{"title":"Decision Making Under Uncertainty","authors":"C. Yoe","doi":"10.1201/9780429021121-19","DOIUrl":"https://doi.org/10.1201/9780429021121-19","url":null,"abstract":"We design and implement a novel experimental test of subjective expected utility theory and its generalizations. Our experiments are implemented in the lab, and pushed out through a large-scale panel to a general sample of the U.S. population. We find that subjects respond to price changes in the expected direction, but not enough to make their choices consistent with the theory. Surprisingly, maxmin expected utility adds no explanatory power to subjective expected utility. Our findings are the same, regardless of whether we look at laboratory data or a large panel survey, even though the subject populations are very different. The degree of violations of subjective expected utility theory is not affected by age, but is correlated with financial literacy and income. The effects of education level and gender are weak and not independent from the effects of other demographic variables. Decision making under uncertainty: An experimental study in market settings The authors wish to thank Aurélien Baillon, Yoram Halevy, Pietro Ortoleva, Maricano Siniscalchi, and Charles Sprenger for helpful discussions. The authors also thank Noriko Imai for developing the software for the experiment, Yimeng Li for research assistance, John Duffy, Michael McBride, and Jason Ralston for supporting our experiments at ESSL, and Tania Gutsche and Bart Orriens at CESR (University of Southern California) for setting up and implementing the survey on the Understanding America Study. This research is supported by Grant SES1558757 from the National Science Foundation and the TIAA Institute and Wharton School's Pension Research Council/Boettner Center. In addition, Echenique thanks the NSF for its support through the grant CNS-1518941, and Imai acknowledges financial support by the Deutsche Forschungsgemeinschaft through CRC TRR 190. This project received funding from the TIAA Institute and Wharton School’s Pension Research Council/Boettner Center. The content is solely the responsibility of the authors and does not necessarily represent official views of the TIAA Institute or Wharton School’s Pension Research Council/Boettner Center. The project described in this paper relies on data from surveys administered by the Understanding America Study, which is maintained by the Center for Economic and Social Research (CESR) at the University of Southern California. The content of this paper is solely the responsibility of the authors and does not necessarily represent the official views of USC or UAS. Any opinions expressed herein are those of the authors, and do not necessarily represent the views of TIAA, the TIAA Institute or any other organization with which the authors are affiliated. Federico Echenique, California Institute of Technology Taisuke Imai, Ludwig Maximilian University of Munich Kota Saito, California Institute of Technology","PeriodicalId":428630,"journal":{"name":"Principles of Risk Analysis","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128500444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}