Reporting probabilistic expectations with dynamic uncertainty about possible distributions
We study how experimental subjects report subjective probability distributions in the presence of ambiguity characterized by uncertainty over a fixed set of possible probability distributions generating future outcomes. Subjects observe draws from the true but unknown probability distribution genera...
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Published in | Journal of risk and uncertainty Vol. 57; no. 2; pp. 153 - 176 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
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01.10.2018
Springer US Springer Nature B.V |
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Abstract | We study how experimental subjects report subjective probability distributions in the presence of ambiguity characterized by uncertainty over a fixed set of possible probability distributions generating future outcomes. Subjects observe draws from the true but unknown probability distribution generating outcomes at the beginning of each period of the experiment and state at selected periods a) the likelihoods that each probability distribution in the set is the true distribution, and b) the likelihoods of future outcomes. We estimate heterogeneity of rules used to update uncertainty about the true distribution and rules used to report distributions of future outcomes. We find that approximately 65% of subjects report distributions by properly weighing the possible distributions using their expressed uncertainty over them, while 22% of subjects report distributions close to the distribution they perceive as most likely. We find significant heterogeneity in how subjects update their expressed uncertainty. On average, subjects tend to overweigh the importance of their prior uncertainty relative to new information, leading to ambiguity that is substantially more persistent than would be predicted using Bayes' rule. Counterfactual simulations suggest that this persistence will likely hold in settings not covered by our experiment. |
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AbstractList | We study how experimental subjects report subjective probability distributions in the presence of ambiguity characterized by uncertainty over a fixed set of possible probability distributions generating future outcomes. Subjects observe draws from the true but unknown probability distribution generating outcomes at the beginning of each period of the experiment and state at selected periods a) the likelihoods that each probability distribution in the set is the true distribution, and b) the likelihoods of future outcomes. We estimate heterogeneity of rules used to update uncertainty about the true distribution and rules used to report distributions of future outcomes. We find that approximately 65% of subjects report distributions by properly weighing the possible distributions using their expressed uncertainty over them, while 22% of subjects report distributions close to the distribution they perceive as most likely. We find significant heterogeneity in how subjects update their expressed uncertainty. On average, subjects tend to overweigh the importance of their prior uncertainty relative to new information, leading to ambiguity that is substantially more persistent than would be predicted using Bayes’ rule. Counterfactual simulations suggest that this persistence will likely hold in settings not covered by our experiment. |
Author | Kröger, Sabine Bellemare, Charles Sossou, Kouamé Marius |
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CitedBy_id | crossref_primary_10_1007_s11166_018_9292_4 crossref_primary_10_1016_j_jeconom_2020_11_003 |
