Name
Abstract | ||
|---|---|---|
One of the biggest challenges facing behavioral economics is the lack of a single theoretical framework that is capable of directly utilizing all types of behavioral data. One of the biggest challenges of game theory is the lack of a framework for making predictions and designing markets in a manner that is consistent with the axioms of decision theory. An approach in which solution concepts are distribution-valued rather than set-valued (i.e. equilibrium theory) has both capabilities. We call this approach Predictive Game Theory (or PGT). This paper outlines a general Bayesian approach to PGT. It also presents one simple example to illustrate the way in which this approach differs from equilibrium approaches in both prediction and mechanism design settings. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-12079-4_39 | SBP |
Keywords | Field | DocType |
statistical approach,behavioral data,mechanism design,equilibrium approach,approach predictive game theory,single theoretical framework,decision theory,equilibrium theory,general bayesian approach,behavioral economics,biggest challenge,game theory,solution concept,bayesian approach | Decision rule,Strategy,Computer science,Implementation theory,Operations research,Mechanism design,Decision theory,Game theory,Artificial intelligence,Nash equilibrium,Positive political theory,Machine learning | Conference |
Volume | ISSN | ISBN |
6007 | 0302-9743 | 3-642-12078-4 |
Citations | PageRank | References |
1 | 0.42 | 2 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| David H. Wolpert | 1 | 4334 | 591.07 |
| James W. Bono | 2 | 16 | 2.29 |