Strategic bidding in multi-object auctions

Last registered on October 28, 2023

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Pre-Trial

Trial Information

General Information

Title

Strategic bidding in multi-object auctions

RCT ID

AEARCTR-0009924

Initial registration date

August 14, 2022

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published

August 18, 2022, 2:58 PM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Last updated

October 28, 2023, 4:03 AM EDT

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Country

United Kingdom of Great Britain and Northern Ireland

Region

Primary Investigator

Name

Simon Finster

Affiliation

Nuffield College and Department of Economics, University of Oxford

Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status

In development

Start date

2022-09-16

End date

2024-02-29

Keywords

Behavior, Other

Additional Keywords

Virtual lab, Auctions, Mutli-object auction, Strategic bidding, Strategic behaviour, Empirical econometrics, Experiment

JEL code(s)

C12, C72, C91, C92, D44, D47, D82

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

I study strategic bidding behaviour in three pay-as-bid multi-object auctions: a Product-Mix auction, a sequential auction, and a simultaneous auction. In a theoretical model, bidders are assumed to behave optimally, i.e. are maximising their expected surplus. This model predicts that, in equilibrium, the Product-Mix and the sequential format perform nearly identically with respect to bidder surplus, revenue, and welfare. The simultaneous auction, however, performs slightly worse than the other two formats. I test if these predictions hold up in a virtual lab experiment. I consider a bidding environment identical to the theoretical model of an asymmetric market, but also study a bidding environment with symmetric bidders, a more general setting for which current Bayes-Nash equilibrium models cannot make predictions.

External Link(s)

Registration Citation

Citation

Finster, Simon. 2023. "Strategic bidding in multi-object auctions." AEA RCT Registry. October 28. https://doi.org/10.1257/rct.9924-1.11

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Experimental Details

Interventions

Intervention(s)

Each subject is participates in several bidding rounds of a multi-object auction. Some subjects compete against computerised bidders and some subjects compete against other participants. All subjects also answer a survey on demographics, bidding experience, and complete additional tasks relating to numeracy and risk/ambiguity preferences.

Intervention Start Date

2022-09-16

Intervention End Date

2024-02-29

Primary Outcomes

Primary Outcomes (end points)

Expected bidder surplus, expected auctioneer's revenue, expected welfare

Primary Outcomes (explanation)

Expected bidder surplus, auctioneer's revenue, and welfare will be computed as described in detail in the pre-analysis plan.

Secondary Outcomes

Secondary Outcomes (end points)

Bids submitted by subjects

Secondary Outcomes (explanation)

Experimental Design

Each auction mechanism in a given environment corresponds to one treatment. I run a between-subject design, in which each subject is randomly assigned to one of six treatments: (Product-Mix auction, sequential auction, simultaneous auction) x (computerised opponents, human opponents). In the computerised environment, each subject competes against two computerised bidders for two virtual objects with induced values. The distribution of the computerised bidders' bids is known. In the human environment, subjects compete in groups of three for two virtual objects with induced values. The distribution of the human opponents' induced values is known to all subjects within the same group. In each treatment, subjects play a number of bidding rounds. The outcome of each round is shown to the subject at the end of each round and payments are disclosed at the end of the experiment.

Experimental Design Details

Randomization Method

Randomisation done by computer

Randomization Unit

random assignment of individuals to treatments; in some treatments, individuals are randomly selected into groups (sampling with replacement)

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

no clusters

Sample size: planned number of observations

6 treatments, 175-185 individuals per treatment, 20 observations from each individual = >21,000 observations

Sample size (or number of clusters) by treatment arms

185 individuals per treatment in computerised environments

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

The study is powered for pairwise equivalence tests between all considered auction formats in the computerised bidding environment, concerning the outcomes of bidder surplus, auctioneer's revenue, and welfare (all absolute values), respectively. The hypothesied effect is therefore zero. I choose a significance level of 0.05 and equivalence bounds of +/- 20% of the pooled standard deviation corresponding to each pairwise comparison. Power calulations (sample sizes) are presented in the pre-analysis plan for a power of 0.8 and 0.9 and for various scenarios of within-subject correlation. Choosing a sample size of 185 subjects per treatment conservatively allows for a serial correlation between rounds of up to 0.4. This is assumed to be a strict upper bound based on related experimental designs in the literature. The treatments with only human bidders are for exploratory analysis only, hence the sample size will be lower.

Supporting Documents and Materials

IRB