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1

Koehler, Jonathan J. "Decision Theory for a General Audience." Contemporary Psychology: A Journal of Reviews 40, no. 4 (1995): 315–16. http://dx.doi.org/10.1037/003549.

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2

Cyert, Richard M. "Decision Makers and Decision Theory." Contemporary Psychology: A Journal of Reviews 34, no. 9 (1989): 840–41. http://dx.doi.org/10.1037/031085.

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3

French, Simon. "Decision making not decision theory." Journal of Multi‐Criteria Decision Analysis 7, no. 6 (1998): 303. http://dx.doi.org/10.1002/(sici)1099-1360(199811)7:6<303::aid-mcda204>3.0.co;2-6.

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4

Abd El-Monsef, M. M. E., and N. M. Kilany. "Decision Analysis via Granulation Based on General Binary Relation." International Journal of Mathematics and Mathematical Sciences 2007 (2007): 1–13. http://dx.doi.org/10.1155/2007/12714.

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Decision theory considers how best to make decisions in the light of uncertainty about data. There are several methodologies that may be used to determine the best decision. In rough set theory, the classification of objects according to approximation operators can be fitted into the Bayesian decision-theoretic model, with respect to three regions (positive, negative, and boundary region). Granulation using equivalence classes is a restriction that limits the decision makers. In this paper, we introduce a generalization and modification of decision-theoretic rough set model by using granular computing on general binary relations. We obtain two new types of approximation that enable us to classify the objects into five regions instead of three regions. The classification of decision region into five areas will enlarge the range of choice for decision makers.
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5

Hirokawa, Randy Y., and Dierdre D. Johnston. "Toward a General Theory of Group Decision Making." Small Group Behavior 20, no. 4 (1989): 500–523. http://dx.doi.org/10.1177/104649648902000408.

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6

Matic, Ivan. "The general will and the jury theorem." Theoria, Beograd 57, no. 2 (2014): 73–86. http://dx.doi.org/10.2298/theo1402073m.

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The subject of this paper is the description and analysis of some elements of Jean-Jacques Rousseau?s political theory, primarily his understanding of the process of democratic decision-making, aimed at reaching decisions that are in accordance with the principle of the general will. The first part of the paper analyses Rousseau?s understanding of the general will and the system of political decision-making. The second part compares Rousseau?s and Condorcet?s understanding of politics, further analysing Rousseau?s theory through certain elements of Condorcet?s jury theorem, based on the works of B. Grofman, S. Feld, D. Estlund and J. Waldron. The third part focuses on interpretations and critiques of Rousseau?s political theory by authors B. Barry and R. Dagger.
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7

Chen, Mengjie, Chao Gao, and Zhao Ren. "A general decision theory for Huber’s $\epsilon$-contamination model." Electronic Journal of Statistics 10, no. 2 (2016): 3752–74. http://dx.doi.org/10.1214/16-ejs1216.

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8

Denneberg, Dieter. "Keynes' decision rule, on “expectation” in the general theory." European Journal of Political Economy 4, no. 2 (1988): 205–23. http://dx.doi.org/10.1016/0176-2680(88)90001-8.

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9

Sandul, G. O., O. G. Sandul, and A. O. Bulgakov. "On the Nuclear Decision-Making Theory." Nuclear and Radiation Safety, no. 4(80) (December 3, 2018): 58–64. http://dx.doi.org/10.32918/nrs.2018.4(80).10.

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The study is devoted to the decision-making theory, its philosophical foundations and certain algorithms of practical activities. The paper is focused on activities in the field of nuclear industry.&#x0D; The functioning of a technologically complex and large enterprise, for example, a nuclear power plant, is impossible without a complex management system, and the decision-making process is one of its elements. This paper is the primary description of the topic and its goal is to formulate approaches to several questions: why do we need a decision-making theory in general, how do we evaluate the correctness of an already made decision.&#x0D; The significance of mathematical calculations in decision-making theory is described. The paper defines the basic terms that are used or will be used in the future, such as: methodology, technology, process, risk, human factor. The theoretical (philosophical) foundations that formed the basis of the decision-making doctrine are described, an attempt is made to answer the question why it is needed and by whom this theory is used and for what purpose. The connection between decision-making with the human factor is formulated. The distinction between the concepts of “decision” (as already existing) and “making decision” (as a complex process, a system of management actions, forecasting options and choosing between them) is described.&#x0D; Certain procedures are presented in the implementation of the decisionmaking process in order to show possible approaches, stages and decisionmaking techniques, as well as the readiness to implement a specific solution. The dependence (efficiency) of the management organization on the decisions made and their implementation is indicated. Besides, the paper sets forth the logic of making the “right” decisions based on “information”.&#x0D; The authors conclude about the importance of applying the decisionmaking theory during the safety analysis and predicting the likely extreme situations at nuclear power facilities and the need to continuously improve the management structures.
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10

Price, Huw. "Against causal decision theory." Synthese 67, no. 2 (1986): 195–212. http://dx.doi.org/10.1007/bf00540068.

