Choice under Limited Uncertainty

2006 ◽  
Vol 6 (1) ◽  
Author(s):  
Ettore Damiano
Keyword(s):  
Probability Distribution ◽  
Expected Utility ◽  
Selection Rule ◽  
Subjective Expected Utility ◽  
Subjective Probabilities ◽  
True Probability ◽  
Utility Maximizer ◽  
The Third ◽  
Choice Under Uncertainty ◽  
New Framework

This paper considers the problem of an agent's choice under uncertainty in a new framework. The agent does not know the true probability distribution over the state space but is objectively informed that it belongs to a specified set of probabilities. Maintaining the hypothesis that this agent is a subjective expected utility maximizer, we address the question of how the objective information influences her subjective prior.Three plausible rules are proposed. The first, named state independence, states that the subjective probability should not depend on how the uncertain states are `labeled'. Location-consistency, the second property, assumes that `similar' objective sets of probabilities result in `similar' subjective priors. The third rule is an `update-consistency' rule. Suppose the agent selects some probability p. She is then told that the likelihood assigned by p to some event A is in fact correct; then this should not cause her to revise her choice of p.Another property, alternative to update-consistency, is also proposed. When an agent forms her subjective prior assigning subjective probabilities to events in some ordered sequence, this property requires that the resulting prior be independent of that order. This last property, named order independence, is shown to be equivalent to update-consistency.A class of sets of probabilities is found on which state independence, location-consistency and update consistency (order independence) uniquely determine a selection rule. Some intuition is given regarding why these properties work in this collection of problems.

Subjective Expected Utility and Referral Decisions in Obesity

1986 ◽  
Vol 25 (04) ◽  
pp. 215-221 ◽  
Author(s):  
P. A. Jennett ◽  
A. S. Elstein ◽  
Marilyn Rothert ◽  
D. R. Rovner ◽  
Necia Black
Keyword(s):  
Primary Care ◽  
Patient Satisfaction ◽  
Expected Utility ◽  
Primary Care Physicians ◽  
Subjective Expected Utility ◽  
Value Assessment ◽  
Subjective Probabilities ◽  
Adequate Model ◽  
Referral Decisions ◽  
Patient Goals

SummaryReferral decisions of physicians dealing with long-term ambulatory problems are complex phenomena that are not clearly understood. This study was designed to assess the possible rationale behind such decisions in the management of obesity. It examined how well a subjective expected utility (SEU) model accounted for decisions of 45 primary care physicians regarding referral of obese female patients to an endocrinologist. Two patient goals, weight reduction and patient satisfaction, and a two-year time horizon were incorporated in the model.Data were collected using 24 written cases representing 12 patients approximately 100% overweight and 12 about 50% overweight, and a semi-structured interview in which subjective probabilities and importance weights were obtained. Values were calculated by transforming physicians’ ratings of risk of morbidity in the 24 cases into a utility scale.The SEU did not account for the primary care physicians’ referral behavior. Correlations between number of patient cases referred and SEU were analyzed and were not statistically significant, although there was substantial variation across physicians in number of cases referred. Mean subjective probabilities of weight loss and patient satisfaction were essentially identical for referral and non-referral.The formulation of the model, the design of the cases, and the method of value assessment are discussed as potential threats to the validity of the model as an account of referral decisions. Problems of constructing an adequate model are considered.

scholarly journals Investment choice under uncertainty: A review essay

2005 ◽  
Vol 50 (167) ◽  
pp. 141-170
Author(s):  
Dejan Trifunovic
Keyword(s):  
Probability Distribution ◽  
Expected Utility ◽  
Investment Decision ◽  
Distribution Functions ◽  
Risk Averse ◽  
Investment Opportunity ◽  
Probability Distribution Functions ◽  
Investment Choice ◽  
Choice Under Uncertainty ◽  
Investment Decision Making

An investment opportunity whose return is perfectly predictable, hardly exists at all. Instead, investor makes his decisions under conditions of uncertainty. Theory of expected utility is the main analytical tool for description of choice under uncertainty. Critics of the theory contend that individuals have bounded rationality and that the theory of expected utility is not correct. When agents are faced with risky decisions they behave differently, conditional on their attitude towards risk. They can be risk loving, risk averse or risk neutral. In order to make an investment decision it is necessary to compare probability distribution functions of returns. Investment decision making is much simpler if one uses expected values and variances instead of probability distribution functions.

