Competition Alleviates Present Bias in Task Completion

• Published in: Web and Internet Economics Vol. 12495; pp. 266 - 279
• Main Authors: Saraf, Aditya, Karlin, Anna R., Morgenstern, Jamie
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2020; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Behavioral economics; Incentive design; Present bias
• ISBN: 3030649458; 9783030649456
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-64946-3_19

We build upon recent work by Kleinberg, Oren, and Raghavan [10–12] that considers present biased agents, who place more weight on costs they must incur now than costs they will incur in the future. They consider a graph theoretic model where agents must complete a task and show that present biased agents can take exponentially more expensive paths than optimal. We propose a theoretical model that adds competition into the mix – two agents compete to finish a task first. We show that, in a wide range of settings, a small amount of competition can alleviate the harms of present bias. This can help explain why biased agents may not perform so poorly in naturally competitive settings, and can guide task designers on how to protect present biased agents from harm. Our work thus paints a more positive picture than much of the existing literature on present bias.