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

Ayato Kitadai, Sinndy Dayana Rico Lugo, Yudai Tsurusaki, Yusuke Fukasawa, Nariaki Nishino · 2025 · experimental-economics · doi:10.1007/s10726-025-09946-9 · full text at the source · TEI xml · tagged edition · index · concepts · authors
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.
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, whether or not the words occur in the text below — a number is how many papers in the corpus carry that tag; a tag with no number is unique to this paper
generative agent economic experiments behavioral realism2 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 conditions2 outputs of generative agents simulation design choices Generative AI6 generative models2 discriminative models underlying data distribution recognition to generation methodological transformation LLMs5 key subclass Transformer architecture statistical knowledge base social norms2 strategic heuristics foundation models2 general-purpose substrates downstream tasks retraining hallucinations learned statistical associations grounded world models black boxes interpretability9 prompt design few-shot prompting2 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 variables2 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 engineering2 Monte Carlo methods experimental instructions human-subject experiments Anderson-Darling (AD) Kolmogorov-Smirnov (KS) overall distributional differences maximum cumulative distribution differences social context3 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 rationality3 social preferences Variability in reasoning ability and persona configurations agent behavior simulation outcomes simulation51

Our tag layer for this paper

Cross-disciplinary analysis
content-tags & facets: phenomenon · claim-type · cross-domain-transfer · mechanism · claim-relation · replication-status · limitation — grounded in the full text
phenomenonLLM generative agents in the ultimatum game— “A multi-agent simulation, designed to improve the reasoning ability of generative agents through prompting methods, was developed to reproduce the result of an actual economic experiment on the ultimatum game.”
claim-typenull-result— “Therefore, by improving the reasoning ability of generative agents, the result of LLMs-driven MAS does not align with the actual experimental result; rather, it closely aligns with the theoretical solution.”
method — evidence— “The simulation strategy involved generating 1000 independent decisions for each unique combination of role (proposer/responder), prompt type, and temperature.”
transferfrom natural language processing / machine learning (prompt engineering): Prompt-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.— “To situate our approach within existing methodological traditions, we note that our use of LLM-based generative agents as substitutes for human participants builds on the lineage of agent-based modeling in experimental economics, while integrating recent advances in natural language reasoning through prompt engineering.”transfers[0]
mechanismPersona 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— “This connection is further supported by our earlier observation that persona settings influenced the degree of deviation from human behavior: their inclusion may act as a proxy for embedding social preferences and bounded rationality into in LLM-driven MAS.”mechanisms[0]
mechanismRandomly 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— “The randomly generated persona attributes in previous studies likely acted as noise, limiting the systematic evaluation of reasoning-related outcomes.”mechanisms[1]
mechanismSkew 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— “This skewness accounts for the anomalies observed in the regression lines of Fig. 4, where acceptance rates exhibited extreme values, either exceeding 1.0 or falling below 0.”mechanisms[2]
extends Kitadai et al. (2023) LLM-driven ultimatum-game simulation— “The simulation framework in this study was constructed as an extension of the prompting structure introduced in Kitadai et al. (2023), incorporating methodological enhancements to examine the impact of reasoning ability in LLMs.”claim_relations[0]
contradicts Horton (2023) and Aher et al. (2023) reports of successful behavioural replication by LLM agents— “However, these successes may stem from coarse-grained evaluation criteria: both studies assessed behavior at the level of aggregate distributions or binary alignment with typical outcomes, without deeper analysis of underlying reasoning processes or individual-level deviations.”claim_relations[1]
builds-on prior LLM-agent studies that confounded reasoning with persona, payoff framing and temperature— “Our study addresses this gap by varying only the reasoning-related prompting strategy (zero-shot, few-shot, and chain-of-thought) while holding other factors constant.”claim_relations[2]
sensereplication of human behaviour - AT AGGREGATE GRAIN: distributions of choices or binary alignment with typical outcomes, which is the criterion under which earlier work succeeded— “both studies assessed behavior at the level of aggregate distributions or binary alignment with typical outcomes”senses[0]
sensereplication of human behaviour - AT INDIVIDUAL GRAIN: underlying reasoning processes and individual-level deviations, which the earlier evaluations did not examine— “without deeper analysis of underlying reasoning processes or individual-level deviations.”senses[1]
senseagent quality - split into two properties that trade off against each other: behavioral realism and rationality— “the results highlighted distinct trade-offs between behavioral realism and rationality”senses[2]