Spontanität oder Reflexion?

2001 ◽  
Vol 23 (1) ◽  
Author(s):  
Jörg-Peter Schräpler
Keyword(s):  
Information Processing ◽  
Expected Utility ◽  
Second Step ◽  
Subjective Expected Utility ◽  
Subjective Probabilities ◽  
Definition Of The Situation ◽  
Definition Of ◽  
Two Stages ◽  
Selection Of ◽  
Action Decision

AbstractEvery action decision made by an actor is preceded by a special definition of the situation that first structures the preferences and expectations from which the selection of an action proceeds in a second step. In a conception based on the ‚subjective expected utility‘ (SEU) Esser 1996 modelled the definition of a situation as a dual structure comprising two stages of selection: the selection of the model and the selection of the mode of information processing. In this paper it is shown how a link to a threedimensional picture of the situation emerges from the formal assumptions of these two selection steps. Depending on the selected utility ratios this picture then represents the choice of the mode of information processing as a function of subjective probabilities.

If It Is Surely Better, Do It More? Implications for Preferences Under Ambiguity

2021 ◽  
Author(s):  
Soheil Ghili ◽  
Peter Klibanoff
Keyword(s):  
Expected Utility ◽  
Ambiguity Aversion ◽  
Upper Bounds ◽  
Maxmin Expected Utility ◽  
Weak Dominance ◽  
Choice Under Uncertainty ◽  
Tight Connection ◽  
Sales Agent ◽  
Utility Preferences ◽  
Sensitive Behavior

Consider a canonical problem in choice under uncertainty: choosing from a convex feasible set consisting of all (Anscombe–Aumann) mixtures of two acts f and g, [Formula: see text]. We propose a preference condition, monotonicity in optimal mixtures, which says that surely improving the act f (in the sense of weak dominance) makes the optimal weight(s) on f weakly higher. We use a stylized model of a sales agent reacting to incentives to illustrate the tight connection between monotonicity in optimal mixtures and a monotone comparative static of interest in applications. We then explore more generally the relation between this condition and preferences exhibiting ambiguity-sensitive behavior as in the classic Ellsberg paradoxes. We find that monotonicity in optimal mixtures and ambiguity aversion (even only local to an event) are incompatible for a large and popular class of ambiguity-sensitive preferences (the c-linearly biseparable class. This implies, for example, that maxmin expected utility preferences are consistent with monotonicity in optimal mixtures if and only if they are subjective expected utility preferences. This incompatibility is not between monotonicity in optimal mixtures and ambiguity aversion per se. For example, we show that smooth ambiguity preferences can satisfy both properties as long as they are not too ambiguity averse. Our most general result, applying to an extremely broad universe of preferences, shows a sense in which monotonicity in optimal mixtures places upper bounds on the intensity of ambiguity-averse behavior. This paper was accepted by Manel Baucells, decision analysis.

scholarly journals Constraints on probability distributions of grammatical forms

Psihologija ◽  
2007 ◽  
Vol 40 (1) ◽  
pp. 5-35
Author(s):  
Aleksandar Kostic ◽  
Milena Bozic
Keyword(s):  
Probability Distribution ◽  
Relative Entropy ◽  
Probability Distributions ◽  
The Third ◽  
Singular Form ◽  
And Gender ◽  
Grammatical Number ◽  
Morphological System

In this study we investigate the constraints on probability distribution of grammatical forms within morphological paradigms of Serbian language, where paradigm is specified as a coherent set of elements with defined criteria for inclusion. Thus, for example, in Serbian all feminine nouns that end with the suffix "a" in their nominative singular form belong to the third declension, the declension being a paradigm. The notion of a paradigm could be extended to other criteria as well, hence, we can think of noun cases, irrespective of grammatical number and gender, or noun gender, irrespective of case and grammatical number, also as paradigms. We took the relative entropy as a measure of homogeneity of probability distribution within paradigms. The analysis was performed on 116 morphological paradigms of typical Serbian and for each paradigm the relative entropy has been calculated. The obtained results indicate that for most paradigms the relative entropy values fall within a range of 0.75 - 0.9. Nonhomogeneous distribution of relative entropy values allows for estimating the relative entropy of the morphological system as a whole. This value is 0.69 and can tentatively be taken as an index of stability of the morphological system.

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