The descriptive and predictive adequacy of theories of decision making under uncertainty/ambiguity
Abstract In this paper we examine the performance of theories of decision making under uncertainty/ambiguity from the perspective of their descriptive and predictive power. To this end, we employ an innovative experimental design which enables us to reproduce ambiguity in the laboratory in a transpa...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Hey, John D. [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2010 |
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| Schlagwörter: |
(Gilboa and Schmeidler) MaxMin EU |
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| Anmerkung: |
© Springer Science+Business Media, LLC 2010 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of risk and uncertainty - Springer US, 1988, 41(2010), 2 vom: 28. Juli, Seite 81-111 |
|---|---|
| Übergeordnetes Werk: |
volume:41 ; year:2010 ; number:2 ; day:28 ; month:07 ; pages:81-111 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11166-010-9102-0 |
|---|
| Katalog-ID: |
OLC206376719X |
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| 520 | |a Abstract In this paper we examine the performance of theories of decision making under uncertainty/ambiguity from the perspective of their descriptive and predictive power. To this end, we employ an innovative experimental design which enables us to reproduce ambiguity in the laboratory in a transparent and non-probabilistic way. We find that judging theories on the basis of their theoretical appeal, or on their ability to do well in terms of estimation, is not the same as judging them on the basis of their predictive power. We find that the models that perform better in an aggregate sense are Gilboa and Schmeidler’s MaxMin and MaxMax Expected Utility Models, and Ghiradarto et al.’s Alpha Model, implying that more elegant theoretical models do not perform as well as relatively simple models. This suggests that decision-makers, when confronted with a difficult problem, try to simplify it, rather than apply a sophisticated decision rule. | ||
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10.1007/s11166-010-9102-0 doi (DE-627)OLC206376719X (DE-He213)s11166-010-9102-0-p DE-627 ger DE-627 rakwb eng 300 330 150 VZ 3,2 5,2 ssgn Hey, John D. verfasserin aut The descriptive and predictive adequacy of theories of decision making under uncertainty/ambiguity 2010 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2010 Abstract In this paper we examine the performance of theories of decision making under uncertainty/ambiguity from the perspective of their descriptive and predictive power. To this end, we employ an innovative experimental design which enables us to reproduce ambiguity in the laboratory in a transparent and non-probabilistic way. We find that judging theories on the basis of their theoretical appeal, or on their ability to do well in terms of estimation, is not the same as judging them on the basis of their predictive power. We find that the models that perform better in an aggregate sense are Gilboa and Schmeidler’s MaxMin and MaxMax Expected Utility Models, and Ghiradarto et al.’s Alpha Model, implying that more elegant theoretical models do not perform as well as relatively simple models. This suggests that decision-makers, when confronted with a difficult problem, try to simplify it, rather than apply a sophisticated decision rule. Ambiguity Bingo blower Choquet expected utility Decision field theory Decision making (Subjective) expected utility (Gilboa and Schmeidler) MaxMin EU (Gilboa and Schmeidler) MaxMax EU (Ghirardato) alpha model MaxMin MaxMax Minimum regret Prospect theory Uncertainty Lotito, Gianna aut Maffioletti, Anna aut Enthalten in Journal of risk and uncertainty Springer US, 1988 41(2010), 2 vom: 28. Juli, Seite 81-111 (DE-627)129253170 (DE-600)59837-9 (DE-576)017944279 0895-5646 nnns volume:41 year:2010 number:2 day:28 month:07 pages:81-111 https://doi.org/10.1007/s11166-010-9102-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW GBV_ILN_11 GBV_ILN_22 GBV_ILN_26 GBV_ILN_31 GBV_ILN_70 GBV_ILN_231 GBV_ILN_2005 GBV_ILN_2012 GBV_ILN_4012 GBV_ILN_4028 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4193 GBV_ILN_4305 GBV_ILN_4318 AR 41 2010 2 28 07 81-111 |
