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Record W2998954198

Informed seller problem : signaling, information design, and mechanism design

2019· dissertation· en· W2998954198 on OpenAlexfundno aff
Yanlin Chen

Bibliographic record

VenueUTS ePRESS (University of Technology Sydney) · 2019
Typedissertation
Languageen
FieldDecision Sciences
TopicAuction Theory and Applications
Canadian institutionsnot available
FundersQueen's UniversityUniversity of Technology SydneyAustralian GovernmentNew York University Abu Dhabi
KeywordsOutcome (game theory)Mechanism designPrivate information retrievalMicroeconomicsPaymentProfit (economics)Information asymmetryValue (mathematics)Mechanism (biology)EconomicsBusinessMathematical economicsComputer scienceComputer security
DOInot available

Abstract

fetched live from OpenAlex

This thesis studies an informed seller problem in which the seller tries to signal her private information through dierent channels-information disclosure, selling mechanism and return policy.Chapter 1 analyzes the signalling eect of information disclosure and price posting.Any separating equilibria must have the two types of seller setting dierent disclosure rules as well as dierent prices.Furthermore, the outcome that survives the intuitive criterion always exists and is unique.This equilibrium outcome is separating, for which a closed-form solution is provided.The signaling concern forces the high-type seller to disclose an ine cient amount of information and charge a higher price, resulting in fewer sales and lower prot.A regulation on minimal quality could potentially damage social welfare.In chapter 2, the seller is allowed to design a grand mechanism in which she herself participates in addition to information disclosure.The RSW (Rothschild-Stiglitz-Wilson) mechanism is fully characterized, in which each type of seller separates at the lowest cost.In this mechanism, the low-type seller sells to the buyer with certainty and leaves zero surplus to the buyer.The high-type seller discloses to the buyer whether his value is above a cuto, sets a payment dierence equal to the conditional expected value, and provides a nonnegative bonus.Furthermore, the RSW mechanism can always be supported as a PBE and its outcome is the unique PBE outcome under certain conditions.Finally, the RSW mechanism always survives the Intuitive criterion, and is the unique one under certain conditions.Chapter 3 studies an second-price auction with return policies.It starts with binary type.In the separating equilibria, the high-type seller's return policy needs to be generous enough to deter the low-type seller from mimicking.Notably, a better return policy may not correspond to a better type.In the pooling equilibria, the return policy cannot be too generous.All separating equilibria have the same outcome and all survive Eso and Schummer's [24] credible deviation criterion while all pooling equilibria fail.Separation is costless and e cient.Similar results apply when sellers have multiple types.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.773
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.036
GPT teacher head0.280
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2019
Admission routes1
Has abstractyes

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