Pith. sign in

REVIEW 4 major objections 5 minor 1 cited by

Twin-2K-500: A dataset for building digital twins of over 2,000 people based on their answers to over 500 questions

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper introduces a public dataset of 2,058 US adults who each answered 500+ questions, and reports that LLM-based digital twins predict individual holdout responses with 71.72% accuracy, reaching 87.67% of the human test-retest…

desk verdict A genuinely valuable public dataset for digital-twin research, but the headline accuracy claim overstates what is shown because no unpersonalized LLM baseline is run. read the letter →

arxiv 2505.17479 v1 pith:55LORQAB submitted 2025-05-23 cs.CY cs.AIcs.HCecon.EM

Olivier Toubia , George Z. Gui , Tianyi Peng , Daniel J. Merlau , Ang Li , Haozhe Chen This is my paper · ORCID
classification cs.CYcs.AIcs.HCecon.EM
keywords digitaltwinsLLMsimulationpersonatest-retestaccuracyrepresentativesamplebehavioraleconomicssurveydatasetbenchmark
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper introduces Twin-2K-500, a public dataset of 2,058 US participants who each answered over 500 questions across four survey waves, covering demographics, personality, cognitive ability, economic preferences, replications of classic behavioral-economics experiments, and a product-pricing study. The final wave repeats earlier questions to measure how consistently humans answer the same tasks, giving a test-retest accuracy baseline of 81.72%. Using the non-repeated answers to build LLM-based digital twins, the paper reports that the twins predict individual holdout responses with 71.72% accuracy, which is 87.67% of the human test-retest accuracy. At the group level, the twin simulations replicate most of the classic experimental effects, with clear failures in cases where humans deviate from normative or 'rational' behavior, such as outcome bias, omission bias, and the Allais paradox. The dataset is offered as a public benchmark for developing and validating persona simulations and for social-science research more broadly.

What carries the argument

The load-bearing structure is the dataset's three-way split of each participant's answers: a persona record (all non-holdout responses from waves 1-3, formatted as text or JSON), an evaluation answer block (the wave 1-3 answers to the 88 holdout questions, used as ground truth), and a retest answer block (the same questions answered again in wave 4, used to compute the human test-retest benchmark). On top of this, the metric that makes the headline number meaningful is the accuracy definition: for each of the 17 tasks, accuracy is 1 minus the absolute deviation between predicted and ground-truth answer, normalized by the answer range, then averaged across respondents; for binary items this reduces to exact match. This lets the authors express twin performance as a single ratio, 71.72% / 81.72% = 87.67%, so that the human's own consistency is the ceiling against which machine imitation is measured.

What would settle it

Compare the wave-1 demographics and key measures of the 451 participants who did not complete all four waves with those of the 2,058 completers; if the dropouts are systematically different (e.g., younger, lower-income, or lower-scoring), the 'representative US sample' claim behind the dataset fails. The paper reports no such comparison, so this is the most direct check.

Watch

Extended reading notes

Core claim

The paper's central claim is that a sufficiently rich individual-level survey record can be turned into an LLM persona that reproduces a large share of that person's behavioral responses, and that the field now has a public, large-scale resource for measuring that share honestly. The authors construct, for each of 2,058 participants, a structured record of roughly 412 non-holdout answers (the persona), hold out 88 questions covering 17 behavioral tasks, and have GPT-4.1-mini answer those questions as if it were the participant. The twin accuracy of 71.72% compares against a human test-retest accuracy of 81.72% on the same tasks, meaning the simulations capture roughly 88% of the consistency humans show with themselves across a two-week gap. On aggregate treatment effects, the twins replicate 6 of 10 between-subject and 2 of 5 within-subject classic results; the failures cluster in domains where humans show bias (outcome bias, omission bias, probability matching) or where the model cannot 'unlearn' textbook facts (e.g., the number of African countries in the UN). The paper concludes that the dataset is unique in combining breadth, representativeness, behavioral tasks, and a test-retest benchmark, and that it can accelerate transparent benchmarking of digital-twin methods.

Load-bearing premise

The 2,058 people who completed all four waves still represent the US adult population, even though about 18% of the initially recruited 2,509 dropped out and the paper does not analyze whether completers differ from dropouts.