sensesuccessful 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— “However, these successes may stem from coarse-grained evaluation criteria: both studies assessed behavior at the level of aggregate distributions or binary alignment with typical outcomes, without deeper analysis of underlying reasoning processes or individual-level deviations”senses[3]
senseOne-shot ultimatum game - a demanding test that removes learning and reputation and supplies a unique equilibrium benchmark— “We focus on the one-shot ultimatum game for three main reasons. First, it eliminates history-dependent learning and reputation effects, leaving a unique subgame-perfect equilibrium that provides a clean theoretical benchmark. Second, recent cross-task evaluations by Mei et al. (2024), who compared large-language-model agents with human…senses[4]
senseHuman low-offer rejection - payoff sacrifice explained as resistance to perceived unfairness— “Although s2 = accept is the weakly dominant strategy of player 2, in economic experiments it is common to observe decisions of s2 = reject when the value of s1 is low (Güth et al. 1982). A commonly proposed explanation for this phenomenon is that responders may choose to sacrifice their payoffs to resist perceived unfairness, particularly…senses[5]
senseGenerative AI - distribution-learning synthesis rather than classification within existing boundaries— “Generative AI refers to a class of models that autonomously produce new, plausible data samples across various modalities—from text and images to audio and beyond. Unlike discriminative models that classify or predict within existing data boundaries, generative models learn the underlying data distribution itself, enabling them to synthesize…senses[6]
senseGPT-4 as behavioral model - a statistical knowledge base reflecting norms, heuristics, and communication conventions— “From a behavioral modeling standpoint, gpt-4 can be interpreted as a statistical knowledge base constructed from human language and interaction patterns. Its training corpus contains linguistic patterns that reflect social norms, strategic heuristics, and communicative conventions across cultural contexts (Lorè and Heydari 2024). This enables…senses[7]
senseHallucination - plausible but wrong output arising from association-based rather than grounded modeling— “Nevertheless, several limitations must be addressed when deploying LLMs as behavioral agents. First, hallucinations, plausible but incorrect outputs, can emerge, especially under poorly constrained prompts (Bubeck et al. 2023). These are a byproduct of the model’s reliance on learned statistical associations rather than grounded world models.…senses[8]
senseFew-shot prompting - annotated examples guiding output style and logic— “While these limitations remain open challenges, recent work has shown that prompt design can shape the inferential structure of LLM outputs. We therefore adopt two prompting strategies—(i) few-shot prompting, where the model is shown a small number of annotated examples to guide output style and logic (Brown et al. 2020), and (ii)…senses[9]
senseChain-of-thought prompting - explicit instruction to articulate intermediate reasoning stepssenses[10]
senseReasoning ability - observable coherent multi-step response structure, not a claim about mental models or logical soundness
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senses[11]
senseLLMs as empirical tools - outputs analyzed, compared, and validated like participant data under safeguards
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senses[12]
senseReplication success under coarse criteria - aggregate distributions or binary typical-outcome alignment without individual reasoning
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senses[13]
senseLatent utility hypothesis - agents may optimize perceived social harmony rather than material self-interest
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senses[14]
senseIsolated 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]
senseCore validity question - convergence toward normative rationality versus divergence from observed human behavior
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senses[16]
senseSimulation sample - 1000 independent decisions balancing statistical resolution against chain-of-thought cost
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senses[17]
sensePrompt engineering - methodological analog of instructions delivered to human experimental participants
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senses[18]
senseAD versus KS - overall distributional difference versus maximum cumulative distribution difference
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senses[19]
senseLLMs as human stand-ins - currently unwarranted without explicit social context and methodological scaffolding
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senses[20]
sensePersona settings - configured cultural, age, and gender context needed because current models do not inherently possess it
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senses[21]
sensePersona as behavioral proxy - possible embedding of social preferences and bounded rationality
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senses[22]
controlthe 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 — held — rules out 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
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controls[0]
auditreasoning ability —causes→ theoretical solution — 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.
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concept_edges[0]
differs inreasoning ability —causes→ theoretical solution — 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
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concept_edges[0]
weightreasoning ability —causes→ theoretical solution — 0.9
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concept_edges[0]
auditreasoning ability —contrasted-with→ behavioral realism — 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.
