Testing a Purportedly More Learnable Auction Mechanism

Katherine L Milkman,Gregory M Barron,James Burns,David C Parkes,Kagan Tumer

doi:10.2139/ssrn.1090323

Katherine L Milkman, Gregory M Barron + Show 3 more

Open Access

https://doi.org/10.2139/ssrn.1090323

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Abstract

We describe an auction mechanism in the class of Groves mechanisms that has received attention in the computer science literature because of its theoretical property of being more than the standard second price auction mechanism. We bring this which we refer to as the second price auction mechanism, into the laboratory to determine whether it helps human subjects learn to play their optimal strategy faster than the standard second price auction mechanism. Contrary to earlier results within computer science using simulated reinforcement learning agents, we find that both in settings where subjects are given complete information about auction payoff rules and in settings where they are given no information about auction payoff rules, subjects converge on playing their optimal strategy significantly faster in sequential auctions conducted with a standard second price auction mechanism than with a clamped second price auction mechanism. We conclude that while it is important for mechanism designers to think more about creating learnable mechanisms, the clamped second price auction mechanism in fact produces slower learning in human subjects than the standard second price auction mechanism. Our results also serve to highlight differences in behavior between simulated agents and human bidders that mechanism designers should take into account before placing too much faith in simulations to test the performance of mechanisms intended for human use.

Highlights

Each year, auctions are used to determine how billions of dollars of goods and services will be allocated across the globe
Contrary to earlier results within computer science using simulated reinforcement learning agents, we find that both in settings where subjects are given complete information about auction payoff rules and in settings where they are given no information about auction payoff rules, subjects converge on playing their optimal strategy significantly faster in sequential auctions conducted with a standard second price auction mechanism than with a clamped second price auction mechanism
Our findings suggest that both in settings where subjects are given complete information about auction payoff rules and in settings where they are given no information about auction payoff rules, subjects converge on playing their optimal strategy significantly faster in sequential auctions conducted with a standard second price auction mechanism than in auctions conducted with a clamped second price auction mechanism

Summary

Introduction

Auctions are used to determine how billions of dollars of goods and services will be allocated across the globe. (2) The difference between the payment a bidder would owe given any two, different bids is equal to the effect of the resulting change in allocation (if any) on all other bidders’ reported values These properties make truthful revelation of preferences a dominant strategy in such a mechanism, and they ensure that allocations are efficient (Groves, 1973). The auction mechanisms studied in this paper differ only in the choice of this function,

The Standard Second Price Auction Mechanism

Learning in Auctions

Study One

Results

Findings

A Study of Decision Making in Auction Games