Editorial extensions

If this is right

  • Future persona methods can be benchmarked on the same persona/holdout/retest split, with 71.72% as a strong baseline reported here.
  • The 87.67% ratio implies LLM twins capture most of the predictable, person-specific variance in these tasks; the remaining gap is likely a mix of model limitations and the irreducible noise in human re-answers.
  • The aggregate failures (outcome bias, omission bias, Allais, probability matching) show that some classic effects will not be reproduced by LLM twins, and that these are precisely the cases where human behavior is non-normative.
  • The public release of all four waves, including the retest block, lets researchers separate measurement error from genuine behavioral change.
  • Across the many prompt/format/model variations tested, accuracies cluster in a narrow band (67.9-71.9%), suggesting the architecture matters less than the quality of the persona data.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the representativeness holds, the dataset could serve as a calibration set for correcting LLM opinion surveys toward true population distributions, since it contains both the psychological profiles and the actual political and medical judgments of the same people.
  • The two-week gap between wave 1-3 and wave 4 gives an upper bound on what any deterministic mapping from a persona to behavior can achieve; methods that exceed ~82% are not 'better' than the human-human consistency, but may be exploiting task-specific regularities rather than true personalization.
  • A direct comparison of the 451 week-1 dropouts with the 2,058 completers on demographics and wave-1 measures would quantify the attrition bias and tell whether the 'representative' target is met; this analysis is not reported.
  • Because the twins reproduced the framing, conjunction, and myside effects but not the medical-domain biases, one testable hypothesis is that LLM personas are systematically more 'normative' and more trustful of medical authority than the US population; future surveys that vary the medical scenario could falsify this.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper introduces Twin-2K-500, a publicly released dataset of 2,058 US participants who each answered 500+ questions across four waves, covering demographics, personality, cognitive ability, economic preferences, heuristics-and-biases experiments, and a pricing study. The authors construct LLM-based digital twins by feeding each participant's non-holdout responses as a text or JSON persona to GPT-4.1-mini (and several ablations), then evaluate the twins on 88 holdout questions from waves 1–3. They report a digital-twin accuracy of 71.72% against a human test-retest accuracy of 81.72%, a ratio they describe as 87.67%, and they assess aggregate replication of classic behavioral-economics effects. The paper also reports data-quality checks: correlations with face validity, replication of most known effects, and a test-retest baseline. The dataset and simulation code are publicly available.

Significance. If the central claims hold, this is a valuable community resource: it is, to my knowledge, the largest public dataset explicitly designed for digital-twin benchmarking, combining rich psychological profiles, behavioral tasks, and a test-retest baseline. The data collection appears careful, the authors honestly report failures (e.g., base-rate fallacy not replicating), and the holdout design is a genuine out-of-sample prediction rather than a circular fit. The public release of persona, evaluation, and retest JSON blocks will facilitate standardized comparisons. However, the headline quantitative claim about digital-twin fidelity currently lacks a crucial control—an unpersonalized LLM baseline—and the reported ratio overstates the twins' performance relative to chance. These issues weaken, but do not invalidate, the dataset contribution.

