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DC Field | Value | Language |
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dc.contributor.author | Olga Kosheleva | en_US |
dc.contributor.author | Vladik Kreinovich | en_US |
dc.contributor.author | Songsak Sriboonchitta | en_US |
dc.date.accessioned | 2018-09-05T03:35:16Z | - |
dc.date.available | 2018-09-05T03:35:16Z | - |
dc.date.issued | 2017-02-01 | en_US |
dc.identifier.issn | 1860949X | en_US |
dc.identifier.other | 2-s2.0-85012267327 | en_US |
dc.identifier.other | 10.1007/978-3-319-50742-2_5 | en_US |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85012267327&origin=inward | en_US |
dc.identifier.uri | http://cmuir.cmu.ac.th/jspui/handle/6653943832/57123 | - |
dc.description.abstract | © Springer International Publishing AG 2017. Traditional decision theory assumes that for every two alternatives, people always make the same (deterministic) choice. In practice, people’s choices are often probabilistic, especially for similar alternatives: the same decision maker can sometimes select one of them and sometimes the other one. In many practical situations, an adequate description of this probabilistic choice can be provided by a logit model proposed by 2001 Nobelist D. McFadden. In this model, the probability of selecting an alternative a is proportional to exp(β · u(a)), where u(a) is the alternative’s utility. Recently, however, empirical evidence appeared that shows that in some situations, we need to go beyond McFadden’s formulas. In this paper, we use natural symmetries to come up with an appropriate generalization of McFadden’s formulas. | en_US |
dc.subject | Computer Science | en_US |
dc.title | Econometric models of probabilistic choice: beyond mcfadden’s formulas | en_US |
dc.type | Book Series | en_US |
article.title.sourcetitle | Studies in Computational Intelligence | en_US |
article.volume | 692 | en_US |
article.stream.affiliations | University of Texas at El Paso | en_US |
article.stream.affiliations | Chiang Mai University | en_US |
Appears in Collections: | CMUL: Journal Articles |
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