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concept_edges[2]
notereasoning ability —contrasted-with→ behavioral realism — 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.
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concept_edges[2]
weightreasoning ability —contrasted-with→ behavioral realism — 0.8
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concept_edges[2]
noteGenerative AI —distinct-from→ discriminative models — The paper explicitly contrasts distribution-learning generation with classification and prediction.
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concept_edges[5]
noteopaque biases —causes→ harmful stereotypes — The paper says biases can perpetuate or amplify harmful stereotypes.
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weightopaque biases —causes→ harmful stereotypes — 0.7
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concept_edges[6]
notescaffolding methods —distinct-from→ general mitigation techniques — The authors explicitly adopt the strategies not as general mitigation but as reasoning scaffolds.
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noteprompting strategies —causes→ coherence, inference depth, and contextual relevance — Prior work is reported to show these strategies enhance all three output properties.
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concept_edges[8]
weightprompting strategies —causes→ coherence, inference depth, and contextual relevance — 0.9
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concept_edges[8]
auditperceived social harmony —contrasted-with→ 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.
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concept_edges[9]
noteperceived social harmony —contrasted-with→ material self-interest — The proposed implicit objective is social harmony rather than material self-interest.
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concept_edges[9]
weightperceived social harmony —contrasted-with→ material self-interest — 0.7
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concept_edges[9]
notetreating LLMs as direct substitutes for human subjects —is-a→ flawed approach — The paper directly classifies treating LLMs as substitutes as a flawed approach.
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concept_edges[10]
notecontextual variables —causes→ human decision-making — Culture, age, and gender are said to significantly influence human decision-making.
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concept_edges[11]
noteVariability in reasoning ability and persona configurations —causes→ agent behavior — The paper states this variability can significantly influence agent behavior.
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concept_edges[12]
weightVariability in reasoning ability and persona configurations —causes→ agent behavior — 0.7
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concept_edges[12]
outcome (stated by the tagger, no quotation)control — heldcontrols[0]
for which uselimitation — 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
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limitations[0]
for which uselimitation — the precision of every estimate: WHILE THIS NUMBER IS NOT DERIVED FROM A FORMAL POWER ANALYSIS, IT WAS SELECTED BASED ON TWO MAIN CONSIDERATIONS
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limitations[1]
for which uselimitation — the scope of the conclusions: BROADER APPLICATIONS TO MARKET-LEVEL MODELING OR INSTITUTIONAL DESIGN REMAIN IMPORTANT DIRECTIONS FOR FUTURE WORK
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limitations[2]
for which uselimitation — 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
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limitations[3]
for which uselimitation — 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
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limitations[4]
weightLLM agents → traditional economic experiments — 0.7
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unhoused_relations[0]
weightPersona settings → social preferences — 0.6
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unhoused_relations[6]
weightPersona settings → bounded rationality — 0.6
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unhoused_relations[7]
replicationuntested
no type for thisLLM agents → traditional economic experiments — 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.
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unhoused_relations[0]
no type for thisground-truth benchmarks → LLM-based simulations — 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.
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unhoused_relations[1]
no type for thisoutputs of generative agents → simulation design choices — 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.
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unhoused_relations[2]
no type for thisfoundation models → downstream tasks — 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.
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unhoused_relations[3]
no type for thisprompt engineering → experimental instructions — 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.
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unhoused_relations[4]
no type for thissocial context → stand-ins for real human groups — 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.
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unhoused_relations[5]
no type for thisPersona settings → social preferences — 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.
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unhoused_relations[6]
no type for thisPersona settings → bounded rationality — 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.
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unhoused_relations[7]
limitationExcluding 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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limitations[0]
limitationThe sample size of 1000 decisions per condition was chosen pragmatically rather than by a formal power analysis.
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limitations[1]
limitationScope is restricted to decision-making and negotiation tasks; market-level and institutional applications are left to future work.
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limitations[2]
limitationEarlier 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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limitations[3]
limitationThe expectation the title asks about is not borne out: greater reasoning ability does not directly produce reproduction of the experiments.
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limitations[4]
limitationThe 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]
limitationAgent 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]
Content tags
content-observations: finding · quantity · variable · definition · key-term — grounded in the full text
findingRaising 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]
findingNo 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]
findingOn 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]
findingOn 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]
findingAgainst 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]
findingThe 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]
findingAt every temperature setting almost all agents offered a 50-50 split of coins.