major comments (4)
  1. [§5.1, Table 2] No unpersonalized LLM baseline is reported: every LLM row in Table 2 receives at least some persona input, so the 12.55-point improvement over random guessing could reflect generic LLM priors about how humans answer classic tasks rather than the specific individual's profile. The aggregate results in Table 3—twins unanimously choosing the normative option in the Allais problem, 98.8% giving the correct UN country count, 4.0% refusing the vaccine versus 45% of humans, and 100% choosing the maximizing strategy—suggest that a competent unpersonalized LLM would already achieve high accuracy on many holdout questions. Please add a condition with the same model and questions but no persona, and report the incremental accuracy attributable to personalization. This is load-bearing for the abstract's claim that the digital twins 'predict human behavior well.'
  2. [§5.1, Abstract] The headline '87.67% relative accuracy' is computed as a ratio of absolute accuracies (71.72/81.72 ≈ 87.76%, with a small arithmetic discrepancy in the reported value). This ratio does not account for the chance baseline. Measured relative to random guessing, the twins capture (71.72–59.17)/(81.72–59.17) ≈ 55.6% of the test-retest advantage, a much less favorable number. Please report both the absolute ratio and the chance-relative ratio, and use the more conservative framing in the abstract and Figure 1.
  3. [§5.2, Table 3 vs. §5.1] The sentence in §5.1—'The improvement over the baseline is consistent across all question types, highlighting the value of personalization and LLM-based simulation'—is contradicted by Table 3, where the twins fail to replicate the outcome bias, sunk cost fallacy, Allais problem, omission bias, and probability matching, and only partially replicate anchoring and nonseparability. Please qualify the individual-level claim in light of these aggregate replication failures, and discuss the possibility that the individual-level accuracy is driven by tasks where behavioral variation is small relative to generic priors.
  4. [§2, Abstract] The abstract and Section 2 describe the sample as 'representative US respondents,' but no attrition analysis is provided: 2,509 participants completed Wave 1 and 2,058 completed all four waves (82%). Without a comparison of completers versus dropouts on baseline demographics and key measures, the representativeness claim is not empirically supported. Please report such an analysis, or soften the representativeness claim to 'a Prolific sample recruited with demographic targets.'
minor comments (5)
  1. [Abstract, Figure 1] The abstract and Section 5.1 report 87.67% relative accuracy, while Figure 1 and the conclusion say '88%.' Please unify these numbers.
  2. [§3, Figure 2] The random-guessing baseline is defined only as 'chooses each answer from a random uniform distribution.' Please specify whether this is uniform over all response options or calibrated to empirical marginal distributions, since this affects the magnitude of the claimed improvement over baseline.
  3. [Table 3, Anchoring and adjustment row] The twins' row shows a partial replication ('✓✗') for anchoring, but the text only explains the UN country count. Please state explicitly which of the two anchoring items (redwood height or UN countries) replicated and which did not.
  4. [§5.2, Probability matching] The text states that digital twins 'always selected the normative option' in probability matching; please clarify whether this holds for both the card and dice versions, and whether any 'OTHER' strategies were observed.
  5. [Technical Appendix A.1] The system prompt instructs the model to answer as the persona but does not specify whether repeated sampling with temperature is used; for binary and numerical tasks, please state the number of samples per question and whether the reported accuracy is averaged over them.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the digital twin accuracy is a genuine out-of-sample holdout evaluation, and the 87.67% ratio is a direct quotient of independently measured accuracies.

full rationale

The paper's central quantitative claim rests on an out-of-sample evaluation. Section 4 partitions each participant's record into a Persona JSON (all non-holdout content from waves 1-3), an Evaluation answer-block JSON (wave 1-3 responses to 88 holdout questions), and a Retest answer-block JSON (wave 4 responses). The digital twin is prompted only with the persona and must answer the holdout 'New Survey Question'; accuracy is scored against the excluded wave 1-3 ground truth. The human test-retest benchmark is computed independently from wave 4 versus wave 1-3 responses. The reported 87.67% is simply 71.72/81.72, a quotient of two separately measured accuracies, not a fitted parameter or a quantity defined in terms of the prediction. Self-citations appear (e.g., the pricing study from Gui and Toubia 2023, and scales from Johnson et al. 2019), but they supply instruments or comparison points and do not enter the holdout accuracy calculation; they are not load-bearing for the main claim. The absence of an unpersonalized LLM baseline is a substantive threat to attribution of the accuracy to personalization, but that is a benchmarking/comparison concern, not circularity: it does not make the reported accuracy reduce to its inputs. Accordingly, no circular step can be quoted from the paper, and the appropriate finding is no significant circularity.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper introduces no free parameters or new entities; it applies standard psychometric and behavioral instruments. The main assumptions are about sample representativeness and the validity of self-report measures.

assumptions (3)
  • domain assumption The final sample of 2,058 participants is representative of the US adult population despite attrition from 2,509 to 2,058.
    Section 2 states the target was a representative sample by age, sex, and ethnicity, but no attrition analysis is reported.
  • domain assumption Self-reported answers to psychometric scales and economic experiments are valid measures of the underlying constructs.
    Used throughout Section 3 to claim data quality and construct correlations.
  • domain assumption Wave 4 repeated responses provide a clean test-retest benchmark unaffected by memory or practice effects.
    Section 3 uses the 81.72% test-retest accuracy as a benchmark for digital twin performance.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Twin-2K-500: A dataset for building digital twins of over 2,000 people based on their answers to over 500 questions." pith.science (2026). https://pith.science/paper/55LORQAB

@misc{pith2026250517479,
  author       = {Pith},
  title        = {Pith review of: Twin-2K-500: A dataset for building digital twins of over 2,000 people based on their answers to over 500 questions},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/55LORQAB}},
  note         = {Machine review of arXiv:2505.17479}
}
abstract