Cites_doi | 10.1007/s10683-006-9159-4 10.1002/jae.1225 10.2307/2998573 10.1287/mnsc.2016.2700 10.1017/S1930297500004940 10.1111/j.1468-0262.2004.00537.x 10.1007/s10198-015-0701-1 10.1037/h0034747 10.1002/9781118468333.ch3 10.1007/s11166-016-9244-9 10.1016/j.jebo.2012.08.006 10.1007/s11166-017-9250-6 10.1016/j.geb.2012.01.002 10.1111/j.1468-0262.2008.00860.x 10.1111/j.1467-937X.2009.00557.x 10.1006/obhd.1993.1037 10.1016/j.euroecorev.2013.03.008 10.1162/jeea.2007.5.2-3.369 10.1111/ecoj.12160 10.1007/s11166-017-9264-0 10.3386/w17014 10.1111/0272-4332.00063 10.1111/j.1468-0262.2005.00640.x 10.1016/j.geb.2013.06.001 10.2307/1885092 10.1093/restud/rdu044 10.1126/science.185.4157.1124 10.1016/j.electstud.2015.01.006 10.1007/s10683-014-9420-1 10.1017/S1357530900000132 10.2307/1884324 |
ContentType | Journal Article |
Copyright | Springer Science+Business Media, LLC, part of Springer Nature 2018 Journal of Risk and Uncertainty is a copyright of Springer, (2018). All Rights Reserved. |
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References | Edwards, W. (1982). Conservatism in human information processing (excerpted). In Judgment under uncertainty: Heuristics and biases. Cambridge: Cambridge University Press. TverskyAKahnemanDJudgment under uncertainty: Heuristics and biasesScience19741851124113110.1126/science.185.4157.1124 RappangeDRBrouwerWBvan ExelJA long life in good health: Subjective expectations regarding length and future healthrelated quality of lifeThe European Journal of Health Economics201617557758910.1007/s10198-015-0701-1 DelavandeAManskiCFUsing elicited choice probabilities in hypothetical elections to study decisions to voteElectoral Studies201538283710.1016/j.electstud.2015.01.006 GoldsteinGDRothschildDLay understanding of probability distributionsJudgment and Decision Making20149114 KahnemanDTverskyAOn the psychology of predictionPsychological Review19738023725110.1037/h0034747 MorenoOMRosokhaYLearning under compound risk vs. learning under ambiguity—an experimentJournal of Risk and Uncertainty20165313716210.1007/s11166-017-9250-6 DominitzJManskiCFExpected equity returns and portfolio choice: Evidence from the health and retirement studyJournal of the European Economic Association2007536937910.1162/jeea.2007.5.2-3.369 Mobius, M.M., Niederle, M., Niehaus, P., Rosenblat, T.S. (2011). Managing self-confidence: Theory and experimental evidence, Tech. rep., National Bureau of Economic Research. QiuJWeitzelUExperimental evidence on valuation with multiple priorsJournal of Risk and Uncertainty201653557410.1007/s11166-016-9244-9 BaillonABleichrodtHKeskinUl’HaridonOLiCThe effect of learning on ambiguity attitudesManagement Science2017642181219810.1287/mnsc.2016.2700 Engle-WarnickJLaszloSLearning-by-doing in an ambiguous environmentJournal of Risk and Uncertainty201755719410.1007/s11166-017-9264-0 CohenMGilboaIJaffrayJ-YSchmeidlerDAn experimental study of updating ambiguous beliefsRisk Decision and Policy2000512313310.1017/S1357530900000132 Al-Nowaihi, A., & Dhami, S. (2010). Probability weighting functions. Wiley Encyclopedia of Operations Research and Management Science. McClelland, A., & Bolder, F. (1994). The calibration of subjective probabilities: Theories and models 1980–1994. In Subjective probability (pp. 453–481). New York: Wiley. DominitzJManskiCFMeasuring and interpreting expectations of equity returnsJournal of Applied Econometrics20112635237010.1002/jae.1225 FischbacherUz-Tree: Zurich toolbox for ready-made economic experimentsExperimental Economics20071017117810.1007/s10683-006-9159-4 TrautmannStefan T.van de KuilenGijsAmbiguity AttitudesThe Wiley Blackwell Handbook of Judgment and Decision Making2015Chichester, UKJohn Wiley & Sons, Ltd8911610.1002/9781118468333.ch3 ArmantierOTreichNEliciting beliefs: Proper