Access

You are not currently logged in.

Access your personal account or get JSTOR access through your library or other institution:

login

Log in to your personal account or through your institution.

If you need an accessible version of this item please contact JSTOR User Support

The Predictive Utility of Generalized Expected Utility Theories

David W. Harless and Colin F. Camerer
Econometrica
Vol. 62, No. 6 (Nov., 1994), pp. 1251-1289
Published by: The Econometric Society
DOI: 10.2307/2951749
Stable URL: http://www.jstor.org/stable/2951749
Page Count: 39
  • Read Online (Free)
  • Download ($10.00)
  • Subscribe ($19.50)
  • Cite this Item
If you need an accessible version of this item please contact JSTOR User Support
The Predictive Utility of Generalized Expected Utility Theories
Preview not available

Abstract

Many alternative theories have been proposed to explain violations of expected utility (EU) theory observed in experiments. Several recent studies test some of these alternative theories against each other. Formal tests used to judge the theories usually count the number of responses consistent with the theory, ignoring systematic variation in responses that are inconsistent. We develop a maximum-likelihood estimation method which uses all the information in the data, creates test statistics that can be aggregated across studies, and enables one to judge the predictive utility--the fit and parsimony--of utility theories. Analyses of 23 data sets, using several thousand choices, suggest a menu of theories which sacrifice the least parsimony for the biggest improvement in fit. The menu is: mixed fanning, prospect theory, EU, and expected value. Which theories are best is highly sensitive to whether gambles in a pair have the same support (EU fits better) or not (EU fits poorly). Our method may have application to other domains in which various theories predict different subsets of choices (e.g., refinements of Nash equilibrium in noncooperative games).

Page Thumbnails

  • Thumbnail: Page 
1251
    1251
  • Thumbnail: Page 
1252
    1252
  • Thumbnail: Page 
1253
    1253
  • Thumbnail: Page 
1254
    1254
  • Thumbnail: Page 
1255
    1255
  • Thumbnail: Page 
1256
    1256
  • Thumbnail: Page 
1257
    1257
  • Thumbnail: Page 
1258
    1258
  • Thumbnail: Page 
1259
    1259
  • Thumbnail: Page 
1260
    1260
  • Thumbnail: Page 
1261
    1261
  • Thumbnail: Page 
1262
    1262
  • Thumbnail: Page 
1263
    1263
  • Thumbnail: Page 
1264
    1264
  • Thumbnail: Page 
1265
    1265
  • Thumbnail: Page 
1266
    1266
  • Thumbnail: Page 
1267
    1267
  • Thumbnail: Page 
1268
    1268
  • Thumbnail: Page 
1269
    1269
  • Thumbnail: Page 
1270
    1270
  • Thumbnail: Page 
1271
    1271
  • Thumbnail: Page 
1272
    1272
  • Thumbnail: Page 
1273
    1273
  • Thumbnail: Page 
1274
    1274
  • Thumbnail: Page 
1275
    1275
  • Thumbnail: Page 
1276
    1276
  • Thumbnail: Page 
1277
    1277
  • Thumbnail: Page 
1278
    1278
  • Thumbnail: Page 
1279
    1279
  • Thumbnail: Page 
1280
    1280
  • Thumbnail: Page 
1281
    1281
  • Thumbnail: Page 
1282
    1282
  • Thumbnail: Page 
1283
    1283
  • Thumbnail: Page 
1284
    1284
  • Thumbnail: Page 
1285
    1285
  • Thumbnail: Page 
1286
    1286
  • Thumbnail: Page 
1287
    1287
  • Thumbnail: Page 
1288
    1288
  • Thumbnail: Page 
1289
    1289