# decision theory in statistics

Statistics & Decisions provides an international forum for the discussion of theoretical and applied aspects of mathematical statistics with a special orientation to decision theory. As stated, the paradox turns on the acceptability of this principle. It applies those principles not only when an expected-utility representation of preferences exists but also in other circumstances. We can continue to ask how much of a backward constraint ordinalism lays on the informational basis of admissible preference-revealing data and, thereby, on our psychological conception of what preferences amount to. Beja and Gilboa (1992), in particular, propose a very refined and visual way of associating progressively more stringent conditions on orders of preferences with alternative representations corresponding to distinct levels of discrimination of utility (or other) differences (we refer the reader to this work and also warn that this is not what we present in the argument of the next paragraph). But in the same way it is standard that representation theorems impose an interpretation of the nature (in terms of ordinality, cardinality, and type of cardinality) of the utility function and that the role of the utility function as rationalizing choice-data constraints back the interpretation of preferences; hence its axiomatization and its possible representation. Anyone interested in the whys and wherefores of statistical science will find much to enjoy in this book." It would be if we could effectively accrue observable data that would point to the actual processing of utility differences and comparisons of preferences intensities, if these data jointly reveal some inherent structure of preferences, and if the latter structure could be axiomatized and represented in these terms. Inverse problems of probability theory are a subject of mathematical statistics. is a family of probability distributions. Another advantage of this interpretation of probabilities is that one may calculate expected utilities to form preferences without extracting probabilities and utilities from preferences already formed. I will not be concerned with experimental design (in which hypotheses but not data are typically known), nor with hypothesis generation (if there is such a thing). Paul Weirich, in Philosophy of Statistics, 2011. $$. For example, one may infer some probabilities from an agent's evidence. and choose the most profitable way to proceed (in particular, it may be decided that insufficient material has been collected and that the set of observations has to be extended before final inferences be made). Thus, I am far removed from utilitarianism, which is one of the most important versions of consequentialism. This distinction, scholastic as it sounds, is nevertheless crucial to distinguish two roles of utility functions: representing preference relations and rationalizing choice-data. Given this instrumentalist view, it might seem that causal inference maybe distinguished from other inferences only due to its emphasis on manipulation rather than prediction. from  ( \Omega ^ {n} , {\mathcal A} ^ {n} )  Sacha Bourgeois-Gironde, in The Mind Under the Axioms, 2020. I understand, here, by consequentialism the following ethical doctrine: the possible consequences of an act are ranked from best to worst in accordance with certain criterion which varies according to the different brands of consequentialism. By the choice of topics and the way they are dealt with, we do not offer the reader a textbook. It is somewhat more difficult to assign numbers (utilities) to them, but I don't think that this is an unsurmountable difficulty. Within Savage’s framework the possibility of this separate representation relies on the interplay between its two axioms P3 and P4. But parsimony can be expressed in different ways. Although it seems reasonable, Hájek takes the cable guy paradox as showing that it is mistaken.12 While the principle may often apply, it does seem that it might be overridden in this case. As is standard, I use X and Y to denote random variables4 and p(F(x) | G(x)) as shorthand for p(F(X) = F(x) | G(Y) = G(y)). Here we feel that it is not that our subjective evaluation of the events probability has changed from one stake to the other but that, perhaps, our behavior is sensitive to incentives and that we do not take the elicitation procedure seriously when the stakes are too low or presented in the fashion they were. In the corresponding interpretation, many problems of the theory of quantum-mechanical measurements become non-commutative analogues of problems of statistical decision theory (see [6]). A non-vNM-representation relative cardinal utility function has—to warrant its role of representing preferences—to represent quaternary relations of the form; wRz≽xRy (R and ≽ do not have to be two distinct preference relations, but at least we can say that ≽ is of a logically higher order than R, justifying the difference in notation). When xobs is the only observation being used to make inferences about a hypothesis space H, I will refer to xobs as the actual observation. Another issue that has not been formally or conceptually addressed, to our knowledge, is that the widening or restricting of the set of states over which we consider the consequences of acts, even though these acts continue to yield similar consequences whether we widen or restrict their evaluative space—that is, are in principle subject to normative compliance with P3—has nevertheless evaluative implications. This gives some stochasticity to the preference relation, which is not essential to the argument but may allude to the fact that δ being deterministic is a limit-case. In much of the traditional debate around ordinalism and cardinalism, it is implicitly held that since preferences are rankings, and since utility functions represent preferences, this representational property of utility functions impose that they are by default ordinal. This framework, however, is not offered as an ethical theory, that is, as a theory for deciding on any moral act, but only as a framework for decisions in a restricted situation where all the actions are morally acceptable. There are some interesting connections with Bayesian inference. So, in the context presented here, possible actions, means physically possible to perform and morally permissible. …a solid addition to the literature of decision theory from a formal mathematical statistics approach. Kochov (2010) offers interesting forays on this issue. It is as if the logic of a representation procedure required this structural morphism from preferences to utility. A fact that is not as obvious as it looks and would need clarification. This fact has many implications that differentiate the theory from consequentialism: The possible consequences of the acts are restricted in number, and are of a very specific form, depending on the situation at hand. The objects of choices now become composite, which can raise some problems, but at the same time this is what makes this procedure potentially behavioral or choice-theoretically founded. In consequentialism as an ethical theory, one should take into account all these remote