Custody Data as Decision-Theory Information: Evaluating a Psychological Contribution by Its Value to a Decision Maker

2006 ◽  
Vol 6 (3) ◽  
pp. 339-343
Author(s):  
Barry Bricklin ◽  
Patricia M. Bricklin
Author(s):  
Marion Ledwig

Spohn's decision model, an advancement of Fishburn's theory, is valuable for making explicit the principle used also by other thinkers that 'any adequate quantitative decision model must not explicitly or implicitly contain any subjective probabilities for acts.' This principle is not used in the decision theories of Jeffrey or of Luce and Krantz. According to Spohn, this principle is important because it has effects on the term of action, on Newcomb's problem, and on the theory of causality and the freedom of the will. On the one hand, I will argue against Spohn with Jeffrey that the principle has to be given up. On the other, I will try to argue against Jeffrey that the decision-maker ascribes subjective probabilities to actions on the condition of the given decision situation.


1992 ◽  
Vol 3 (6) ◽  
pp. 358-361 ◽  
Author(s):  
Amos Tversky ◽  
Eldar Shafir

Choice often produces conflict. This notion, however, plays no role in classical decision theory, in which each alternative is assigned a value, and the decision maker selects from every choice set the option with the highest value. We contrast this principle of value maximization with the hypothesis that the option to delay choice or seek new alternatives is more likely to be selected when conflict is high than when it is low. This hypothesis is supported by several studies showing that the tendency to defer decision, search for new alternatives, or choose the default option can be increased when the offered set is enlarged or improved, contrary to the principle of value maximization.


2000 ◽  
Vol 15 (2) ◽  
pp. 181-185
Author(s):  
SIMON PARSONS ◽  
MICHAEL WOOLDRIDGE

In the last few years, increasing numbers of members of the agent community have been adopting techniques from game theory and decision theory. Broadly speaking, decision theory (Raiffa, 1968) is a means of analysing which of a series of options should be taken when it is uncertain exactly what the result of taking the option will be. Decision theory concentrates on identifying the “best” decision option, where the notion of “best” is allowed to have a number of different meanings, of which the most common is that which maximises the expected utility of the decision maker. Game theory (Binmore, 1992) can be considered as a variant of decision theory in which the outcome of taking a particular decision is dependent upon the actions of another, frequently an opponent which is trying to maximise its own benefit at the cost of the decision maker. Alternatively, game theory can be considered a mechansim for analysing games between two players in which each gets to choose a move from some limited set of options and, depending on what both have chosen, each receives a payout. Since the payout one player receives depends upon the move made by the other then, to maximise its payout, each player needs to take into account the likely move taken by its opponent. From this perspective, decision theory can be considered to be the study of games played against nature, an opponent which does not look to gain the best payout, but rather acts randomly.


Author(s):  
J. Q. Smith

To make a Bayes decision we choose the infimum of an expected loss function. Catastrophe theory classifies a wide class of functions locally in terms of their critical values. Firstly we will show how this local classification relates globally to some mixtures of symmetric expected loss functions. Secondly we shall indicate how such mixtures can arise and how the above classification can be usefully applied to the qualitative study of the behaviour of a Bayes decision-maker.


2021 ◽  
pp. 209-226
Author(s):  
Julian Velasco

Sometimes it is possible to deal productively with the subject matter of choosing and making decisions without actually settling upon any particular theory of choice. This is the case in the law of business organisations, which does not settle upon a theory of choice because it does not consider itself the ultimate decision maker. Rather, the law develops rules to allocate decision-making authority among the various parties. Utilising only a few basic principles of decision theory, the law of business organisation creates a structure for allocating decision-making responsibility on many different levels. However, it leaves the ultimate decision makers free not only to make substantive decisions for themselves but also to select from among the various theories of choice for doing so.


2003 ◽  
Vol 22 (4) ◽  
pp. 185-195
Author(s):  
Lucio Biggiero ◽  
Domenico Laise

To the extent an organizational structure is the outcome of an intentional and rational choice, such a choice is complex. A relevant aspect of such a complexity is the number of different and usually conflicting criteria employed by the decision maker. The application of traditional and pragmatic approaches implies the choice of one type of organizational structure through multiple assessing criteria. A multicriteria choice is involved even in a purely scientific approach: different organization theories suggest different criteria. The standard decision theory, based on the maxi-minimization of some utility function, cannot deal with multicriteriality. The task can be fulfilled in a rigorous and formal way by outranking methods, which are a branch of operations research. With an application to the problem of choosing the organizational structure, a tutorial example of outranking methods is discussed, addressing the larger theoretical framework where they can be placed and developed in management science.


1966 ◽  
Vol 14 (1) ◽  
pp. 58-69
Author(s):  
S.L. Louwes

The discrepancy between the develoment of agricultural policy and the situation of agricultural statistics in the EEC is noted and this leads to the question of the connexion between policy and statistics. An attempt is made to assemble the whole process of decision making in one analytical framework in order to attain the optimum result. An introductory section is followed by: (2) Decision theory; (3) The function of statistics in formulating economic policy; (4) The economic aspect of statistics; and (5) examples (fictitious). It is concluded that modern decision theory turns on the realization that the uncertainty involved in almost every decision must be specifically incorporated in the mechanism by which the decision is taken and cannot be eliminated by using some probability distribution, because it can only be evaluated by the decision maker. Finally, the advantages of the method for the policy-making body, the analyst and the statistician are summarized. A. T. S. (Abstract retrieved from CAB Abstracts by CABI’s permission)


Author(s):  
Erio Castagnoli ◽  
Marzia De Donno ◽  
Gino Favero ◽  
Paola Modesti

A classical problem in Decision Theory is to represent a preference preorder among random variables. The fundamental Debreu's Theorem states that, in the discrete case, a preference satisfies the so-called Sure Thing Principle if and only if it can be represented by means of a function that can be additively decomposed along the states of the world where the random variables are defined. Such a representation suggests that every discrete random variable may be seen as a “histogram” (union of rectangles), i.e., a set. This approach leads to several fruitful consequences, both from a theoretical and an interpretative point of view. Moreover, an immediate link can be found with another alternative approach, according to which a decision maker sorts random variables depending on their probability of outperforming a given benchmark. This way, a unified approach for different points of view may be achieved.


2020 ◽  
Vol 43 ◽  
Author(s):  
Peter Dayan

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.


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