Economics, Psychology, and Social Dynamics of Consumer Bidding in Auctions ∗
Bibliographic record
Abstract
With increasing numbers of consumers in auction marketplaces, we highlight some recent approaches that bring additional economic, social, and psychological factors to bear on existing economic theory to better understand ∗ This paper is based on the special session at the 6th Triennial Invitational Choice Symposium, University of Colorado Boulder, June 2004 (co-chaired by the first two authors). † Corresponding author. 402 CHEEMA ET AL. and explain consumers ’ behavior in auctions. We also highlight specific research streams that could contribute towards enriching existing economic models of bidding behavior in emerging market mechanisms. Keywords: auctions, bidding, economic psychology, social dynamics, experimental economics The past decade has seen the advent and growth of online auction marketplaces, with online auction revenues expected to reach $36 billion by the year 2007 (C2C and B2C, (Laudon and Traver, 2004, p. 784). Study of specific auction formats for the past several decades has produced rich normative economic theories of rational buyers ’ and sellers ’ behavior (see Klemperer, 1999 for a review). A majority of these theories are developed for rational
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".