Strategic bidding in multi-object auctions

Last registered on December 02, 2022

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
December 02, 2022, 5:14 AM EST

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

Locations

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Primary Investigator

Affiliation
Nuffield College and Department of Economics, University of Oxford

Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2022-09-16
End date
2023-01-31
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. 2022. "Strategic bidding in multi-object auctions." AEA RCT Registry. December 02. https://doi.org/10.1257/rct.9924-1.8
Sponsors & Partners

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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
2023-01-31

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

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
Not available
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?
No

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.
IRB

Institutional Review Boards (IRBs)

IRB Name
CESS Ethics Committee, Nuffield College
IRB Approval Date
2022-08-09
IRB Approval Number
N/A
IRB Name
Department’s Research Ethics Committee, University of Oxford
IRB Approval Date
2022-07-19
IRB Approval Number
ECONCIA21-22-46
Analysis Plan

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