Learning from Synthetic Labs: Language Models as Auction Participants

Anand Shah; Arif K. Dayi; David C. Parkes; Jeffrey G. Wang; John J. Horton; Kehang Zhu; Yanchen Jiang

REVIEW 3 major objections 4 minor 3 cited by

Learning from Synthetic Labs: Language Models as Auction Participants

T0 review · 3 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read LLM bidders reproduce classic human auction behaviors — risk aversion, clock-auction clarity, and the winner's curse — in a synthetic lab.

desk verdict Useful proof-of-concept with honest reporting, but the proxy-validity claim is overstated and the memorization concern is untested. read the letter →

arxiv 2507.09083 v1 pith:CRHB6QQH submitted 2025-07-12 cs.GT cs.AI

Anand Shah , Kehang Zhu , Yanchen Jiang , Jeffrey G. Wang , Arif K. Dayi , John J. Horton , David C. Parkes This is my paper · ORCID

classification cs.GTcs.AI MSC 91B2691A90

keywords largelanguagemodelsauctionexperimentssyntheticlabschain-of-thoughtreasoningwinner'scurseobviouslystrategy-proofmechanismdesignGPT-4

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 tries to establish that large language models, when given chain-of-thought reasoning and a plan-bid-reflect loop, can act as plausible stand-ins for human bidders in auction experiments. It finds that GPT-4 agents bid above the risk-neutral prediction in first-price auctions, matching risk-averse human behavior, bid closer to true value in ascending clock auctions than in strategically equivalent sealed-bid auctions, and fall victim to the winner's curse in common-value settings. If these results hold, auction researchers could test designs and interventions at a cost roughly three orders of magnitude lower than running human experiments. The paper runs over a thousand GPT-4 auctions for less than $400 and provides a codebase to extend the approach to any auction format an experimenter can describe.

What carries the argument

The central machinery is the synthetic-lab protocol: a plan-bid-reflect loop in which each GPT-4 bidder independently writes a bidding plan, places a bid given its private value, observes the outcome and all bids, then reflects and revises its plan before the next round. This loop, powered by chain-of-thought prompting (asking the model to reason step-by-step before answering), is what generates the behavior under study. Its low per-round cost lets the authors run more than a thousand auctions and compare aggregate bid patterns against theoretical equilibria and published human data.

What would settle it

Run the same protocol on a novel auction format with no published human benchmark, and record whether the model's chain-of-thought references specific known studies when it produces human-typical bids; absence of such references on novel formats, together with preserved behavioral patterns, would support emergent reasoning, while patterns that only appear for formats covered in the training literature would point to recitation.

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Extended reading notes

Core claim

The central claim is that LLM agents, when endowed with chain-of-thought planning and reflection, reproduce the main behavioral regularities of human auction experiments across multiple classic formats. In independent private-value auctions, their first-price bids sit above the risk-neutral Bayes-Nash equilibrium, consistent with the risk-averse overbidding documented in human subjects, while their second-price bids skew below value, an asymmetry not found in the human data. In affiliated private-value settings, LLM bidders drop out near their value in ascending clock auctions far more often than they bid their value in the strategically equivalent second-price sealed-bid auction, mirroring the obvious strategy-proofness effect. In common-value auctions, they bid as if their private signal were an unbiased estimate of the common value and suffer a winner's curse that deepens as the number of bidders grows. The paper also shows that naive prompt changes, such as language and currency, matter little, while prompting with the language of Nash deviations substantially improves play, and that an extended closing rule eliminates last-second sniping in an eBay-style environment.

Load-bearing premise

The paper assumes that GPT-4's bids come from reasoning about the described auction context rather than from memorizing the results of the auction-experiment literature during training.

Editorial extensions

If this is right

If LLM proxies are valid, auction researchers can screen designs and interventions for about $10 per experiment instead of $15,000, making large parameter sweeps feasible.
The similarity with human risk aversion and the winner's curse means LLM agents can generate synthetic data on behavioral deviations, not just rational benchmarks.
The finding that clock formats improve truth-telling suggests that obvious strategy-proofness design principles carry over to LLM agents, and that prompting interventions can be tested in silico before human trials.
The eBay closing-rule result offers a low-cost path to revisit classic marketplace design debates, such as the competing ending rules that distinguished Amazon and eBay auctions.
Naive prompt robustness, across languages and currencies, increases confidence that the observed patterns are not artifacts of a specific wording choice.