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content_tags.findings[6]
findingBecause 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]
findingRobustness 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]
quantity1000 — independent agent decisions generated for every combination of role, prompt type and temperature
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content_tags.quantities[0]
quantity10 — 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]
quantityKS = 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]
quantityAD = 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]
quantityAD = 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]
quantityapproximately 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]
variablereasoning-related prompting strategy (independent)
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content_tags.variables[0]
variabletemperature parameter (independent)
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content_tags.variables[1]
variableGPT model (independent)
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content_tags.variables[2]
variableacceptance rate (dependent)
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content_tags.variables[3]
variablepersona settings (control)
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content_tags.variables[4]
definitionreasoning ability
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content_tags.definitions[0]
definitiongenerative agent
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content_tags.definitions[1]
definitionPlatonic Representation Hypothesis
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content_tags.definitions[2]
Concepts
terms this paper carries, each with the sentence it was cut from — the layer that joins one paper to another. Terms with no quote yet are on CONCEPTS.html.
conceptgenerative agent
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concepts[0]
concepteconomic experiments
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concepts[1]
conceptbehavioral realism
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concepts[2]
conceptreasoning ability
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concepts[3]
concepttheoretical solution
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concepts[4]
conceptpersona
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concepts[5]
conceptone-shot ultimatum game — not defined in this paper's own words; single-round bargaining design chosen to remove learning and reputation while preserving a demanding human-AI gap
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concepts[6]
concepthistory-dependent learning — not defined in this paper's own words; cross-round adaptation eliminated by the single-round design
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concepts[7]
conceptreputation effects — not defined in this paper's own words; cross-round social consequence eliminated by the single-round design
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concepts[8]
conceptsubgame-perfect equilibrium — not defined in this paper's own words; unique game-theoretic solution used as a clean benchmark
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concepts[9]
concepttheoretical benchmark — not defined in this paper's own words; equilibrium reference against which human-like bargaining is compared
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concepts[10]
concepthuman-AI performance gap — not defined in this paper's own words; performance discrepancy reported as largest for the selected game
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concepts[11]
conceptstringent test bed — not defined in this paper's own words; interpretable and demanding environment for testing enhanced reasoning
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concepts[12]
conceptartificial correlations — not defined in this paper's own words; cross-round dependence that chain-of-thought leakage could induce
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concepts[13]
conceptweakly dominant strategy — not defined in this paper's own words; acceptance strategy that maximizes payoff weakly under the formal game
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concepts[14]
conceptperceived unfairness — not defined in this paper's own words; social interpretation motivating costly rejection of low offers
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concepts[15]
conceptLLM agents — not defined in this paper's own words; language-model-driven agents proposed as an augmenting experimental tool
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concepts[16]
concepttraditional economic experiments — not defined in this paper's own words; human-subject economic studies the agents are intended to augment rather than replace
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concepts[17]
conceptadditional methodological tool — not defined in this paper's own words; supplementary role proposed for LLM agents
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concepts[18]
conceptopaque biases — not defined in this paper's own words; training- and development-rooted distortions in behavioral output
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concepts[19]
conceptharmful stereotypes — not defined in this paper's own words; social harms that model bias can perpetuate or amplify
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concepts[20]
conceptstructural inequities — not defined in this paper's own words; systemic harms that model bias can perpetuate or amplify
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concepts[21]
conceptground-truth benchmarks — not defined in this paper's own words; real human data used to calibrate and bound LLM simulations
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concepts[22]
conceptLLM-based simulations — not defined in this paper's own words; agent simulations calibrated against human population behavior
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concepts[23]
conceptboundary conditions — not defined in this paper's own words; conditions separating faithful approximation from systematic deviation
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concepts[24]
conceptoutputs of generative agents — not defined in this paper's own words; simulated decisions sensitive to persona, prompt, and role design
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concepts[25]
conceptsimulation design choices — not defined in this paper's own words; persona, prompt, and role settings on which outputs depend
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concepts[26]
conceptGenerative AI — not defined in this paper's own words; model class producing plausible new samples across modalities
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concepts[27]
