Book contents
- Frontmatter
- Contents
- Contributors
- Editors' Note
- 1 Dynamic Mechanism Design: Robustness and Endogenous Types
- 2 Learning, Experimentation, and Information Design
- 3 Dynamic Selection and Reclassification Risk: Theory and Empirics
- 4 Discussion of “Agency Problems”
- 5 Recent Developments in Matching Theory and Their Practical Applications
- 6 What Really Matters in Designing School Choice Mechanisms
- 7 Networks and Markets
- 8 Econometrics of Network Models
- 9 Networks in Economics: Remarks
- Index
4 - Discussion of “Agency Problems”
Published online by Cambridge University Press: 27 October 2017
- Frontmatter
- Contents
- Contributors
- Editors' Note
- 1 Dynamic Mechanism Design: Robustness and Endogenous Types
- 2 Learning, Experimentation, and Information Design
- 3 Dynamic Selection and Reclassification Risk: Theory and Empirics
- 4 Discussion of “Agency Problems”
- 5 Recent Developments in Matching Theory and Their Practical Applications
- 6 What Really Matters in Designing School Choice Mechanisms
- 7 Networks and Markets
- 8 Econometrics of Network Models
- 9 Networks in Economics: Remarks
- Index
Summary
Apart from their high quality, these two papers share a common thread: they emphasize learning. Now learning can take place in a wide variety of ways. It can be a purposeful and costly activity; or information can flow in exogenously. One can learn from one's own experiments, as well as from observing others’ experimenting and its results; or learning can be informed by recommendations from experts. In a market situation, one can learn from competitors, in ways that are shaped both by their strategies and by regulations. Learning also builds on what one already knows; in modelling terms, some information about the world must be assumed to be known. I will return to this perhaps obvious point in my conclusion.
In the models surveyed by Hörner and Skrzypacz, learning is costly, as experimenting with a risky choice forgoes the benefits of safer options. In so far as each agent can also learn from what (s)he observes on other agents experiments, this opens the door to free riding. But since other agents’ payoffs to experimenting depend on their current beliefs, it is sometimes useful to experiment in order to change these beliefs – “nudging” others to experiment. This strategic interaction between agents in turn opens the door for other parties to attempt to manipulate information accrual. The seller of a new experience good, for instance, can design the way it rewards early adopters for their reviews, or simply how it chooses to publish them.
Hendel's chapter builds on a long tradition in contract theory: it has principals learning about agents’ types over time. The main focus of the chapter is on the case when each agent's type changes exogenously over time, so that learning occurs on both sides of the relationship. Learning can still be asymmetric, both because the agent learns his type privately and/or because other principals may not observe what one principal has learnt. Unlike much of the theoretical literature on dynamic contracting, Hendel focuses on learning that is symmetric between the two parties in a relationship, but may be asymmetric between the various principals: my insurer learns my risk at the same time that I do, but her competitors may be equally well-informed (“symmetric learning” in the chapter) or not (“asymmetric learning”.) Given limited commitment, changes in the agent's type may give rise to reclassification risk: insurers are tempted to index premia to risk.
- Type
- Chapter
- Information
- Advances in Economics and EconometricsEleventh World Congress, pp. 130 - 137Publisher: Cambridge University PressPrint publication year: 2017