Decision Markets with Good Incentives

DSpace/Manakin Repository

Decision Markets with Good Incentives

Citable link to this page

 

 
Title: Decision Markets with Good Incentives
Author: Chen, Yiling; Kash, Ian; Ruberry, Michael Edward; Shnayder, Victor

Note: Order does not necessarily reflect citation order of authors.

Citation: Chen, Yiling, Ian Kash, Mike Ruberry, and Victor Shnayder. 2011. "Decision Markets with Good Incentives." Lecture Notes in Computer Science 7090: 72-83.
Full Text & Related Files:
Abstract: Decision markets both predict and decide the future. They allow experts to predict the effects of each of a set of possible actions, and after reviewing these predictions a decision maker selects an action to perform. When the future is independent of the market, strictly proper scoring rules myopically incentivize experts to predict consistent with their beliefs, but this is not generally true when a decision is to be made. When deciding, only predictions for the chosen action can be evaluated for their accuracy since the other predictions become counterfactuals. This limitation can make some actions more valuable than others for an expert, incentivizing the expert to mislead the decision maker. We construct and characterize decision markets that are – like prediction markets using strictly proper scoring rules – myopic incentive compatible. These markets require the decision maker always risk taking every available action, and reducing this risk increases the decision maker’s worst-case loss. We also show a correspondence between strictly proper decision markets and strictly proper sets of prediction markets, creating a formal connection between the incentives of prediction and decision markets.
Published Version: doi:10.1007/978-3-642-25510-6_7
Terms of Use: This article is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#OAP
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:11693950
Downloads of this work:

Show full Dublin Core record

This item appears in the following Collection(s)

 
 

Search DASH


Advanced Search
 
 

Submitters