Study on the Price Formation Mechanism of Art Auctions from the Perspective of Behavioral Economics: The Influence of Anchoring Effect and Endowment Effect
Bibliographic record
Abstract
The art market is both unique and subjective. Particularly in art auctions, transaction prices not only reflect the value of the artwork but are also influenced by the psychological factors of the bidders. This paper systematically examines how the anchoring effect and endowment effect, as described in behavioral economics, contribute to high transaction prices in art auctions. The research focuses on traditional Chinese paintings, Impressionist works, and contemporary art, with data sourced from major art trading platforms such as Artron.net, Christie’s, and Sotheby’s. The results indicate that the anchoring effect significantly influences bidders’ offers through prior auction prices or pre-sale estimates, with this effect being more pronounced when the time between auctions is short. The endowment effect reveals that bidders assign greater psychological value to their preferred artworks, making them willing to pay higher prices. Overall, these behavioral biases notably impact the market pricing of artworks, driving auction prices higher. This study integrates behavioral economics, art management, and statistical analysis to offer practical recommendations for auction houses to optimize bidding strategies, for buyers and sellers to make informed decisions, and for market regulators to develop effective policies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".