scoring rule, incentives, stakes and hedgingEuropean Economic Review201362174010.1016/j.euroecorev.2013.03.008 TrautmannSde van KuilenGBelief elicitation: A horse race among truth serumsEconomic Journal20151252116213510.1111/ecoj.12160 PrelecDThe probability weighting functionEconometrica19986649752710.2307/2998573 Bruine de BruinWFischbeckPSStiberNAFischhoffBWhat number is “fifty-fifty”?: Redistributing excessive 50% responses in elicited probabilitiesRisk Analysis20022271372310.1111/0272-4332.00063 DominiakADuerschPLefortJ-PA dynamic Ellsberg urn experimentGames and Economic Behavior20127562563810.1016/j.geb.2012.01.002 EllsbergDRisk, ambiguity, and the Savage axiomsThe Quarterly Journal of Economics19617564366910.2307/1884324 OffermanTSonnemansJde van KuilenGWakkerPA truth serum for non-Bayesians: Correcting proper scoring rules for risk attitudesReview of Economic Studies2009761461148910.1111/j.1467-937X.2009.00557.x WiswallMZafarBDeterminants of college major choice: Identification using an information experimentThe Review of Economic Studies20158279182410.1093/restud/rdu044 GretherDMBayes rule as a descriptive model: The representativeness heuristicThe Quarterly Journal of Economics19809553755710.2307/1885092 NeriCEliciting beliefs in continuous-choice games: A double auction experimentExperimental Economics20151856960810.1007/s10683-014-9420-1 GrossmanZOwensDAn unlucky feeling: Overconfidence and noisy feedbackJournal of Economic Behavior & Organization20128451052410.1016/j.jebo.2012.08.006 ManskiCNeriCFirst- and second-order subjective expectations in strategic decision-making: Experimental evidenceGames and Economic Behavior20138123225410.1016/j.geb.2013.06.001 ManskiCFMeasuring expectationsEconometrica2004721329137610.1111/j.1468-0262.2004.00537.x BellemareCKrögerSVan SoestAMeasuring inequity aversion in a heterogeneous population using experimental decisions and subjective probabilitiesEconometrica20087681583910.1111/j.1468-0262.2008.00860.x KlibanoffPMarinacciMMukerjiSA smooth model of decision making under ambiguityEconometrica2005731849189210.1111/j.1468-0262.2005.00640.x van LentheJELI: An interactive elicitation technique for subjective probability distributionsOrganizational Behavior and Human Decision Processes19935537941310.1006/obhd.1993.1037 J Dominitz (9291_CR9) 2007; 5 A Tversky (9291_CR32) 1974; 185 A Baillon (9291_CR3) 2017; 64 P Klibanoff (9291_CR19) 2005; 73 U Fischbacher (9291_CR14) 2007; 10 D Prelec (9291_CR27) 1998; 66 9291_CR23 A Dominiak (9291_CR8) 2012; 75 9291_CR22 J Lenthe van (9291_CR33) 1993; 55 A Delavande (9291_CR7) 2015; 38 OM Moreno (9291_CR24) 2016; 53 D Ellsberg (9291_CR12) 1961; 75 DM Grether (9291_CR16) 1980; 95 S Trautmann (9291_CR31) 2015; 125 C Manski (9291_CR21) 2013; 81 GD Goldstein (9291_CR15) 2014; 9 T Offerman (9291_CR26) 2009; 76 C Neri (9291_CR25) 2015; 18 CF Manski (9291_CR20) 2004; 72 J Qiu (9291_CR28) 2016; 53 9291_CR1 D Kahneman (9291_CR18) 1973; 80 J Engle-Warnick (9291_CR13) 2017; 55 9291_CR11 C Bellemare (9291_CR4) 2008; 76 Z Grossman (9291_CR17) 2012; 84 O Armantier (9291_CR2) 2013; 62 J Dominitz (9291_CR10) 2011; 26 Stefan T. Trautmann (9291_CR30) 2015 M Cohen (9291_CR6) 2000; 5 M Wiswall (9291_CR34) 2015; 82 DR Rappange (9291_CR29) 2016; 17 W Bruine de Bruin (9291_CR5) 2002; 22 |