Reading between the lines

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

A direct test of the emergent-reasoning interpretation would be to run the same protocol on a novel auction format with no published human benchmark; if the model still displays human-typical biases there, the proxy claim is strengthened.
The framework's flexibility suggests the same plan-bid-reflect loop could be applied to other mechanism-design settings, such as matching markets or contests, to generate behavioral data before expensive human validation.
The contrast between LLM underbidding and human overbidding in second-price sealed-bid auctions hints at a model-specific bias that prompt engineering might adjust, potentially letting LLM proxies match not just the magnitude but the direction of human error.
Because the paper runs multi-round auctions, the observed overbidding may be partly an artifact of the agent's explicit instruction to explore and gather data, rather than a stable preference; single-round ablation results in the appendix suggest this is the case.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Thanks for sending this. Quick take: it's a useful proof-of-concept with transparent reporting, but the headline claim overreaches. The paper's own SPSB data shows LLMs underbid 66% of the time, while Kagel-Levin humans overbid 67% - that's the opposite error. The abstract says they 'agree with the experimental literature,' which is only true in direction for FPSB and clock formats, not for the SPSB failure mode. To the paper's credit, they acknowledge this mismatch in the text, but the abstract still overstates.

What's new: the systematic benchmark across FPSB/SPSB/TPSB/all-pay, the APV clock comparisons, the common-value winner's curse tests, the eBay-style simulations showing soft-close reduces sniping, and the six prompt interventions. The eBay results are genuinely new and interesting. The prompt intervention results are also useful for thinking about how description affects play.

What the paper does well: the protocols are described in enough detail to replicate, the robustness checks (language, currency, one-shot) are thoughtful, and they openly report where LLMs diverge from human data (SPSB underbid, AC-B not differing from AC). That honesty earns credit.

Soft spots, in proportion. The big one is the memorization concern. The load-bearing claim is that LLM bids reflect emergent strategic reasoning, not recitation of the benchmark literature. The paper never tests this. The out-of-the-box ablation and the prompt-sensitivity results suggest behavior is heavily framing-dependent, which cuts both ways, but the authors don't engage with the contamination question. An easy control would be to debias or to test on newer less-seen settings. Until that's done, the 'proxy validity' claim is conditional.

Second, the sample sizes are small - five experiments per format, 15 rounds, three bidders. The winner's curse results rely on boxplots from what looks like a handful of auctions per n. That's enough for a proof-of-concept, but not for the confidence in the abstract.

Third, the code and data are promised but not yet available. For a paper whose main deliverable is a framework, that's a real issue.

Overall: I think this deserves peer review. The empirical work is careful, the eBay findings are new, and the framework is useful. But the central claim needs to be pulled back to 'LLM behavior is consistent with some human regularities' rather than 'LLMs are valid proxies.' Suggested major revision, with a contamination control and code release.

Would I cite? Not until the code and the control test exist. Bring to reading group though - good discussion of what LLM experiments can and can't tell us.

Referee Report

3 major / 4 minor

Summary. This paper proposes using large language model (LLM) agents as low-cost proxies for human bidders in auction experiments. The authors run a large number of simulated auctions with GPT-4 agents under chain-of-thought reasoning, covering first-, second-, third-price, all-pay, ascending clock, common-value, and eBay-style auctions. They report that LLM bidders overbid relative to the risk-neutral benchmark in first-price auctions, bid closer to truth in clock formats than in sealed-bid formats, suffer the winner's curse in common-value settings, and exhibit strategic behaviors such as sniping in eBay-style auctions. They also study how different prompt interventions affect bidding accuracy. The central claim is that LLM agents reproduce key empirical regularities from the human experimental auctions literature, thereby serving as a valid and cheap synthetic lab.

Significance. If the proxy-validity claim were established, this would be a useful methodological contribution: it would let researchers explore auction designs at a fraction of the cost of human experiments, and the open-source framework would enable broader use. The paper's strengths are its breadth of auction formats, the explicit planning-and-reflection protocol, the cost transparency, and the inclusion of robustness checks on prompts, languages, and currencies. However, the abstract and introduction overstate the agreement with the human literature, and the paper does not address the most important threat to its central claim: that GPT-4's training data includes the very experimental results used as benchmarks, so the observed agreement could reflect memorization rather than emergent strategic reasoning. This unresolved issue, together with an internal contradiction in the reported language-robustness results, means the paper currently supports a proof-of-concept but not yet the stronger proxy-validity claim.