LLM-based digital twin simulation, where large language models are used to emulate individual human behavior, holds great promise for research in AI, social science, and digital experimentation. However, progress in this area has been hindered by the scarcity of real, individual-level datasets that are both large and publicly available. This lack of high-quality ground truth limits both the development and validation of digital twin methodologies. To address this gap, we introduce a large-scale, public dataset designed to capture a rich and holistic view of individual human behavior. We survey a representative sample of $N = 2,058$ participants (average 2.42 hours per person) in the US across four waves with 500 questions in total, covering a comprehensive battery of demographic, psychological, economic, personality, and cognitive measures, as well as replications of behavioral economics experiments and a pricing survey. The final wave repeats tasks from earlier waves to establish a test-retest accuracy baseline. Initial analyses suggest the data are of high quality and show promise for constructing digital twins that predict human behavior well at the individual and aggregate levels. By making the full dataset publicly available, we aim to establish a valuable testbed for the development and benchmarking of LLM-based persona simulations. Beyond LLM applications, due to its unique breadth and scale the dataset also enables broad social science research, including studies of cross-construct correlations and heterogeneous treatment effects.

Figures

Figures reproduced from arXiv: 2505.17479 by the authors.

Figure 1
Figure 1. Overview 2 Methods We assembled a wide-range of measures proposed in the social science literature over the past several decades. In addition to 14 demographic questions, we included 19 personality tests that measured 26 constructs over 279 questions, 11 cognitive ability tests (85 questions, 11 measures), 10 economic preferences tests (34 questions, 10 measures). We also replicated 11 between-subject experiments (1… view at source ↗
Figure 2
Figure 2. Predictive accuracy were not replicated. In the outcome bias experiment, participants evaluate a physician’s decision to operate on a patient. Humans evaluate the decision more favorably when the operation succeeded than when it failed, despite the risk being greater in the first condition. In contrast, digital twins all gave a favorable rating (“correct” to “clearly correct”), with no significant different across c… view at source ↗

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Large Behavior Model: A Promptable Digital Twin of the Retail Customer

    cs.AI 2026-07 conditional novelty 5.0 of 10

    Grounding an LLM in verbalized transaction histories via Person–Environment prompting, continued pre-training, SFT, and GRPO yields stronger retail decision simulation than frontier models, with partial cross-domain transfer.

Reference graph

Works this paper leans on

51 extracted references · 45 canonical work pages · cited by 1 Pith paper

  1. [1]

    V., Arriaga, R

    Aher, G. V., Arriaga, R. I., and Kalai, A. T. (2023). Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies . In Proceedings of the 40th International Conference on Machine Learning , pages 337--371. PMLR. ISSN: 2640-3498

    work page 2023

  2. [2]

    Alattar, L., Messel, M., and Rogofsky, D. (2018). An introduction to the understanding america study internet panel. Soc. Sec. Bull. , 78:13

    work page 2018

  3. [3]

    P., Busby, E

    Argyle, L. P., Busby, E. C., Fulda, N., Gubler, J. R., Rytting, C., and Wingate, D. (2023). Out of One , Many : Using Language Models to Simulate Human Samples . Political Analysis , pages 1--15. Publisher: Cambridge University Press

    work page 2023

  4. [4]

    and Hershey, J

    Baron, J. and Hershey, J. C. (1988). Outcome bias in decision evaluation. Journal of personality and social psychology , 54(4):569

    work page 1988

  5. [5]

    T., Epstein, N., Brown, G., and Steer, R

    Beck, A. T., Epstein, N., Brown, G., and Steer, R. A. (1988). An inventory for measuring clinical anxiety: psychometric properties. Journal of consulting and clinical psychology , 56(6):893

    work page 1988

  6. [6]

    Brand, J., Israeli, A., and Ngwe, D. (2023). Using GPT for Market Research

    work page 2023

  7. [7]

    and Toubia, O

    Brucks, M. and Toubia, O. (2023). Prompt architecture can induce methodological artifacts in large language models. Available at SSRN 4484416

    work page 2023

  8. [8]

    T., Petty, R

    Cacioppo, J. T., Petty, R. E., and Feng Kao, C. (1984). The efficient assessment of need for cognition. Journal of personality assessment , 48(3):306--307

    work page 1984

Show all 51 references
  1. [9]

    D., Trapnell, P

    Campbell, J. D., Trapnell, P. D., Heine, S. J., Katz, I. M., Lavallee, L. F., and Lehman, D. R. (1996). Self-concept clarity: Measurement, personality correlates, and cultural boundaries. Journal of personality and social psychology , 70(1):141

    1996

  2. [10]

    Carr \'e , A., Stefaniak, N., d'Ambrosio, F., Bensalah, L., and Besche-Richard, C. (2013). The basic empathy scale in adults (bes-a): factor structure of a revised form. Psychological assessment , 25(3):679

