Name
Abstract | ||
|---|---|---|
When making a decision, a decision maker selects one of several possible actions and hopes to achieve a desirable outcome. To make a better decision, the decision maker often asks experts for advice. In this article, we consider two methods of acquiring advice for decision making. We begin with a method where one or more experts predict the effect of each action and the decision maker then selects an action based on the predictions. We characterize strictly proper decision making, where experts have an incentive to accurately reveal their beliefs about the outcome of each action. However, strictly proper decision making requires the decision maker use a completely mixed strategy to choose an action. To address this limitation, we consider a second method where the decision maker asks a single expert to recommend an action. We show that it is possible to elicit the decision maker’s most preferred action for a broad class of preferences of the decision maker, including when the decision maker is an expected value maximizer. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2556271 | ACM Trans. Economics and Comput. |
Keywords | Field | DocType |
social and behavioral sciences,decision making,prediction markets,information elicitation,general,market design,theory,decision markets,scoring rules,economics | Decision rule,Decision analysis,Optimal decision,Computer science,Decision field theory,R-CAST,Evidential decision theory,Management science,Decision engineering,Decision fatigue | Journal |
Volume | Issue | Citations |
2 | 2 | 0 |
PageRank | References | Authors |
0.34 | 17 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yiling Chen | 1 | 896 | 84.11 |
| Ian A. Kash | 2 | 393 | 35.84 |
| Michael Ruberry | 3 | 0 | 0.34 |
| Victor Shnayder | 4 | 1393 | 117.15 |