major comments (3)

[Abstract; Section 3.1.5, Table 2] The abstract and introduction state that LLM bidders 'agree with the experimental literature in auctions' and specifically 'produce results consistent with risk-averse human bidders.' However, Section 3.1.5 and Table 2 show that in the SPSB auction, LLMs underbid their value 65.78% of the time, while the paper itself notes that Kagel and Levin (1993) report humans overbid 67% of the time. The direction of the dominant deviation is therefore opposite to the human data, not merely different in magnitude. This is a direct contradiction of the unqualified 'agree' claim, and it is load-bearing because the paper uses this agreement as evidence that LLMs can serve as proxies. The authors should either substantially qualify the central claim in the abstract and introduction or reframe the SPSB result as a systematic difference between LLM and human behavior that itself requires explanation before proxy validity can be assumed.
[Section 2.1; Section 3.1.5; Appendix D] The central proxy-validity claim rests on the premise that GPT-4's bidding behavior reflects emergent, context-driven strategic reasoning rather than memorized reproduction of the auction-theory and experimental-literature content in its training data. The paper never tests this premise. The protocol (Section 2.1) intentionally avoids persona anchoring, but it still uses the exact benchmark settings and vocabulary (the same value ranges, the same APV and common-value structures, and descriptions that echo the experimental papers). Appendix D show that removing the plan-bid-reflect loop produces qualitatively different behavior (e.g., no overbidding in SPSB), which is consistent with the alternative explanation that the observed regularities are sensitive to priming and surface cues rather than reflecting a stable reasoning disposition. Because the intended use of the proxy is to learn about new auction designs, reproducing memorized results would not validate that use. The paper should include a control that distinguishes these hypotheses, for example: (i) auctions with novel formats or payment rules that are unlikely to appear verbatim in training data, (ii) paraphrase or distractor tests that remove auction-theory vocabulary while preserving the incentive structure, or (iii) an analysis of whether the LLM's stated reasoning in chain-of-thought actually derives the equilibrium logic rather than citing known results. Without such a control, the agreement with human experiments remains ambiguous evidence for proxy validity.
[Section C.3, Figure 11; Abstract] The abstract claims that LLMs are 'not very sensitive to naive changes in prompts (e.g., language, currency),' but the robustness check in Appendix C.3 reports that the Russian-language FPSB prompt produces a stark outlier: agents bid close to their true value rather than shading bids as in the English version. This is a qualitative change in the key treatment, not a small quantitative difference. The paper should either remove the overgeneralized claim, or report this outlier prominently in the main text and explain why it does not undermine the conclusion that behavior is robust to prompt variation. The current presentation gives the reader an inaccurate impression of the evidence.

minor comments (4)

[Section 3.1.4] The sentence 'In particular, 90bids exceeded their value in this auction' appears to be missing a percentage sign or a word (likely '90% of bids'); please correct this typo.
[Section 4.1; Section 4.3] The claim that LLMs replicate sniping 'with no prompting' is somewhat overstated, because the prompt explicitly tells bidders that they may place bids on the final day and that no bidder knows if they are the last; this is a design cue that likely encourages late bidding. Please phrase this as 'with a standard rules description that mentions final-day bidding' rather than 'with no prompting.'
[Section 5.1, Table 7] The text says the risk-neutrality intervention 'has about the same performance as the proxy intervention,' but Table 7 reports R^2 = 0.4004 for the risk intervention, which is lower than the no-intervention R^2 = 0.4845 and much lower than the proxy intervention's 0.2966 (or the Nash intervention's 0.8365). The qualitative description should be tied more carefully to the reported fit statistics, or the table should be annotated to clarify that the comparison is limited to the overbidding component.
[Section 3.1.5 and Section 5 use multiple statistical tests] Several tables report pairwise t-tests or chi-square tests without any correction for multiple comparisons or clustering of bids within the 5 experiments per condition. Because the LLM agents share a common history across rounds, the effective sample size is smaller than the number of bids. Please either report cluster-robust standard errors or explicitly acknowledge this limitation in the statistical analysis.