conceptgenerative models — not defined in this paper's own words; models learning a data distribution to synthesize new outputs
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concepts[28]
conceptdiscriminative models — not defined in this paper's own words; models classifying or predicting within existing data boundaries
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concepts[29]
conceptunderlying data distribution — not defined in this paper's own words; distribution learned by generative rather than discriminative models
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concepts[30]
conceptrecognition to generation — not defined in this paper's own words; paradigm shift the authors connect to social modeling
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concepts[31]
conceptmethodological transformation — not defined in this paper's own words; change in the modeling of social phenomena enabled by generation
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concepts[32]
conceptLLMs — not defined in this paper's own words; GPT-family subclass of generative AI built on Transformers
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concepts[33]
conceptkey subclass — not defined in this paper's own words; taxonomic status assigned to LLMs within generative AI
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concepts[34]
conceptTransformer architecture — not defined in this paper's own words; neural architecture underlying the GPT-family models
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concepts[35]
conceptstatistical knowledge base — not defined in this paper's own words; behavioral interpretation of GPT-4 as patterns learned from language and interaction
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concepts[36]
conceptsocial norms — not defined in this paper's own words; human interaction regularities reflected in the training corpus
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concepts[37]
conceptstrategic heuristics — not defined in this paper's own words; decision patterns reflected in the training corpus
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concepts[38]
conceptfoundation models — not defined in this paper's own words; general-purpose model substrates adaptable across tasks
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concepts[39]
conceptgeneral-purpose substrates — not defined in this paper's own words; general-purpose characterization of foundation models
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concepts[40]
conceptdownstream tasks — not defined in this paper's own words; new uses reached through adaptation without retraining
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concepts[41]
conceptretraining — not defined in this paper's own words; model rebuilding said not to be required for downstream adaptation
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concepts[42]
concepthallucinations — not defined in this paper's own words; plausible but incorrect outputs under weak prompt constraints
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concepts[43]
conceptlearned statistical associations — not defined in this paper's own words; ungrounded association mechanism offered for hallucination
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concepts[44]
conceptgrounded world models — not defined in this paper's own words; world representations contrasted with learned statistical associations
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concepts[45]
conceptblack boxes — not defined in this paper's own words; interpretability limitation of behavioral LLM agents
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concepts[46]
conceptinterpretability — not defined in this paper's own words; access limitation: outputs are analyzable but internal reasoning is not
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concepts[47]
conceptprompt design — not defined in this paper's own words; control used to shape output inferential structure
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concepts[48]
conceptfew-shot prompting — not defined in this paper's own words; prompting with a small set of annotated demonstrations
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concepts[49]
conceptchain-of-thought (CoT) prompting — not defined in this paper's own words; prompting that elicits intermediate reasoning steps
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concepts[50]
conceptgeneral mitigation techniques — not defined in this paper's own words; broad correction role explicitly denied for the two strategies
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concepts[51]
conceptscaffolding methods — not defined in this paper's own words; role assigned to prompting strategies for structured decisions
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concepts[52]
conceptprompting strategies — not defined in this paper's own words; few-shot and chain-of-thought methods considered together
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concepts[53]
conceptreasoning quality — not defined in this paper's own words; quality of agent decision reasoning influenced by scaffolding
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concepts[54]
conceptcoherence, inference depth, and contextual relevance — not defined in this paper's own words; three output properties reported as enhanced by prompting strategies
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concepts[55]
conceptcontextually coherent, multi-step responses — not defined in this paper's own words; observable response form used in the paper's reasoning definition
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concepts[56]
conceptinferential structure — not defined in this paper's own words; pattern in generated responses rather than an asserted mental model
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concepts[57]
conceptobservable behavioral property — not defined in this paper's own words; behavioral rather than mentalistic status of reasoning ability
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concepts[58]
conceptexperimental variable — not defined in this paper's own words; status assigned to reasoning ability in the simulation
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concepts[59]
conceptpragmatist perspective — not defined in this paper's own words; stance evaluating useful behavioral approximation without claiming cognition
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concepts[60]
concepthuman cognition — not defined in this paper's own words; target the authors explicitly do not claim GPT-4 replicates
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concepts[61]
conceptbehavioral patterns — not defined in this paper's own words; economic-experiment regularities LLMs may approximate
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concepts[62]
conceptmethodological tools — not defined in this paper's own words; specific-context role justified by adequate behavioral fidelity