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11

Stocklin, Philip L. "DECISION THEORY APPLICATIONS IN HUMAN DECISION MAKING." Annals of the New York Academy of Sciences 89, no. 5 (2006): 823–28. http://dx.doi.org/10.1111/j.1749-6632.1961.tb20180.x.

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12

Bordley, Robert F. "Naturalistic decision making and prescriptive decision theory." Journal of Behavioral Decision Making 14, no. 5 (2001): 355–57. http://dx.doi.org/10.1002/bdm.383.

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13

Anand, Paul. "Interpreting axiomatic (decision) theory." Annals of Operations Research 23, no. 1 (1990): 91–101. http://dx.doi.org/10.1007/bf02204840.

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14

Steele, Katie. "Challenges to decision theory." Metascience 19, no. 3 (2010): 449–51. http://dx.doi.org/10.1007/s11016-010-9421-4.

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15

Campi, Marco Claudio, Simone Garatti, and Federico Alessandro Ramponi. "A General Scenario Theory for Nonconvex Optimization and Decision Making." IEEE Transactions on Automatic Control 63, no. 12 (2018): 4067–78. http://dx.doi.org/10.1109/tac.2018.2808446.

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16

Eysenck, H. J. "Behavioral decision theory. An introduction." Personality and Individual Differences 6, no. 3 (1985): 411. http://dx.doi.org/10.1016/0191-8869(85)90072-8.

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17

Krantz, David H., and Howard C. Kunreuther. "Goals and plans in decision making." Judgment and Decision Making 2, no. 3 (2007): 137–68. http://dx.doi.org/10.1017/s1930297500000826.

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AbstractWe propose a constructed-choice model for general decision making. The model departs from utility theory and prospect theory in its treatment of multiple goals and it suggests several different ways in which context can affect choice.It is particularly instructive to apply this model to protective decisions, which are often puzzling. Among other anomalies, people insure against non-catastrophic events, underinsure against catastrophic risks, and allow extraneous factors to influence insurance purchases and other protective decisions. Neither expected-utility theory nor prospect theory can explain these anomalies satisfactorily. To apply this model to the above anomalies, we consider many different insurance-related goals, organized in a taxonomy, and we consider the effects of context on goals, resources, plans and decision rules.The paper concludes by suggesting some prescriptions for improving individual decision making with respect to protective measures.
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18

Danilov, Vladimir. "Towards the theory of general economic equilibrium." Economics and the Mathematical Methods 58, no. 3 (2022): 19. http://dx.doi.org/10.31857/s042473880020012-2.

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The paper proposes a model of general economic equilibrium, when some firms can influence prices by their actions. Their primitives (that is, the description of production, distribution, and consumption) of the proposed model are the same as those of the Arrow–Debreu model. The difference lies in the behavior of oligopolistic firms: it is assumed that they (as in the original Cournot model) assign their production plans. The main innovation is the description of the decision-making process on production plans by oligopolistic firms. In previous models, it was assumed that oligopolistic firms seek to maximize profits at current prices. Here, a more natural assumption is made that oligopolistic firms make decisions about production plans through a vote of shareholders. It is shown how the Arrow–Debreu model can be modified to account for this circumstance. The concept of Condorcet–Cournot–Walras equilibrium is introduced, combining the ideas of these three classics. The question of the existence of equilibria is discussed. A simplified version of the model is also considered, in which each firm is owned by one agent. The simplification is that there is no need to turn to the voting.
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19

Leahy, Robert L. "Depressive Decision Making: Validation of the Portfolio Theory Model." Journal of Cognitive Psychotherapy 15, no. 4 (2001): 341–62. http://dx.doi.org/10.1891/0889-8391.15.4.341.

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A multi-dimensional model of decision making, based on modern portfolio theory, is advanced that proposes that individuals consider in making behavioral decisions and contemplating risk. This model is specifically applied to depressive decision making. According to this model, depressed individuals view themselves as having few current and future resources, low predictability and control, less maximization of positives, greater minimization of negatives, less utility for gains, more disutility for losses, higher stop-loss criteria, higher information demands, more regret, and more reliance on waiting as a strategy. Participants were 153 adult psychiatric patients who were tested on a 25-item Decision Questionnaire that assessed 25 dimensions of decision making and these responses were correlated with the Beck Depression Inventory. The results substantially supported the assumptions of a general portfolio theory of risk. Risk aversion and depression were related to most of the dimensions and depression was related to risk aversion. Less depression was related to maximizing positives as a goal, but was unrelated to minimizing negatives. Four factors accounted for most of the variance: General efficacy, discouragement, unpredictability, and risk aversion.
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20

Sweeny, Kate. "Crisis decision theory: Decisions in the face of negative events." Psychological Bulletin 134, no. 1 (2008): 61–76. http://dx.doi.org/10.1037/0033-2909.134.1.61.

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21

Yukalov, V. I., and D. Sornette. "Quantum decision theory as quantum theory of measurement." Physics Letters A 372, no. 46 (2008): 6867–71. http://dx.doi.org/10.1016/j.physleta.2008.09.053.

