asks about is not borne out: greater reasoning ability does not directly produce reproduction of the experiments.
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what this limits: the assumption that a more capable model will simulate an economic experiment better: this paper reports zero-shot prompting with an older model aligning closely with human behaviour, and states improving reasoning ability cannot lead directly to reproduction
limitations[4]1.0the tagger's words
limitationsThe paper's own explanation of why earlier work succeeded is a claim about MEASUREMENT RESOLUTION, not about behaviour: prior studies assessed aggregate distributions or binary alignment 'without deeper analysis of underlying reasoning processes or individual-level deviations'. On that account the earlier results are not wrong, they are coarse - which is a harder thing to test.
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limitations[5]0.9the tagger's words
limitationsAgent outputs are reported as highly sensitive to design choices - persona, prompt formulation, role instructions - so any single configuration's result is conditional on choices the paper had to make and could not vary at once.
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limitations[6]0.85the tagger's words
transfersPrompt-engineering techniques for eliciting language-model reasoning are imported into the agent-based-modelling tradition of experimental economics, with prompts standing in for experimental instructions to human subjects.
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transfers[0]the tagger's words
controlsthe in-context examples RE-DRAWN AT RANDOM from the raw data and the whole simulation re-run several times, alongside runs at 15 and 20 examples
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eliminates: the particular examples chosen for the prompt as the source of the result - an LLM's behaviour is conditioned on its examples, so a finding from one draw is a fact about that draw · outcome (stated by the tagger, no quotation): held
controls[0]0.9the tagger's words
conceptsgenerative agent
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conceptseconomic experiments
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conceptsbehavioral realism
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conceptsreasoning ability
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conceptstheoretical solution
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conceptstraditional economic experiments
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conceptsGenerative AI
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conceptsgenerative models
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conceptsprompt engineering
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concepts[98]0.95the paper's words
conceptsMonte Carlo methods
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concepts[99]0.95the paper's words
conceptsexperimental instructions
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concepts[100]0.95the paper's words
conceptshuman-subject experiments
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concepts[101]0.95the paper's words
conceptsAnderson-Darling (AD)
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concepts[102]0.95the paper's words
conceptsKolmogorov-Smirnov (KS)
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concepts[103]0.95the paper's words
conceptsoverall distributional differences
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concepts[104]0.95the paper's words
conceptsmaximum cumulative distribution differences
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concepts[105]0.95the paper's words
conceptssocial context
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concepts[106]0.95the paper's words
conceptsstand-ins for real human groups
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concepts[107]0.95the paper's words
conceptstreating LLMs as direct substitutes for human subjects
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concepts[108]0.95the paper's words
conceptsflawed approach
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concepts[109]0.95the paper's words
conceptsmethodological scaffolding
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concepts[110]0.95the paper's words
conceptsPersona settings
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concepts[111]0.95the paper's words
conceptscontextual variables
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concepts[112]0.95the paper's words
conceptshuman decision-making
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concepts[113]0.95the paper's words
conceptsinequity aversion
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concepts[114]0.95the paper's words
conceptsfairness equilibrium
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concepts[115]0.95the paper's words
conceptsbounded rationality
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concepts[116]0.95the paper's words
conceptssocial preferences
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concepts[117]0.95the paper's words
conceptsVariability in reasoning ability and persona configurations
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concepts[118]0.95the paper's words
conceptsagent behavior
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concepts[119]0.95the paper's words
conceptssimulation outcomes
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concepts[120]0.95the paper's words
unhoused_relationsLLM agents → traditional economic experiments
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LLM agents are proposed to augment rather than replace traditional economic experiments. Augments-but-does-not-replace is absent from the eight types; distinct-from would lose the methodological complement relation.
unhoused_relations[0]0.95the tagger's words
unhoused_relationsground-truth benchmarks → LLM-based simulations
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Human data remain indispensable benchmarks for calibrating LLM simulations and identifying their boundary conditions. Benchmark-for/calibrates is absent from the eight types and is not mere operationalisation.