consequences. ScienceDirect ® is a registered trademark of Elsevier B.V. ScienceDirect ® is a registered trademark of Elsevier B.V. URL: https://www.sciencedirect.com/science/article/pii/S0304020808724319, URL: https://www.sciencedirect.com/science/article/pii/B9780444518620500034, URL: https://www.sciencedirect.com/science/article/pii/B9780444518620500228, URL: https://www.sciencedirect.com/science/article/pii/B9780120887705500447, URL: https://www.sciencedirect.com/science/article/pii/B9780128151310000146, URL: https://www.sciencedirect.com/science/article/pii/B9780128151310000031, URL: https://www.sciencedirect.com/science/article/pii/B9780128151310000018, URL: https://www.sciencedirect.com/science/article/pii/B9780444518620500174, URL: https://www.sciencedirect.com/science/article/pii/B9780444518620500265, URL: https://www.sciencedirect.com/science/article/pii/B9780444518620500071, Luce & Suppes, 1965; Suppes, 1956, 1961; Suppes & Winet, 1955, Because the LP does not take into account a utility or loss function (see discussion of this below), the LP does not give us a, The Logic and Philosophy of Causal Inference, The Bayesian Decision-Theoretic Approach to Statistics, International Transactions in Operational Research. In general, such consequences are not known with certainty but are expressed as a set of probabilistic outcomes. Notice that when there is some time t, however fleeting, for which a future act A will certainly be rationally preferred to B, yet many times at which B will almost certainly be preferred to A, the principle requires choosing A, regardless of the likelihood of generally preferring B to A. This function includes explicit, quantified gains and losses to reach a conclusion. th set, whereas the  \{ P _ {1} , P _ {2} ,\dots \}  However, as early as 1820, P. Laplace had likewise described a statistical estimation problem as a game of chance in which the statistician is defeated if his estimates are bad. Math. Degrees of beliefs may be implicitly defined by the theories to which they belong, as in Weirich [2001]. It is used in a diverse range of applications including but definitely not limited to finance for guiding investment strategies or in engineering for designing control systems. Of interest then is that the most successful statistical model of causation, the potential-outcomes model discussed below, has attracted theoretical criticisms precisely because it contains counterfactual elements hidden from randomizedexperimental test (e.g., [Dawid, 2000]). The statistician knows only the qualitative description of  \phi , and  P _ {2} = P _ {1} \Pi  To what extent is an axiomatic characterization of preferences reflected in its representation by a utility function? Some theorists take the equality of degrees of belief and betting quotients as a definition of degrees of beliefs. Consequently, the main problem in Suppes and Winet’s representation procedure may not be its resort to introspective data but its “in the middle of the way” modification of the preference domain. see Information distance), is a monotone invariant in the category:$$ Suppose that we theoreticians know δ and the values associated with Pδ+jnd+…. is said to be uniformly better than $\Pi _ {2}$ Are they more than a disposition to order? The indifference relation cannot be transitive for stimuli that stand below δ. The invariants and equivariants of this category define many natural concepts and laws of mathematical statistics (see [5]). Press (1944), E.L. Lehmann, "Testing statistical hypotheses" , Wiley (1986), N.N. Beliefs and utilities are jointly elicited on the basis of the same behavioral data that are preferences over acts, the latter being plausibly assimilated to bets. To safeguard against such agendas, a user should accept only decision functions with natures that have been clearly and explicitly documented. is optimal when it minimizes the risk $\mathfrak R = \mathfrak R ( P, \Pi )$— The fact that a utility function is derived from a set of axioms and represents a preference relation does not necessarily make it the best tool to account for and rationalize (i.e., try to find the particular justifications among which preferences thus axiomatized might certainly be one determining factor) choice data. First of all, what do I mean by “inference procedures”? H is the set of hypotheses under active consideration by anyone involved in the process of inference.5, Θ is a set (typically but not necessarily an ordered set) which indexes the set of hypotheses under consideration. Inference to causal models may be viewed as trying to construct a general set of laws from existing observations that can be tested with and applied to new observations. In classical problems of mathematical statistics, the number of independent observations (the size of the sample) was fixed and optimal estimators of the unknown distribution $P$ By the same token, they spell out the testability conditions that would ground this representation. \inf _ \Pi \mathfrak R _ \nu ( \Pi ) = \ Suppes and Winet introduce monetary amounts to be combined with the options x, y, and z. See for example [Forster, 2006] for an extended discussion of the problems introduced by complex hypotheses. Given ideal conditions, one may infer that the person's degree of belief that S holds equals 40%. …The book’s coverage is both comprehensive and general. Of course, one could stray into “heterodox” considerations, as in the case of P3, and counterargue that what we have just seen is that the evaluation of events is sensitive to the stakes. It seems to me that the best way to do inference is often to weight conclusions about hypotheses — each of which is a possible error — according to the desirability of avoiding the amount of error represented by each hypothesis. The next section . In decision-theory, the author of this book considers himself, at best, an in-outsider. Jason Grossman, in Philosophy of Statistics, 2011. This function includes explicit, quantified gains and losses to reach a conclusion. In the context of decision theory that is adopted here, this possibility does not arise. The contrast here is with complex hypotheses, also known as models, which are sets of simple hypotheses such that knowing that some member of the set is true (but not which) is insufficient to specify probabilities of data points. Given a set of alternatives, a set of consequences, and a correspondence between those sets, decision theory offers conceptually simple procedures for choice. State-dependence is the violation of one of the basic principles of decision-theory, namely the separation of preferences and beliefs, or, in functional terms, the noninteraction between their respective utilitarian and probabilistic representations. $$. of inferences (it can also be interpreted as a memoryless communication channel with input alphabet  \Omega  [Royall, 2004] makes an important distinction between the questions. in this sense,$$ The practical application of this prescriptive approach (how people should make decisions) is called decision analysis, and aimed at finding tools, methodologies and software to help people make better decisions. 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