|
| 1 | +# Do Auctioneers Pick Optimal Reserve Prices? |
| 2 | + |
| 3 | +**Authors:** Andrew M. Davis, Elena Katok, Anthony M. Kwasnica |
| 4 | +**Year:** 2011 |
| 5 | + |
| 6 | +--- |
| 7 | + |
| 8 | +## Description |
| 9 | + |
| 10 | +This study investigates how auctioneers set reserve prices in second-price sealed-bid auctions (described to sellers as English auctions). A well-established theoretical result, assuming risk neutrality of the seller, is that the optimal reserve price should not depend on the number of participating bidders (Roger B. Myerson 1981). In a set of controlled laboratory experiments, seller behavior often deviates from this theoretical benchmark. |
| 11 | + |
| 12 | +Specifically, this study tests the Cuberoot distribution treatment with bidder counts in {1, 4, 7, 10} under the NoInfo condition. Computerized bidders follow the weakly dominant strategy of bidding their private valuations, drawn from the Cuberoot distribution F(v) = (v/100)^(1/3) with mean 25 and support [0, 100]. The seller's personal valuation is zero, and the risk-neutral optimal reserve price is 42. |
| 13 | + |
| 14 | +The key finding: sellers systematically increase their reserve prices as the number of bidders grows (Pearson r = 0.42, p < .001), contrary to standard auction theory. |
| 15 | + |
| 16 | +LLM simulation baselines for this experiment are done and reported in [Feng et al., "Noise, Adaptation, and Strategy: Assessing LLM Fidelity in Decision-Making," EMNLP 2025](https://aclanthology.org/2025.emnlp-main.391.pdf). Baselines from GPT-4o and Claude Sonnet models are available in `benchmark/`. |
| 17 | + |
| 18 | +## Participants |
| 19 | + |
| 20 | +- **N:** 40 |
| 21 | +- **Population:** University students |
| 22 | + |
| 23 | +## Replicated tests (human data) |
| 24 | + |
| 25 | +Due to the unavailability of the original human experiment data (Davis et al. 2011), we use the replicated experiment data (Davis et al. 2023) instead. |
| 26 | + |
| 27 | +- **Pearson correlation (reserve price vs. number of bidders)** |
| 28 | + Reported: r = 0.42, p < .001 |
| 29 | + Significance level: 0.05 |
| 30 | + |
| 31 | +## Effect sizes & auxiliary statistics |
| 32 | + |
| 33 | +- F1: Mean reserve prices by number of bidders (Cuberoot distribution, NoInfo): |
| 34 | + 1 bidder: 14.85 (SD = 19.15, n = 716), |
| 35 | + 4 bidders: 24.30 (SD = 17.90, n = 566), |
| 36 | + 7 bidders: 32.93 (SD = 21.25, n = 458), |
| 37 | + 10 bidders: 39.50 (SD = 25.12, n = 660). |
| 38 | + Overall: mean = 27.31, SD = 23.23, sell-through rate = 0.74, mean profit = 32.83. |
| 39 | + Risk-neutral optimal reserve price = 42. |
| 40 | + Raw data present in `ground_truth.json`. |
| 41 | + |
| 42 | +## Files |
| 43 | + |
| 44 | +**source/** |
| 45 | +- ground_truth.json |
| 46 | +- materials/auction_instructions.json |
| 47 | +- metadata.json |
| 48 | +- specification.json |
| 49 | + |
| 50 | +**scripts/** |
| 51 | +- config.py |
| 52 | +- evaluator.py |
| 53 | +- stats_lib.py |
| 54 | +- study_utils.py |
| 55 | + |
| 56 | +**benchmark/** |
| 57 | +- human_data/auction_human_data.csv |
| 58 | +- llm_data |
0 commit comments