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concepts[63]
conceptwell-defined simulation contexts — not defined in this paper's own words; narrow settings in which methodological use may be justified
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concepts[64]
conceptempirical tools — not defined in this paper's own words; LLMs treated as output-producing entities subject to comparison and validation
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concepts[65]
conceptevaluation protocols — not defined in this paper's own words; safeguard required for interpretable behavioral simulations
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concepts[66]
conceptmethodological safeguards — not defined in this paper's own words; controls required alongside careful prompting and evaluation
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concepts[67]
conceptcoarse-grained evaluation criteria — not defined in this paper's own words; aggregate or binary evaluation that may explain earlier replication successes
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concepts[68]
conceptaggregate distributions — not defined in this paper's own words; population-level behavior grain used by prior evaluations
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concepts[69]
conceptindividual-level deviations — not defined in this paper's own words; fine-grained differences omitted by aggregate evaluations
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concepts[70]
conceptfiner-grained behavioral criteria — not defined in this paper's own words; stricter evaluation grain linked to failures of alignment
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concepts[71]
conceptgame-theoretic environments — not defined in this paper's own words; more complex strategic settings used in stricter evaluations
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concepts[72]
conceptfailures of alignment — not defined in this paper's own words; systematic failures highlighted under finer evaluation
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concepts[73]
conceptbehavioral Turing tests — not defined in this paper's own words; tests formalizing systematic social tendencies in LLM agents
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concepts[74]
conceptlatent utility function — not defined in this paper's own words; hypothesized implicit objective behind prosocial or cooperative behavior
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concepts[75]
conceptperceived social harmony — not defined in this paper's own words; hypothesized implicit optimization target
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concepts[76]
conceptmaterial self-interest — not defined in this paper's own words; game-theoretic motive contrasted with social harmony
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concepts[77]
conceptPersona design — not defined in this paper's own words; assignment of demographic or personality traits to agents
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concepts[78]
conceptbehavioral tendencies — not defined in this paper's own words; agent dispositions nudged by specified traits
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concepts[79]
conceptcausal effects — not defined in this paper's own words; effects obscured when several design parameters vary together
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concepts[80]
conceptreasoning-related prompting strategy — not defined in this paper's own words; zero-shot, few-shot, and chain-of-thought manipulation varied in isolation
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concepts[81]
conceptconfounding variables — not defined in this paper's own words; persona, framing, and temperature factors held outside the focal manipulation
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concepts[82]
conceptcentral research question — not defined in this paper's own words; whether better reasoning improves human alignment or amplifies rational deviation
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[83]
conceptnormative benchmarks of rationality — not defined in this paper's own words; game-theoretic rationality target toward which sophistication may converge
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[84]
conceptempirically grounded human behavior — not defined in this paper's own words; descriptive human target from which sophisticated agents may diverge
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[85]
conceptsimulation framework — not defined in this paper's own words; three-step prompting structure extending earlier work
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[86]
conceptthree sequential steps — not defined in this paper's own words; game explanation, decision instructions, and output specification
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[87]
conceptoutput format — not defined in this paper's own words; third stage constraining the agent response
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[88]
concept1000 independent decisions — not defined in this paper's own words; per-condition sample generated for each role, prompt type, and temperature
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[89]
conceptformal power analysis — not defined in this paper's own words; sample-size derivation explicitly not used
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[90]
conceptrobustness check — not defined in this paper's own words; repeat and expanded-example analyses supporting result stability
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[91]
conceptcomputational cost per generation — not defined in this paper's own words; chain-of-thought expense limiting feasible sample size
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[92]
conceptstatistical resolution — not defined in this paper's own words; precision side of the chosen sample-size trade-off
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[93]
conceptfeasibility in simulation-based research — not defined in this paper's own words; resource side of the sample-size trade-off
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[94]
conceptsubstitutes for human participants — not defined in this paper's own words; methodological role located in agent-based modeling lineage
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[95]
conceptagent-based modeling — not defined in this paper's own words; experimental-economics tradition extended by LLM agents
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[96]
conceptnatural language reasoning — not defined in this paper's own words; recent capability integrated through prompting
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[97]
conceptprompt engineering — not defined in this paper's own words; LLM instruction design treated as analogous to experimental instructions