Circularity Check

0 steps flagged · score 2.0 of 10

No definitional or fitted-input circularity; core benchmarks are external. Minor self-citations are not load-bearing.

full rationale

The paper's central claims are empirical comparisons of LLM bidding behavior against external experimental and theoretical benchmarks (Kagel and Levin 1986/1993, Li 2017, Roth and Ockenfels 2002, Cox et al. 1988). The derivation chain does not fit parameters to those benchmarks and then call the fit a prediction: the LLM agents are given auction rules and values, and their bids are generated without reference to the target experimental outcomes. There is no self-definitional step in which a quantity is defined in terms of the quantity it is claimed to predict. The protocol does instruct agents to maximize long-run profit and to use chain-of-thought reasoning, but this is a standard experimental instruction, not a fitted parameter tied to the benchmark results. Sections 3.1.5, 3.2.4, and 3.3.4 report outcomes that agree with, but are not derived from, the cited human experiments. The paper also explicitly acknowledges remaining open questions about how much prompting is needed to match empirical evidence, which is consistent with a non-circular, exploratory design. There are self-citations (e.g., Manning et al. 2024, Horton 2023, and the EDSL package), but these concern the simulation framework and social-science methodology, not the load-bearing auction benchmarks; they do not substitute for the external evidence. The concern that GPT-4 may have memorized the auction literature in its training data is a real external-validity or soundness risk, but it is not an internal circularity of the derivation chain: the paper does not define its conclusions in terms of its inputs or fit its inputs to its outputs. Accordingly, no specific circular step can be exhibited, and the appropriate circularity score is low rather than elevated.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The central claim depends on standard auction-theory benchmarks, the approximation of discrete bids by continuous equilibria, and the unstated assumption that LLM outputs reflect strategic simulation rather than memorized training data. No new theoretical entities are introduced; the paper's contribution is empirical.

free parameters (1)

GPT-4 sampling temperature = 0.5
Chosen by hand to encourage diversity; the authors state no tuning was performed, but the value affects bid variance and all downstream comparisons.

assumptions (4)

standard math Standard Bayes-Nash equilibrium formulas for IPV auctions with uniform values and risk neutrality (Table 1).
Used as theoretical benchmarks for FPSB, SPSB, TPSB, and all-pay auctions in Section 3.1.3.
domain assumption Discrete bids on a $1 grid approximate continuous-theory equilibria.
Invoked in Section 2.3 to justify comparing discrete LLM bids to continuous BNE benchmarks, relying on convergence results of Cai et al. (2010); the paper itself notes this link is not fully general (Rasooly and Gavidia-Calderon 2020).
domain assumption The plan-bid-reflect protocol and universal profit-maximization instruction elicit genuine strategic behavior from the LLM rather than text reproduction of known auction results.
Core to interpreting agreement with human benchmarks as evidence for LLM proxy validity; introduced in Section 2.1 and never tested against training-data contamination.
domain assumption Human experimental results (Kagel-Levin, Cox et al., Li 2017, Roth-Ockenfels) are accurate and transferable benchmarks.
The paper's central comparisons rely on these external experimental findings, cited in Sections 1, 3.1, 3.2, 3.3, and 4.

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Cite this review

Pith. "Pith review of Learning from Synthetic Labs: Language Models as Auction Participants." pith.science (2026). https://pith.science/paper/CRHB6QQH

@misc{pith2026250709083,
  author       = {Pith},
  title        = {Pith review of: Learning from Synthetic Labs: Language Models as Auction Participants},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CRHB6QQH}},
  note         = {Machine review of arXiv:2507.09083}
}

abstract

This paper investigates the behavior of simulated AI agents (large language models, or LLMs) in auctions, introducing a novel synthetic data-generating process to help facilitate the study and design of auctions. We find that LLMs -- when endowed with chain of thought reasoning capacity -- agree with the experimental literature in auctions across a variety of classic auction formats. In particular, we find that LLM bidders produce results consistent with risk-averse human bidders; that they perform closer to theoretical predictions in obviously strategy-proof auctions; and, that they succumb to the winner's curse in common value settings. On prompting, we find that LLMs are not very sensitive to naive changes in prompts (e.g., language, currency) but can improve dramatically towards theoretical predictions with the right mental model (i.e., the language of Nash deviations). We run 1,000$+$ auctions for less than $\$$400 with GPT-4 models (three orders of magnitude cheaper than modern auction experiments) and develop a framework flexible enough to run auction experiments with any LLM model and a wide range of auction design specifications, facilitating further experimental study by decreasing costs and serving as a proof-of-concept for the use of LLM proxies.

Figures

Figures reproduced from arXiv: 2507.09083 by the authors.

