Does feedback really matter in one-shot first-price auctions?

Katuscak, Peter; Michelucci, Fabio; Zajíček, Miroslav

doi:10.1016/j.jebo.2015.08.002

Cited by 9 publications

(5 citation statements)

References 18 publications

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“…But it might also be that regret in auction experiments is not as strong as it has been found in earlier experiments. For example, Ratan and Wen () and also Katušcák, Michelucci, and Zajícek () find no effect of regret treatments concluding that “ loser feedback does not appear to be a good explanation of OB relative to the risk‐neutral Nash equilibrium ” (p. 26). It is, however, difficult to pinpoint why this difference in results exists as our experimental setting differs from that of the aforementioned papers in multiple ways ( computerized vs. human bidders, number of auction rounds, timing of feedback, etc.…”

Section: Discussionmentioning

confidence: 99%

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Risk Aversion and Overbidding in First Price Sealed Bid Auctions: New Experimental Evidence

2018

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Bidding above the risk‐neutral Nash equilibrium in first price sealed bid auctions has traditionally been ascribed to risk aversion. Later studies, however, offer other explanations and even argue that risk aversion plays no or a minor role. In a novel experimental design, we directly test the relationship between risk aversion and overbidding by systematically varying the distribution of risk attitudes in auction markets. We find a significant relationship between our measure of risk aversion and overbidding. (JEL D44, C91)

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Section: Discussionmentioning

confidence: 99%

“…Filiz‐Ozbay and Ozbay () go one step further and provide experimental evidence for “anticipated regret.” In this model, bidders anticipate winner and loser regret and, ex ante, adjust their bids accordingly. Katušcák, Michelucci, and Zajícek () find no evidence for anticipated regret though.…”

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confidence: 99%

Risk Aversion and Overbidding in First Price Sealed Bid Auctions: New Experimental Evidence

2018

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“…After all four bidders in an auction submitted their offers, each saw a results screen. The feedback structure is similar to Katuščák, Michelucci, and Zajiček (2015) and Ockenfels and Selten (2005, treatment NF), showing if they had won or not, their own offered price and quality, the auction's winning price and quality, their profits, and a table with their history of all past rounds. After viewing the results screen, the bidders moved to the next round.…”

Section: Protocolmentioning

confidence: 92%

The role of transparency in procurement: Revealed versus concealed scoring rules in sealed bid A + B auctions

Quiroga

Moritz

Guide

2020

J of Ops Management

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We investigate the impact of using a clear scoring rule in a sealed bid multi‐dimensional (A+B) procurement auction, as frequently used in government procurement. The central procurement agency in Chile (ChileCompra) asked for help to understand how concealing the scoring rule affected buyers. Using an experiment, we analyze the effect of transparently communicating the scoring rule on bidding outcomes by comparing the buyer's surplus and supplier profits when buyers expressly communicate the weight they place on a nonmonetary (B) attribute, versus when this information is concealed from bidders. In addition, we compare outcomes where the scoring rule is made visible only after the offers are submitted. If the scoring rule is not disclosed, outcomes are poorer for buyers, and sellers see their profits increase.

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“…Thus, the upper bound of the 95% confidence interval is 41 cedis, a 28% increase over the control group mean." Katuščák et al (2015) Lab "Note that, given the number of subjects we use in 4H and 4HR, if the effect of loser vs. minimal feedback identified by FO were real and if their data provides a good description of the idiosyncratic variance in bidding behavior, a two-tailed test would have a power of over 0.95 against the null hypothesis of no effect. If we instead use the variance implied by our data, the power of the test ranges from 0.73 to 0.95, depending on the protocol and the outcome measure."…”

Section: Appendix 4 Power / Sample Size Calculations In Jebomentioning

confidence: 99%

Sample Size Calculation in Economic Experiments

Grüner

2019

SSRN Journal

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Clinical studies and economic experiments are often conducted with randomized controlled trials. In clinical studies, power calculations are carried out as a standard. But what's about economic experiments? After describing the basic idea of the calculation procedure, I tackle the practice of sample size calculations in the realm of experimental economics by considering the publications of 5 economic journals in the period 2000-2018. These are two top-ranked economic journals (Quarterly Journals of Economics and American Economic Review), the leading field journals in the area of experimental economics (Experimental Economics) and behavioral sciences (Journal of Economic Behavior and Organization), and a leading field journal in environmental economics (Environmental and Resource Economics). In contrast to clinical drug trials, sample size calculations have rarely been carried out by experimental economists. But the number of power calculations has slightly increased in recent years, especially in the leading journals of economics. However, this can be partly explained by the fact that field experiments (in which scholars pay more attention to power analyses than in lab experiments these days) play an important role in these journals.

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Does feedback really matter in one-shot first-price auctions?

Cited by 9 publications

References 18 publications

Risk Aversion and Overbidding in First Price Sealed Bid Auctions: New Experimental Evidence

Risk Aversion and Overbidding in First Price Sealed Bid Auctions: New Experimental Evidence

The role of transparency in procurement: Revealed versus concealed scoring rules in sealed bid A + B auctions

Sample Size Calculation in Economic Experiments

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