unhoused_relations[1]0.95the tagger's words
unhoused_relationsoutputs of generative agents → simulation design choices
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Generative-agent outputs are highly sensitive to simulation design choices. Depends-on/sensitive-to is absent; causation would overstate a design dependence with multiple interacting parameters.
unhoused_relations[2]0.95the tagger's words
unhoused_relationsfoundation models → downstream tasks
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Foundation models are adaptable to downstream tasks without retraining. Adaptable-to is absent from the eight types; neither kindhood nor operationalisation captures transfer to a task.
unhoused_relations[3]0.95the tagger's words
unhoused_relationsprompt engineering → experimental instructions
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Prompt engineering serves as an analog to instructions given to human participants. Analogous-to is absent; same-as would erase the distinction between machine prompts and human-study instructions.
unhoused_relations[4]0.95the tagger's words
unhoused_relationssocial context → stand-ins for real human groups
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Prompts incorporating social context are a necessary condition for advanced LLMs to serve as human-like stand-ins. Prerequisite-for is absent from the eight relation types.
unhoused_relations[5]0.95the tagger's words
unhoused_relationsPersona settings → social preferences
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Persona inclusion may act as a proxy for embedding social preferences in the simulation. Proxy-for is absent; operationalised-as would claim a measurement definition the paper does not make.
unhoused_relations[6]0.95the tagger's words
unhoused_relationsPersona settings → bounded rationality
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Persona inclusion may proxy bounded rationality in LLM-driven simulations. Proxy-for is absent from the eight types and the paper presents this as a hypothesis.
unhoused_relations[7]0.95the tagger's words
sensesreplication of human behaviour - AT AGGREGATE GRAIN: distributions of choices or binary alignment with typical outcomes, which is the criterion under which earlier work succeeded
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senses[0]1.0the tagger's words
sensesreplication of human behaviour - AT INDIVIDUAL GRAIN: underlying reasoning processes and individual-level deviations, which the earlier evaluations did not examine
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senses[1]1.0the tagger's words
sensesagent quality - split into two properties that trade off against each other: behavioral realism and rationality
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senses[2]1.0the tagger's words
sensessuccessful replication - in the prior literature a claim about AGGREGATE DISTRIBUTIONS or binary alignment with typical outcomes; this paper attributes those successes to the coarseness of the criterion rather than to the agents
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senses[3]0.95the tagger's words
sensesOne-shot ultimatum game - a demanding test that removes learning and reputation and supplies a unique equilibrium benchmark
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senses[4]0.95the tagger's words
sensesHuman low-offer rejection - payoff sacrifice explained as resistance to perceived unfairness
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senses[5]0.95the tagger's words
sensesGenerative AI - distribution-learning synthesis rather than classification within existing boundaries
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senses[6]0.95the tagger's words
sensesGPT-4 as behavioral model - a statistical knowledge base reflecting norms, heuristics, and communication conventions
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senses[7]0.95the tagger's words
sensesHallucination - plausible but wrong output arising from association-based rather than grounded modeling
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senses[8]0.95the tagger's words
sensesFew-shot prompting - annotated examples guiding output style and logic
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senses[9]0.95the tagger's words
sensesChain-of-thought prompting - explicit instruction to articulate intermediate reasoning steps
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senses[10]0.95the tagger's words
sensesReasoning ability - observable coherent multi-step response structure, not a claim about mental models or logical soundness
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senses[11]0.95the tagger's words
sensesLLMs as empirical tools - outputs analyzed, compared, and validated like participant data under safeguards
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senses[12]0.95the tagger's words
sensesReplication success under coarse criteria - aggregate distributions or binary typical-outcome alignment without individual reasoning
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senses[13]0.95the tagger's words
sensesLatent utility hypothesis - agents may optimize perceived social harmony rather than material self-interest
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senses[14]0.9the tagger's words
sensesIsolated reasoning manipulation - vary zero-shot, few-shot, and chain-of-thought while holding persona, framing, and temperature outside the focal contrast
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senses[15]0.95the tagger's words
sensesCore validity question - convergence toward normative rationality versus divergence from observed human behavior
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senses[16]0.95the tagger's words