References_xml | – reference: TrautmannSde van KuilenGBelief elicitation: A horse race among truth serumsEconomic Journal20151252116213510.1111/ecoj.12160 – reference: Bruine de BruinWFischbeckPSStiberNAFischhoffBWhat number is “fifty-fifty”?: Redistributing excessive 50% responses in elicited probabilitiesRisk Analysis20022271372310.1111/0272-4332.00063 – reference: BellemareCKrögerSVan SoestAMeasuring inequity aversion in a heterogeneous population using experimental decisions and subjective probabilitiesEconometrica20087681583910.1111/j.1468-0262.2008.00860.x – reference: van LentheJELI: An interactive elicitation technique for subjective probability distributionsOrganizational Behavior and Human Decision Processes19935537941310.1006/obhd.1993.1037 – reference: DominiakADuerschPLefortJ-PA dynamic Ellsberg urn experimentGames and Economic Behavior20127562563810.1016/j.geb.2012.01.002 – reference: RappangeDRBrouwerWBvan ExelJA long life in good health: Subjective expectations regarding length and future healthrelated quality of lifeThe European Journal of Health Economics201617557758910.1007/s10198-015-0701-1 – reference: BaillonABleichrodtHKeskinUl’HaridonOLiCThe effect of learning on ambiguity attitudesManagement Science2017642181219810.1287/mnsc.2016.2700 – reference: McClelland, A., & Bolder, F. (1994). The calibration of subjective probabilities: Theories and models 1980–1994. In Subjective probability (pp. 453–481). New York: Wiley. – reference: GoldsteinGDRothschildDLay understanding of probability distributionsJudgment and Decision Making20149114 – reference: Mobius, M.M., Niederle, M., Niehaus, P., Rosenblat, T.S. (2011). Managing self-confidence: Theory and experimental evidence, Tech. rep., National Bureau of Economic Research. – reference: OffermanTSonnemansJde van KuilenGWakkerPA truth serum for non-Bayesians: Correcting proper scoring rules for risk attitudesReview of Economic Studies2009761461148910.1111/j.1467-937X.2009.00557.x – reference: DominitzJManskiCFExpected equity returns and portfolio choice: Evidence from the health and retirement studyJournal of the European Economic Association2007536937910.1162/jeea.2007.5.2-3.369 – reference: CohenMGilboaIJaffrayJ-YSchmeidlerDAn experimental study of updating ambiguous beliefsRisk Decision and Policy2000512313310.1017/S1357530900000132 – reference: Engle-WarnickJLaszloSLearning-by-doing in an ambiguous environmentJournal of Risk and Uncertainty201755719410.1007/s11166-017-9264-0 – reference: GrossmanZOwensDAn unlucky feeling: Overconfidence and noisy feedbackJournal of Economic Behavior & Organization20128451052410.1016/j.jebo.2012.08.006 – reference: ManskiCFMeasuring expectationsEconometrica2004721329137610.1111/j.1468-0262.2004.00537.x – reference: MorenoOMRosokhaYLearning under compound risk vs. learning under ambiguity—an experimentJournal of Risk and Uncertainty20165313716210.1007/s11166-017-9250-6 – reference: DelavandeAManskiCFUsing elicited choice probabilities in hypothetical elections to study decisions to voteElectoral Studies201538283710.1016/j.electstud.2015.01.006 – reference: KahnemanDTverskyAOn the psychology of predictionPsychological Review19738023725110.1037/h0034747 – reference: KlibanoffPMarinacciMMukerjiSA smooth model of decision making under ambiguityEconometrica2005731849189210.1111/j.1468-0262.2005.00640.x – reference: WiswallMZafarBDeterminants of college major choice: Identification using an information experimentThe Review of Economic Studies20158279182410.1093/restud/rdu044 – reference: Edwards, W. (1982). Conservatism in human information processing (excerpted). In Judgment under uncertainty: Heuristics and biases. 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SubjectTerms | Ambiguity Economic Theory/Quantitative Economics/Mathematical Methods Economics Economics and Finance Environmental Economics Expectations Heterogeneity Microeconomics Operations Research/Decision Theory Probability distribution Research subjects Studies Uncertainty Weighing |
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Title | Reporting probabilistic expectations with dynamic uncertainty about possible distributions |
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