Risk, Ambiguity, and the Savage Axioms: Comment

1963 ◽  
Vol 77 (2) ◽  
pp. 327 ◽  
Author(s):  
Harry V. Roberts
Keyword(s):  
Risk Ambiguity ◽  
Savage Axioms

[Risk, Ambiguity, and the Savage Axioms]:Reply

1963 ◽  
Vol 77 (2) ◽  
pp. 336 ◽  
Author(s):  
Daniel Ellsberg
Keyword(s):  
Risk Ambiguity ◽  
Savage Axioms

scholarly journals Risk, Ambiguity, and the Savage Axioms

1961 ◽  
Vol 75 (4) ◽  
pp. 643 ◽  
Author(s):  
Daniel Ellsberg
Keyword(s):  
Risk Ambiguity ◽  
Savage Axioms

Risk, Ambiguity, and the Savage Axioms: Comment

1961 ◽  
Vol 75 (4) ◽  
pp. 690 ◽  
Author(s):  
Howard Raiffa
Keyword(s):  
Risk Ambiguity ◽  
Savage Axioms

scholarly journals Risk, ambiguity, and the Savage axioms

1988 ◽  
pp. 245-269 ◽  
Author(s):  
Daniel Ellsberg
Keyword(s):  
Risk Ambiguity ◽  
Savage Axioms

scholarly journals Risk, Ambiguity, and the Separation of Utility and Beliefs

2001 ◽  
Author(s):  
Paolo Ghirardato ◽  
Massimo Marinacci
Keyword(s):  
Risk Ambiguity

scholarly journals Risk, ambiguity, and sovereign rating

2014 ◽  
Vol 12 (1) ◽  
pp. 41-57
Author(s):  
Paolo Di Caro
Keyword(s):  
Risk Ambiguity

Risk, ambiguity, and equity premium: International evidence

2021 ◽  
Author(s):  
Eung-Bin Kim ◽  
Suk-Joon Byun
Keyword(s):  
Equity Premium ◽  
International Evidence ◽  
Risk Ambiguity

scholarly journals Uncertainty and computational complexity

2018 ◽  
Vol 374 (1766) ◽  
pp. 20180138 ◽  
Author(s):  
Peter Bossaerts ◽  
Nitin Yadav ◽  
Carsten Murawski
Keyword(s):  
Decision Making ◽  
Computational Complexity ◽  
Risk Taking ◽  
Utility Maximization ◽  
Decision Makers ◽  
Primary Concern ◽  
Subjective Beliefs ◽  
Theoretical Perspectives ◽  
New Information ◽  
Savage Axioms

Modern theories of decision-making typically model uncertainty about decision options using the tools of probability theory. This is exemplified by the Savage framework, the most popular framework in decision-making research. There, decision-makers are assumed to choose from among available decision options as if they maximized subjective expected utility, which is given by the utilities of outcomes in different states weighted with subjective beliefs about the occurrence of those states. Beliefs are captured by probabilities and new information is incorporated using Bayes’ Law. The primary concern of the Savage framework is to ensure that decision-makers’ choices are rational . Here, we use concepts from computational complexity theory to expose two major weaknesses of the framework. Firstly, we argue that in most situations, subjective utility maximization is computationally intractable, which means that the Savage axioms are implausible. We discuss empirical evidence supporting this claim. Secondly, we argue that there exist many decision situations in which the nature of uncertainty is such that (random) sampling in combination with Bayes’ Law is an ineffective strategy to reduce uncertainty. We discuss several implications of these weaknesses from both an empirical and a normative perspective. This article is part of the theme issue ‘Risk taking and impulsive behaviour: fundamental discoveries, theoretical perspectives and clinical implications’.

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