sensesSimulation sample - 1000 independent decisions balancing statistical resolution against chain-of-thought cost
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senses[17]0.95the tagger's words
sensesPrompt engineering - methodological analog of instructions delivered to human experimental participants
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senses[18]0.95the tagger's words
sensesAD versus KS - overall distributional difference versus maximum cumulative distribution difference
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senses[19]0.95the tagger's words
sensesLLMs as human stand-ins - currently unwarranted without explicit social context and methodological scaffolding
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senses[20]0.95the tagger's words
sensesPersona settings - configured cultural, age, and gender context needed because current models do not inherently possess it
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senses[21]0.95the tagger's words
sensesPersona as behavioral proxy - possible embedding of social preferences and bounded rationality
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senses[22]0.9the tagger's words
claim_relationsextends → Kitadai et al. (2023) LLM-driven ultimatum-game simulation
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claim_relations[0]the tagger's words
claim_relationscontradicts → Horton (2023) and Aher et al. (2023) reports of successful behavioural replication by LLM agents
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claim_relations[1]the tagger's words
claim_relationsbuilds-on → prior LLM-agent studies that confounded reasoning with persona, payoff framing and temperature
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claim_relations[2]the tagger's words
concept_edgesreasoning ability —causes→ theoretical solution
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differs in: WHICH TARGET THE AGENTS CONVERGE ON, and the two targets pull apart: raising reasoning ability moves the simulation TOWARDS the game-theoretic solution and AWAY from the human experimental result. Better reasoning makes the agent a worse model of people - the paper's title asks the question and its abstract answers no · strength of the relation: 0.9 Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but higher reasoning moved results closer to the theoretical solution; causes is loose.
concept_edges[0]0.95the tagger's words
concept_edgesreasoning ability —contrasted-with→ behavioral realism
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strength of the relation: 0.8 · note: The record already carries a sense saying agent quality splits into two properties that TRADE OFF. This edge is that trade-off as the paper measured it. Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but higher reasoning moved results away from real experimental results; contrasted-with 'behavioral realism' is loose.
concept_edges[2]0.9the tagger's words
concept_edgesGenerative AI —distinct-from→ discriminative models
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the relation itself is separately evidenced · strength of the relation: 1 · note: The paper explicitly contrasts distribution-learning generation with classification and prediction.
concept_edges[5]0.95the tagger's words
concept_edgesopaque biases —causes→ harmful stereotypes
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the relation itself is separately evidenced · strength of the relation: 0.7 · note: The paper says biases can perpetuate or amplify harmful stereotypes.
concept_edges[6]0.95the tagger's words
concept_edgesscaffolding methods —distinct-from→ general mitigation techniques
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the relation itself is separately evidenced · strength of the relation: 1 · note: The authors explicitly adopt the strategies not as general mitigation but as reasoning scaffolds.
concept_edges[7]0.95the tagger's words
concept_edgesprompting strategies —causes→ coherence, inference depth, and contextual relevance
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the relation itself is separately evidenced · strength of the relation: 0.9 · note: Prior work is reported to show these strategies enhance all three output properties.
concept_edges[8]0.95the tagger's words
concept_edgesperceived social harmony —contrasted-with→ material self-interest
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the relation itself is separately evidenced · strength of the relation: 0.7 · note: The proposed implicit objective is social harmony rather than material self-interest. Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the claim is what Mei et al. (2024) 'proposed'; attributed.
concept_edges[9]0.95the tagger's words
concept_edgestreating LLMs as direct substitutes for human subjects —is-a→ flawed approach
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the relation itself is separately evidenced · strength of the relation: 1 · note: The paper directly classifies treating LLMs as substitutes as a flawed approach.
concept_edges[10]0.95the tagger's words
concept_edgescontextual variables —causes→ human decision-making
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the relation itself is separately evidenced · strength of the relation: 1 · note: Culture, age, and gender are said to significantly influence human decision-making.
concept_edges[11]0.95the tagger's words
concept_edgesVariability in reasoning ability and persona configurations —causes→ agent behavior
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the relation itself is separately evidenced · strength of the relation: 0.7 · note: The paper states this variability can significantly influence agent behavior.