**Figure 1.** Comparison of FPSB(Left) and SPSB(Right) under IPV setting. Bidders’ values are independently drawn from a uniform distribution of [0, 99] and LLM bids given a value are represented by blue circles. The black dotted line represents the 45◦ line (the dominant strategy for SPSB auctions), the green dashed line represents the Bayes-Nash Equilibrium on the FPSB plot, and the solid red line represents the LOESS-smoothed … view at source ↗

**Figure 2.** Comparison of TPSB and all-pay under IPV setting. Bidders’ values are independently drawn from a uniform distribution of [0, 99] and the experimental data points are represented by blue circles. The black dotted line represents bid=value (the dominant strategy for SPSB auctions), the green line on the FPSB figure denotes the Bayes-Nash Equilibrium, and the solid red line represents the LOESS-smoothed bidding data. H… view at source ↗

**Figure 3.** Comparison of three strategically equivalent auctions (the AC, AC-B and SPSB auctions) in an APV setting. The AC and AC-B auctions are obviously strategy-proof, whereas SPSB auctions are only strategy-proof. The black dot-dash line represents the dominant strategy where a bidder’s bid equals their value. In the clock auctions, red triangles indicate non-winners’ drop out prices and blue circles indicate the final pr… view at source ↗

Figures from the paper (10 more)

**Figure 4.** Figure 4: Distribution of the winner’s profit across SPSB auctions with 2 to 6 bidders in a CV setting. Each box shows the interquartile range of profits, with the median indicated by the central yellow line. The horizontal red dashed line represents zero profit. As the number o…

**Figure 5.** Figure 5: Final Winning Bid Timing in Simulated eBay-style auctions in the IPV environment: standard eBay closing rule vs. modified eBay closing rule. The modified closing rule introduces the auction-extension logic, moving the timing of the final winning bid substantially earli…

**Figure 6.** Figure 6: CDF of Final Winning Bid Timing Across Variations on eBay-Style auctions, IPV environment. The cumulative distribution function of the final winning bid time for each auction format. Formats with a modified closing rule brings earlier, both with and without the hidden …

**Figure 7.** Figure 7: Results for SPSB with the six interventions, IPV environment. The green dotted line represents the scenario of bid=value, blue dots are bids from agents, and the red line is the LOESS smoothed curve of bids. Menu and proxy framings reduce scatter but still hug the regi…

**Figure 8.** Figure 8: One-round Sealed-bid auction under IPV 39 [PITH_FULL_IMAGE:figures/full_fig_p040_8.png]

**Figure 9.** Figure 9: Robustness check of Number of agent in FPSB (IPV setting) [PITH_FULL_IMAGE:figures/full_fig_p041_9.png]

**Figure 10.** Figure 10: Robustness check with Currency Variation under IPV setting. [PITH_FULL_IMAGE:figures/full_fig_p043_10.png]

**Figure 11.** Figure 11: Robustness check with Language Variation (under IPV setting) [PITH_FULL_IMAGE:figures/full_fig_p044_11.png]

**Figure 12.** Figure 12: Out-of-the-Box LLM agent in FPSB (left) and SPSB (right) auctions under IPV setting. The [PITH_FULL_IMAGE:figures/full_fig_p046_12.png]

**Figure 13.** Figure 13: eBay Revenue by Auction Type, IPV environment No significant different between any of the settings. At the start of each round, bidders will see their value for the prize, randomly drawn between \$0 and \${{private}}, with all values equally likely. After learning you…

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Forward citations

Cited by 3 Pith papers

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

Can LLM Agents Price Competitively? A Dynamic Multi-Attribute Auction Benchmark for Agentic Commerce
cs.AI 2026-07 conditional novelty 7.0 of 10

LLM merchant agents in a new dynamic auction benchmark capture at most 32% of hindsight-optimal profit; profit tracks margin per win more than win rate, and fast pre-shock learners adapt poorly to preference shocks.
Choose Your Agent: Tradeoffs in Adopting AI Advisors, Coaches, and Delegates in Multi-Party Negotiation
cs.GT 2026-02 conditional novelty 6.0 of 10

Users prefer an AI Advisor but gain most with a Delegate, because human editing filters out the AI's best proposals.
When Is Delegated Play Truthful? Within-Range Regret and the Trilemma of Aligned Delegation
cs.GT 2026-07 conditional novelty 4.0 of 10

The gain from misreporting to your own proxy equals the proxy's within-range regret, so honest reporting is optimal exactly when the proxy already plays the best reachable action; guardrails then face a binding–truthf...