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10.1007/s11166-010-9102-0 doi (DE-627)OLC206376719X (DE-He213)s11166-010-9102-0-p DE-627 ger DE-627 rakwb eng 300 330 150 VZ 3,2 5,2 ssgn Hey, John D. verfasserin aut The descriptive and predictive adequacy of theories of decision making under uncertainty/ambiguity 2010 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2010 Abstract In this paper we examine the performance of theories of decision making under uncertainty/ambiguity from the perspective of their descriptive and predictive power. To this end, we employ an innovative experimental design which enables us to reproduce ambiguity in the laboratory in a transparent and non-probabilistic way. We find that judging theories on the basis of their theoretical appeal, or on their ability to do well in terms of estimation, is not the same as judging them on the basis of their predictive power. We find that the models that perform better in an aggregate sense are Gilboa and Schmeidler’s MaxMin and MaxMax Expected Utility Models, and Ghiradarto et al.’s Alpha Model, implying that more elegant theoretical models do not perform as well as relatively simple models. This suggests that decision-makers, when confronted with a difficult problem, try to simplify it, rather than apply a sophisticated decision rule. Ambiguity Bingo blower Choquet expected utility Decision field theory Decision making (Subjective) expected utility (Gilboa and Schmeidler) MaxMin EU (Gilboa and Schmeidler) MaxMax EU (Ghirardato) alpha model MaxMin MaxMax Minimum regret Prospect theory Uncertainty Lotito, Gianna aut Maffioletti, Anna aut Enthalten in Journal of risk and uncertainty Springer US, 1988 41(2010), 2 vom: 28. Juli, Seite 81-111 (DE-627)129253170 (DE-600)59837-9 (DE-576)017944279 0895-5646 nnns volume:41 year:2010 number:2 day:28 month:07 pages:81-111 https://doi.org/10.1007/s11166-010-9102-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW GBV_ILN_11 GBV_ILN_22 GBV_ILN_26 GBV_ILN_31 GBV_ILN_70 GBV_ILN_231 GBV_ILN_2005 GBV_ILN_2012 GBV_ILN_4012 GBV_ILN_4028 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4193 GBV_ILN_4305 GBV_ILN_4318 AR 41 2010 2 28 07 81-111 |
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10.1007/s11166-010-9102-0 doi (DE-627)OLC206376719X (DE-He213)s11166-010-9102-0-p DE-627 ger DE-627 rakwb eng 300 330 150 VZ 3,2 5,2 ssgn Hey, John D. verfasserin aut The descriptive and predictive adequacy of theories of decision making under uncertainty/ambiguity 2010 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2010 Abstract In this paper we examine the performance of theories of decision making under uncertainty/ambiguity from the perspective of their descriptive and predictive power. To this end, we employ an innovative experimental design which enables us to reproduce ambiguity in the laboratory in a transparent and non-probabilistic way. We find that judging theories on the basis of their theoretical appeal, or on their ability to do well in terms of estimation, is not the same as judging them on the basis of their predictive power. We find that the models that perform better in an aggregate sense are Gilboa and Schmeidler’s MaxMin and MaxMax Expected Utility Models, and Ghiradarto et al.’s Alpha Model, implying that more elegant theoretical models do not perform as well as relatively simple models. This suggests that decision-makers, when confronted with a difficult problem, try to simplify it, rather than apply a sophisticated decision rule. Ambiguity Bingo blower Choquet expected utility Decision field theory Decision making (Subjective) expected utility (Gilboa and Schmeidler) MaxMin EU (Gilboa and Schmeidler) MaxMax EU (Ghirardato) alpha model MaxMin MaxMax Minimum regret Prospect theory Uncertainty Lotito, Gianna aut Maffioletti, Anna aut Enthalten in Journal of risk and uncertainty Springer US, 1988 41(2010), 2 vom: 28. Juli, Seite 81-111 (DE-627)129253170 (DE-600)59837-9 (DE-576)017944279 0895-5646 nnns volume:41 year:2010 number:2 day:28 month:07 pages:81-111 https://doi.org/10.1007/s11166-010-9102-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW GBV_ILN_11 GBV_ILN_22 GBV_ILN_26 GBV_ILN_31 GBV_ILN_70 GBV_ILN_231 GBV_ILN_2005 GBV_ILN_2012 GBV_ILN_4012 GBV_ILN_4028 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4193 GBV_ILN_4305 GBV_ILN_4318 AR 41 2010 2 28 07 81-111 |
