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DC Field | Value | Language |
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dc.contributor.author | Francisco Zapata | en_US |
dc.contributor.author | Olga Kosheleva | en_US |
dc.contributor.author | Vladik Kreinovich | en_US |
dc.contributor.author | Thongchai Dumrongpokaphan | en_US |
dc.date.accessioned | 2018-09-05T04:26:26Z | - |
dc.date.available | 2018-09-05T04:26:26Z | - |
dc.date.issued | 2018-01-01 | en_US |
dc.identifier.issn | 16113349 | en_US |
dc.identifier.issn | 03029743 | en_US |
dc.identifier.other | 2-s2.0-85044004169 | en_US |
dc.identifier.other | 10.1007/978-3-319-75429-1_3 | en_US |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85044004169&origin=inward | en_US |
dc.identifier.uri | http://cmuir.cmu.ac.th/jspui/handle/6653943832/58578 | - |
dc.description.abstract | © 2018, Springer International Publishing AG, part of Springer Nature. At first glance, it seems to make sense to conclude that when a 1 dollar reward tomorrow is equivalent to a D< 1 dollar reward today, the day-after-tomorrow’s 1 dollar reward would be equivalent to D· D= D2dollars today, and, in general, a reward after time t is equivalent to D(t) = Dtdollars today. This exponential discounting function D(t) was indeed proposed by the economists, but it does not reflect the actual human behavior. Indeed, according to this formula, the effect of distant future events is negligible, and thus, it would be reasonable for a person to take on huge loans or get engaged in unhealthy behavior even when the long-term consequences will be disastrous. In real life, few people behave like that, since the actual empirical discounting function is different: it is hyperbolic D(t) = 1/ (1 + k· t). In this paper, we use symmetry ideas to explain this empirical phenomenon. | en_US |
dc.subject | Computer Science | en_US |
dc.subject | Mathematics | en_US |
dc.title | Do It Today or Do It Tomorrow: Empirical Non-exponential Discounting Explained by Symmetry Ideas | en_US |
dc.type | Book Series | en_US |
article.title.sourcetitle | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | en_US |
article.volume | 10758 LNAI | 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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