    2013

  3. [11]

    Center, P. R. (2023). Pew research center survey datasets. Accessed: 2024-05-16

    2023

  4. [12]

    Date, C. (1987). Beck depression inventory

    1987

  5. [13]

    and Ortoleva, P

    Dean, M. and Ortoleva, P. (2019). The empirical relationship between nonstandard economic behaviors. Proceedings of the National Academy of Sciences , 116(33):16262--16267

    2019

  6. [14]

    Dillion, D., Tandon, N., Gu, Y., and Gray, K. (2023). Can AI language models replace human participants? Trends in Cognitive Sciences , 0(0). Publisher: Elsevier

    2023

  7. [15]

    Epley, N., Keysar, B., Van Boven, L., and Gilovich, T. (2004). Perspective taking as egocentric anchoring and adjustment. Journal of personality and social psychology , 87(3):327

    2004

  8. [16]

    Fellner, B., Holler, M., Kirchler, E., and Schabmann, A. (2007). Regulatory focus scale (rfs): Development of a scale to record dispositional regulatory focus. Swiss Journal of Psychology , 66(2):109--116

    2007

  9. [17]

    Furnas, A. C. and LaPira, T. M. (2024). The people think what i think: False consensus and unelected elite misperception of public opinion. American Journal of Political Science , 68(3):958--971

    2024

  10. [18]

    forward flow

    Gray, K., Anderson, S., Chen, E. E., Kelly, J. M., Christian, M. S., Patrick, J., Huang, L., Kenett, Y. N., and Lewis, K. (2019). “forward flow”: A new measure to quantify free thought and predict creativity. American Psychologist , 74(5):539

    2019

  11. [19]

    and Toubia, O

    Gui, G. and Toubia, O. (2023). The challenge of using llms to simulate human behavior: A causal inference perspective. arXiv preprint arXiv:2312.15524

    2023

  12. [20]

    G \"u th, W., Schmittberger, R., and Schwarze, B. (1982). An experimental analysis of ultimatum bargaining. Journal of economic behavior & organization , 3(4):367--388

    1982

  13. [21]

    L., Winterich, K

    Haws, K. L., Winterich, K. P., and Naylor, R. W. (2014). Seeing the world through green-tinted glasses: Green consumption values and responses to environmentally friendly products. Journal of consumer psychology , 24(3):336--354

    2014

  14. [22]

    Hewitt, L., Ashokkumar, A., Ghezae, I., and Willer, R. (2024). Predicting results of social science experiments using large language models. Preprint

    2024

  15. [23]

    T., Klein, R., and Strauman, T

    Higgins, E. T., Klein, R., and Strauman, T. (1985). Self-concept discrepancy theory: A psychological model for distinguishing among different aspects of depression and anxiety. Social cognition , 3(1):51--76

    1985

  16. [24]

    Horton, J. J. (2023). Large Language Models as Simulated Economic Agents : What Can We Learn from Homo Silicus ? arXiv:2301.07543 [econ, q-fin]

    2023

  17. [25]

    P., Srivastava, S., et al

    John, O. P., Srivastava, S., et al. (1999). The big-five trait taxonomy: History, measurement, and theoretical perspectives

    1999

  18. [26]

    J., Meier, S., and Toubia, O

    Johnson, E. J., Meier, S., and Toubia, O. (2019). What’s the catch? suspicion of bank motives and sluggish refinancing. The Review of Financial Studies , 32(2):467--495

    2019

  19. [27]

    and Tversky, A

    Kahneman, D. and Tversky, A. (1973). On the psychology of prediction. Psychological review , 80(4):237

    1973

  20. [28]

    C., Stahl, C., and Erdfelder, E

    Klauer, K. C., Stahl, C., and Erdfelder, E. (2007). The abstract selection task: new data and an almost comprehensive model. Journal of Experimental Psychology: Learning, Memory, and Cognition , 33(4):680

    2007

  21. [29]

    R., and Johnson, E

    Krefeld-Schwalb, A., Sugerman, E. R., and Johnson, E. J. (2024). Exposing omitted moderators: Explaining why effect sizes differ in the social sciences. Proceedings of the National Academy of Sciences , 121(12):e2306281121

    2024

  22. [30]

    Lennox, R. D. and Wolfe, R. N. (1984). Revision of the self-monitoring scale

    1984

  23. [31]