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10.1007/s11166-010-9102-0 doi (DE-627)OLC206376719X (DE-He213)s11166-010-9102-0-p DE-627 ger DE-627 rakwb eng 300 330 150 VZ 3,2 5,2 ssgn Hey, John D. verfasserin aut The descriptive and predictive adequacy of theories of decision making under uncertainty/ambiguity 2010 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2010 Abstract In this paper we examine the performance of theories of decision making under uncertainty/ambiguity from the perspective of their descriptive and predictive power. To this end, we employ an innovative experimental design which enables us to reproduce ambiguity in the laboratory in a transparent and non-probabilistic way. We find that judging theories on the basis of their theoretical appeal, or on their ability to do well in terms of estimation, is not the same as judging them on the basis of their predictive power. We find that the models that perform better in an aggregate sense are Gilboa and Schmeidler’s MaxMin and MaxMax Expected Utility Models, and Ghiradarto et al.’s Alpha Model, implying that more elegant theoretical models do not perform as well as relatively simple models. This suggests that decision-makers, when confronted with a difficult problem, try to simplify it, rather than apply a sophisticated decision rule. Ambiguity Bingo blower Choquet expected utility Decision field theory Decision making (Subjective) expected utility (Gilboa and Schmeidler) MaxMin EU (Gilboa and Schmeidler) MaxMax EU (Ghirardato) alpha model MaxMin MaxMax Minimum regret Prospect theory Uncertainty Lotito, Gianna aut Maffioletti, Anna aut Enthalten in Journal of risk and uncertainty Springer US, 1988 41(2010), 2 vom: 28. Juli, Seite 81-111 (DE-627)129253170 (DE-600)59837-9 (DE-576)017944279 0895-5646 nnns volume:41 year:2010 number:2 day:28 month:07 pages:81-111 https://doi.org/10.1007/s11166-010-9102-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW GBV_ILN_11 GBV_ILN_22 GBV_ILN_26 GBV_ILN_31 GBV_ILN_70 GBV_ILN_231 GBV_ILN_2005 GBV_ILN_2012 GBV_ILN_4012 GBV_ILN_4028 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4193 GBV_ILN_4305 GBV_ILN_4318 AR 41 2010 2 28 07 81-111 |
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Abstract In this paper we examine the performance of theories of decision making under uncertainty/ambiguity from the perspective of their descriptive and predictive power. To this end, we employ an innovative experimental design which enables us to reproduce ambiguity in the laboratory in a transparent and non-probabilistic way. We find that judging theories on the basis of their theoretical appeal, or on their ability to do well in terms of estimation, is not the same as judging them on the basis of their predictive power. We find that the models that perform better in an aggregate sense are Gilboa and Schmeidler’s MaxMin and MaxMax Expected Utility Models, and Ghiradarto et al.’s Alpha Model, implying that more elegant theoretical models do not perform as well as relatively simple models. This suggests that decision-makers, when confronted with a difficult problem, try to simplify it, rather than apply a sophisticated decision rule. © Springer Science+Business Media, LLC 2010 |
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Abstract In this paper we examine the performance of theories of decision making under uncertainty/ambiguity from the perspective of their descriptive and predictive power. To this end, we employ an innovative experimental design which enables us to reproduce ambiguity in the laboratory in a transparent and non-probabilistic way. We find that judging theories on the basis of their theoretical appeal, or on their ability to do well in terms of estimation, is not the same as judging them on the basis of their predictive power. We find that the models that perform better in an aggregate sense are Gilboa and Schmeidler’s MaxMin and MaxMax Expected Utility Models, and Ghiradarto et al.’s Alpha Model, implying that more elegant theoretical models do not perform as well as relatively simple models. This suggests that decision-makers, when confronted with a difficult problem, try to simplify it, rather than apply a sophisticated decision rule. © Springer Science+Business Media, LLC 2010 |
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Abstract In this paper we examine the performance of theories of decision making under uncertainty/ambiguity from the perspective of their descriptive and predictive power. To this end, we employ an innovative experimental design which enables us to reproduce ambiguity in the laboratory in a transparent and non-probabilistic way. We find that judging theories on the basis of their theoretical appeal, or on their ability to do well in terms of estimation, is not the same as judging them on the basis of their predictive power. We find that the models that perform better in an aggregate sense are Gilboa and Schmeidler’s MaxMin and MaxMax Expected Utility Models, and Ghiradarto et al.’s Alpha Model, implying that more elegant theoretical models do not perform as well as relatively simple models. This suggests that decision-makers, when confronted with a difficult problem, try to simplify it, rather than apply a sophisticated decision rule. © Springer Science+Business Media, LLC 2010 |
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