concept_edges[12]0.95the tagger's words
content_tagsAnderson-Darling test
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content_tags.statistics[0]the tagger's words
content_tagsKolmogorov-Smirnov test
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content_tags.statistics[1]the tagger's words
content_tagsp-value
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also in: p-value
content_tags.statistics[2]the tagger's words
content_tagsregression
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also in: regression
content_tags.statistics[3]the tagger's words
content_tagsconfidence interval
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content_tags.statistics[4]the tagger's words
content_tagsRaising the reasoning ability of LLM-driven agents pushed their ultimatum-game behaviour toward the game-theoretic solution and away from the real human experimental data.
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content_tags.findings[0]the tagger's words
content_tagsNo prompting condition produced a distribution that statistically matched either the theoretical solution or the human experimental data.
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content_tags.findings[1]the tagger's words
content_tagsOn the proposer side the older, weaker-reasoning model with zero-shot prompting was also relatively close to human behaviour, showing that better reasoning does not straightforwardly buy better reproduction of economic experiments.
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content_tags.findings[2]the tagger's words
content_tagsOn the responder side, chain-of-thought prompting produced the closest match to the theoretical (rational) distribution of any condition.
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content_tags.findings[3]the tagger's words
content_tagsAgainst human responder data the ranking inverted: the weakest-reasoning condition, zero-shot gpt-3.5, matched human acceptance behaviour most closely.
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content_tags.findings[4]the tagger's words
content_tagsThe authors conclude that LLM agents do not spontaneously behave like humans in economic games without prompts carrying social context, so using LLM-driven multi-agent simulations as substitutes for real human groups is currently unwarranted.
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content_tags.findings[5]the tagger's words
content_tagsAt every temperature setting almost all agents offered a 50-50 split of coins.
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content_tags.findings[6]the tagger's words
content_tagsBecause every other setting was identical to the earlier study, the authors attribute the difference in results to the presence or absence of persona settings, and conclude that personas are essential for applying LLM-driven simulation to economic experiments.
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content_tags.findings[7]the tagger's words
content_tagsRobustness checks with 15 and 20 in-prompt examples changed the synthesised distributions only slightly and left the comparative pattern across conditions unchanged.
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content_tags.findings[8]the tagger's words
content_tagsreasoning ability
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content_tags.definitions[0]the tagger's words
content_tagsgenerative agent
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content_tags.definitions[1]the tagger's words
content_tagsPlatonic Representation Hypothesis
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content_tags.definitions[2]the tagger's words
content_tagsreasoning-related prompting strategy
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content_tags.variables[0]the tagger's words
content_tagstemperature parameter
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content_tags.variables[1]the tagger's words
content_tagsGPT model
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also in: GPT model
content_tags.variables[2]the tagger's words
content_tagsacceptance rate
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content_tags.variables[3]the tagger's words
content_tagspersona settings
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content_tags.variables[4]the tagger's words
content_tags1000 — independent agent decisions generated for every combination of role, prompt type and temperature
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content_tags.quantities[0]the tagger's words
content_tags10 — real experimental data points used as in-prompt examples for few-shot and chain-of-thought prompting
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content_tags.quantities[1]the tagger's words
content_tagsKS = 0.479 and AD = 128 — smallest divergence from the human proposer distribution, obtained by the chain-of-thought condition
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content_tags.quantities[2]the tagger's words
content_tagsAD = 15.5 — chain-of-thought responder distribution's divergence from the theoretical distribution, the smallest of any condition
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content_tags.quantities[3]the tagger's words
content_tagsAD = 20.8 ... AD = 43.7 — divergence from human responder data for few-shot (third best) and for chain-of-thought (worst of the four conditions)
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content_tags.quantities[4]the tagger's words
content_tagsapproximately 0.7 / approximately 0.4 or less — human acceptance rate at an offer of 50 versus the simulated agents' acceptance rate at the same offer
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content_tags.quantities[5]the tagger's words

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© 2026 Shir Sivroni · ORCID 0009-0000-2597-2824