Gregory M. Barron
There are 2 articles for this faculty member.
Making the Gambler's Fallacy Disappear: The Role of Experience
| Authors: | Gregory M. Barron and Stephen Leider |
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| Published: | October 16, 2008 |
| Paper Release Date: | September 2008 |
| Feature: | Working Papers |
The Gambler's Fallacy refers to the belief that chance is a self correcting process. The longer the random run of one outcome, the stronger the belief that the opposite outcome is due to appear. This paper asks whether the way we acquire information, by sequential experience or by simultaneous description, plays a critical role in the emergence of the bias in a binary prediction task (betting on red or black roulette outcomes, for example). The results show that the fallacy only occurs when decision makers experience outcomes over time and not when past outcomes are revealed all at once. The question is interesting since several recent papers on decisions from experience and descriptions suggest that the way people acquire information can have a significant effect on behavior.
Testing a Purportedly More Learnable Auction Mechanism
| Authors: | Katherine L. Milkman, James Burns, David C. Parkes, Gregory M. Barron, and Kagan Tumer |
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| Published: | March 3, 2008 |
| Paper Release Date: | February 2008 |
| Feature: | Working Papers |
Each year, auctions are used to determine how billions of dollars of goods and services will be allocated across the globe. On eBay alone, $52.5 billion in merchandise was exchanged in 2.4 billion auctions conducted during fiscal year 2006. Considerable attention has been paid in the academic literature to the question of how to design auctions with efficient allocation and revenue-maximizing properties. However, in part because auction rules are typically published and standard theory assumes economic agents are capable of computing optimal strategies from published rules, little attention has been paid to the question of how to design auctions whose optimal strategies are easy to learn. Evidence suggests that even when auction rules are published and dominant strategies exist, people nonetheless struggle and sometimes fail to learn to play their optimal strategy. As a result, the authors argue that the question of how to design a learnable, strategy-proof auction mechanism is an important one.