    Li, A., Chen, H., Namkoong, H., and Peng, T. (2025). Llm generated persona is a promise with a catch. arXiv preprint arXiv:2503.16527

    2025 arXiv

  24. [32]

    and Nantel, G

    Markovits, H. and Nantel, G. (1989). The belief-bias effect in the production and evaluation of logical conclusions. Memory & cognition , 17(1):11--17

    1989

  25. [33]

    Motoki, F., Pinho Neto, V., and Rodrigues, V. (2024). More human than human: measuring chatgpt political bias. Public Choice , 198(1):3--23

    2024

  26. [34]

    Y., Morrin, M., Ward, A., Schwartz, B., and Hulland, J

    Nenkov, G. Y., Morrin, M., Ward, A., Schwartz, B., and Hulland, J. (2008). A short form of the maximization scale: Factor structure, reliability and validity studies. Judgment and Decision making , 3(5):371--388

    2008

  27. [35]

    S., O'Brien, J

    Park, J. S., O'Brien, J. C., Cai, C. J., Morris, M. R., Liang, P., and Bernstein, M. S. (2023). Generative Agents : Interactive Simulacra of Human Behavior . arXiv:2304.03442 [cs]

    2023 arXiv

  28. [36]

    S., Zou, C

    Park, J. S., Zou, C. Q., Shaw, A., Hill, B. M., Cai, C., Morris, M. R., Willer, R., Liang, P., and Bernstein, M. S. (2024). Generative agent simulations of 1,000 people. arXiv preprint arXiv:2411.10109

    2024 arXiv

  29. [37]

    Qin, X., Huang, M., and Ding, J. (2024). Aiturk: Using chatgpt for social science research. Available at SSRN 4922861

    2024

  30. [38]

    Reynolds, W. M. (1982). Development of reliable and valid short forms of the marlowe-crowne social desirability scale. Journal of clinical psychology , 38(1):119--125

    1982

  31. [39]

    I., Cryder, C

    Rick, S. I., Cryder, C. E., and Loewenstein, G. (2008). Tightwads and spendthrifts. Journal of consumer research , 34(6):767--782

    2008

  32. [40]

    and Van Hiel, A

    Roets, A. and Van Hiel, A. (2011). Item selection and validation of a brief, 15-item version of the need for closure scale. Personality and individual differences , 50(1):90--94

    2011

  33. [41]

    Ruvio, A., Shoham, A., and Bren c i c , M. M. (2008). Consumers' need for uniqueness: short-form scale development and cross-cultural validation. International Marketing Review , 25(1):33--53

    2008

  34. [42]

    Santurkar, S., Durmus, E., Ladhak, F., Lee, C., Liang, P., and Hashimoto, T. (2023a). Whose Opinions Do Language Models Reflect ? arXiv:2303.17548 [cs]

    2023 arXiv

  35. [43]

    Santurkar, S., Durmus, E., Ladhak, F., Lee, C., Liang, P., and Hashimoto, T. (2023b). Whose opinions do language models reflect? In International Conference on Machine Learning , pages 29971--30004. PMLR

    2023

  36. [44]

    Stanovich, K. E. and West, R. F. (2008). On the relative independence of thinking biases and cognitive ability. Journal of personality and social psychology , 94(4):672

    2008

  37. [45]

    Thaler, R. (1985). Mental accounting and consumer choice. Marketing science , 4(3):199--214

    1985

  38. [46]

    Trapnell, P. D. and Paulhus, D. L. (2012). Agentic and communal values: Their scope and measurement. Journal of personality assessment , 94(1):39--52

    2012

  39. [47]

    Triandis, H. C. and Gelfand, M. J. (1998). Converging measurement of horizontal and vertical individualism and collectivism. Journal of personality and social psychology , 74(1):118

    1998

  40. [48]

    and Kahneman, D

    Tversky, A. and Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases: Biases in judgments reveal some heuristics of thinking under uncertainty. science , 185(4157):1124--1131

    1974

  41. [49]

    and Kahneman, D

    Tversky, A. and Kahneman, D. (1981). The framing of decisions and the psychology of choice. science , 211(4481):453--458

    1981

  42. [50]

    and Kahneman, D

    Tversky, A. and Kahneman, D. (1983). Extensional versus intuitive reasoning: The conjunction fallacy in probability judgment. Psychological review , 90(4):293

    1983

  43. [51]

    Wilson, A. V. and Bellezza, S. (2022). Consumer minimalism. Journal of Consumer Research , 48(5):796--816

    2022

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.