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[98]
conceptMonte Carlo methods — not defined in this paper's own words; repeated-sampling tradition paralleled by the simulation
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[99]
conceptexperimental instructions — not defined in this paper's own words; human-study counterpart to prompt engineering
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[100]
concepthuman-subject experiments — not defined in this paper's own words; methodological consistency target for the prompt structure
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[101]
conceptAnderson-Darling (AD) — not defined in this paper's own words; distribution-comparison test used alongside KS
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[102]
conceptKolmogorov-Smirnov (KS) — not defined in this paper's own words; maximum cumulative-difference test used alongside AD
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[103]
conceptoverall distributional differences — not defined in this paper's own words; overall discrepancy evaluated by the two tests
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[104]
conceptmaximum cumulative distribution differences — not defined in this paper's own words; largest cumulative discrepancy evaluated by KS
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[105]
conceptsocial context — not defined in this paper's own words; prompt content required for advanced agents to approach human-like economic behavior
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[106]
conceptstand-ins for real human groups — not defined in this paper's own words; role the paper currently judges unwarranted
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[107]
concepttreating LLMs as direct substitutes for human subjects — not defined in this paper's own words; replacement of human subjects explicitly rejected
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[108]
conceptflawed approach — not defined in this paper's own words; paper's classification of direct LLM substitution
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[109]
conceptmethodological scaffolding — not defined in this paper's own words; deliberate structure required for effective economic modeling
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[110]
conceptPersona settings — not defined in this paper's own words; explicitly configured context shaping human-like simulated behavior
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[111]
conceptcontextual variables — not defined in this paper's own words; culture, age, and gender factors encoded through personas
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[112]
concepthuman decision-making — not defined in this paper's own words; behavior significantly influenced by contextual variables
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[113]
conceptinequity aversion — not defined in this paper's own words; behavioral-economics account of low-offer rejection
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[114]
conceptfairness equilibrium — not defined in this paper's own words; alternative fairness-based account of ultimatum behavior
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[115]
conceptbounded rationality — not defined in this paper's own words; account of human departures from subgame-perfect equilibrium
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[116]
conceptsocial preferences — not defined in this paper's own words; human motive persona inclusion may proxy in LLM simulations
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[117]
conceptVariability in reasoning ability and persona configurations — not defined in this paper's own words; combined design variability affecting agent behavior
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[118]
conceptagent behavior — not defined in this paper's own words; simulation output sensitive to reasoning and persona configurations
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[119]
conceptsimulation outcomes — not defined in this paper's own words; outputs whose realism, interpretability, and applicability require design care
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[120]
Statistics — cross-field join keys
field-invariant statistical terms, used in their statistical sense (gated on a number / symbol / test). A p-value or a standard deviation means the SAME thing in physics, biology, psychology and sociology — so these are the strongest cross-field join keys. Grounded verbatim.
Anderson-Darling test AD as written: Anderson-Darling (AD)
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
content_tags.statistics[0]
Kolmogorov-Smirnov test KS as written: Kolmogorov-Smirnov (KS)
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
content_tags.statistics[1]
p-value p
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
content_tags.statistics[2]
regression as written: piecewise linear regression
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
content_tags.statistics[3]
confidence interval
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
content_tags.statistics[4]

When we have changed our mind. Some entries in the table begin with a word in capitals and the reason for it. EVIDENCE CORRECTED — the finding still stands, but we had quoted the wrong sentence from the paper, and the quotation has been replaced with the right one. RETRACTED — the entry itself was wrong; it stays on the page, marked, with the reason, rather than being deleted. WITHDRAWN AND REPLACED — the entry was wrong, and a corrected version of it appears in the entries below. Each mark carries its own reason, in brackets, because the reasons differ from one entry to the next. An entry we have WITHDRAWN is removed from this page and kept in the record, with its reason. A line reading Audit and a date is a later re-reading of the entry against its whole sentence: DOUBTFUL means it was kept, and the reason says what is loose about it. Quotation marks inside the tagger's sentence. Where a tag is the tagger's own sentence, a phrase in quotation marks inside it is borrowed from the paper — the tagger's wording around it is not. The paper's own sentence is the evidence quote for that row; on this page it is withheld with the article, and the article is at its publisher. Nothing inside quotation marks anywhere on this page is ours: if it is quoted, it is the paper's. Whose words a concept carries. A concept entry records a term the paper itself uses, so the term, the sense given for it, and its evidence are the paper's own words, quoted where the paper fixes them. This is the opposite of a sense entry in the tag table, which is the tagger's sentence about how the paper uses a term. Where a concept entry could not be grounded in the paper's words, the entry says so on its own row.

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