Reference graph

Works this paper leans on

7 extracted references · 6 canonical work pages · cited by 3 Pith papers

[2]

Your value for the prize is equal to the common value plus your private taste adjustment

Then, for each bidder, a private taste adjustment will be drawn between 0 and {{private}}, with all values equally likely. Your value for the prize is equal to the common value plus your private taste adjustment. You will not learn the common value or your private taste adjustment separately. This means that each person in your group may have a different ...

work page
[5]

Your value for the prize is equal to the common value plus your private taste adjustment

Then, for each bidder, a private taste adjustment will be drawn between 0 and {{private}}, with all values equally likely. Your value for the prize is equal to the common value plus your private taste adjustment. You will not learn the common value or your private taste adjustment separately. This means that each person in your group may have a different ...

work page
[6]

First we will randomly draw a common value between {{ common_low}} and {{common_high}}, with all values equally likely

work page
[7]

Your value for the prize is equal to the common value plus your private taste adjustment

Then, for each bidder, a private taste adjustment will be drawn between 0 and {{private}}, with all values equally likely. Your value for the prize is equal to the common value plus your private taste adjustment. You will not learn the common value or your private taste adjustment separately. This means that each person in your group may have a different ...

work page
[8]

For each round, a common value will be drawn between {{ common_low}} and {{common_high}}, with all values equally likely to be drawn

work page
[9]

proxy bidding

For each person, a private noisy adjustment will be drawn between -{{private}} and {{private}}, with all values equally likely to be drawn. We will tell you your perceived value, the sum of the common value and the private noise adjustment. However, everyone’s 49 true value for the prize is equal to the shared common value . After learning your perceived ...

work page
[2025]

kelly is a warm person, joseph is a role model

Automated mechanism design: A survey. ACM SIGecom Exchanges 22, 2 (2025), 102–120. Constantinos Daskalakis, Ian Gemp, Yanchen Jiang, Renato Paes Leme, Christos Papadimitriou, and Geor- gios Piliouras. 2024. Charting the Shapes of Stories with Game Theory.arXiv preprint arXiv:2412.05747 (2024). Emmanuel Dechenaux, Dan Kovenock, and Roman M Sheremeta. 2015....

arXiv 2025

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [2]

Your value for the prize is equal to the common value plus your private taste adjustment

Then, for each bidder, a private taste adjustment will be drawn between 0 and {{private}}, with all values equally likely. Your value for the prize is equal to the common value plus your private taste adjustment. You will not learn the common value or your private taste adjustment separately. This means that each person in your group may have a different ...

work page

[2] [5]

Your value for the prize is equal to the common value plus your private taste adjustment

Then, for each bidder, a private taste adjustment will be drawn between 0 and {{private}}, with all values equally likely. Your value for the prize is equal to the common value plus your private taste adjustment. You will not learn the common value or your private taste adjustment separately. This means that each person in your group may have a different ...

work page

[3] [6]

First we will randomly draw a common value between {{ common_low}} and {{common_high}}, with all values equally likely

work page

[4] [7]

Your value for the prize is equal to the common value plus your private taste adjustment

Then, for each bidder, a private taste adjustment will be drawn between 0 and {{private}}, with all values equally likely. Your value for the prize is equal to the common value plus your private taste adjustment. You will not learn the common value or your private taste adjustment separately. This means that each person in your group may have a different ...

work page

[5] [8]

For each round, a common value will be drawn between {{ common_low}} and {{common_high}}, with all values equally likely to be drawn

work page

[6] [9]

proxy bidding

For each person, a private noisy adjustment will be drawn between -{{private}} and {{private}}, with all values equally likely to be drawn. We will tell you your perceived value, the sum of the common value and the private noise adjustment. However, everyone’s 49 true value for the prize is equal to the shared common value . After learning your perceived ...

work page

[7] [2025]

kelly is a warm person, joseph is a role model

Automated mechanism design: A survey. ACM SIGecom Exchanges 22, 2 (2025), 102–120. Constantinos Daskalakis, Ian Gemp, Yanchen Jiang, Renato Paes Leme, Christos Papadimitriou, and Geor- gios Piliouras. 2024. Charting the Shapes of Stories with Game Theory.arXiv preprint arXiv:2412.05747 (2024). Emmanuel Dechenaux, Dan Kovenock, and Roman M Sheremeta. 2015....

arXiv 2025