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22

Madan, Dilip B., and J. C. Owings. "Decision theory with complex uncertainties." Synthese 75, no. 1 (1988): 25–44. http://dx.doi.org/10.1007/bf00873273.

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23

Lewis, Steven. "Toward a general theory of indifference to research-based evidence." Journal of Health Services Research & Policy 12, no. 3 (2007): 166–72. http://dx.doi.org/10.1258/135581907781543094.

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Evidence-based medicine (EBM) and evidence-based decision-making (EBDM) were intended to revolutionize health care and health policy. Thus far they have not. A great deal of research has demonstrated the persistent ubiquity of error in health care, wide and unjustifiable variations in practice and the minimal impact of decision aids such as clinical practice guidelines. This paper attempts to explain why EBM and EBDM have remained largely unrealized ambitions. It advances 10 propositions that together constitute a general theory of indifference to research-based evidence. Some of these propositions are conceptual (e.g. the epistemic resistance to the randomized trial), some are empirical (e.g. the impact of the corruption of science by industry), some are cognitive (e.g. human problems are holistic while science is typically fragmented and narrative free) and some are normative (e.g. the primary goal is not adherence to methods, but to make better decisions with better outcomes, irrespective of their origins). EBM and EBDM over-reached, and their failure was, as a consequence, inevitable. However, with corrective action on a number of fronts, research-based evidence can and should be more influential. The first step is to reconceive EBM and EBDM as habits of mind rather than a toolbox and to recognize that the sociology of knowledge is as important as its technical content.
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24

Morris, J. A. "Autoimmunity: a decision theory model." Journal of Clinical Pathology 40, no. 2 (1987): 210–15. http://dx.doi.org/10.1136/jcp.40.2.210.

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25

THURSTON, DEBORAH L., and ANGELA LOCASCIO. "DECISION THEORY FOR DESIGN ECONOMICS." Engineering Economist 40, no. 1 (1994): 41–71. http://dx.doi.org/10.1080/00137919408903138.

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26

Marletto, Chiara. "Constructor theory of probability." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 472, no. 2192 (2016): 20150883. http://dx.doi.org/10.1098/rspa.2015.0883.

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Unitary quantum theory, having no Born Rule, is non-probabilistic . Hence the notorious problem of reconciling it with the unpredictability and appearance of stochasticity in quantum measurements. Generalizing and improving upon the so-called ‘decision-theoretic approach’, I shall recast that problem in the recently proposed constructor theory of information— where quantum theory is represented as one of a class of superinformation theories , which are local , non-probabilistic theories conforming to certain constructor-theoretic conditions. I prove that the unpredictability of measurement outcomes (to which constructor theory gives an exact meaning) necessarily arises in superinformation theories. Then I explain how the appearance of stochasticity in (finitely many) repeated measurements can arise under superinformation theories. And I establish sufficient conditions for a superinformation theory to inform decisions (made under it) as if it were probabilistic, via a Deutsch–Wallace-type argument—thus defining a class of decision-supporting superinformation theories. This broadens the domain of applicability of that argument to cover constructor-theory compliant theories. In addition, in this version some of the argument's assumptions, previously construed as merely decision-theoretic, follow from physical properties expressed by constructor-theoretic principles.
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27

Audi, Robert. "Action theory as a resource for decision theory." Theory and Decision 20, no. 3 (1986): 207–21. http://dx.doi.org/10.1007/bf00134039.

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28

Bierman, Harold. "A behavioral decision theory paradox." Behavioral Science 34, no. 4 (1989): 286–88. http://dx.doi.org/10.1002/bs.3830340405.

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29

Koida, Nobuo. "A multiattribute decision time theory." Theory and Decision 83, no. 3 (2017): 407–30. http://dx.doi.org/10.1007/s11238-017-9601-4.

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30

Murali, Komal Patel. "End of Life Decision-Making: Watson’s Theory of Human Caring." Nursing Science Quarterly 33, no. 1 (2019): 73–78. http://dx.doi.org/10.1177/0894318419881807.

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The phenomenon of end-of-life (EOL) decision-making is a lived experience by which individuals or families make decisions about care they will receive prior to death. A postmodern philosophical approach suggests EOL decision-making is a varied contextual phenomenon that is highly influenced by subjectivity. Thus, there is no specific definition for the phenomenon of EOL decision-making. Watson’s theory of human caring complements a postmodern approach in guiding the nursing process of caring for individuals as they experience EOL decision-making.
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31

Martens, Johannes. "Hamilton meets causal decision theory." Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 77 (October 2019): 101187. http://dx.doi.org/10.1016/j.shpsc.2019.101187.

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32

Roberts, Fred S. "Computer science and decision theory." Annals of Operations Research 163, no. 1 (2008): 209–53. http://dx.doi.org/10.1007/s10479-008-0328-z.

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33

Busemeyer, Jerome R., and In Jae Myung. "An adaptive approach to human decision making: Learning theory, decision theory, and human performance." Journal of Experimental Psychology: General 121, no. 2 (1992): 177–94. http://dx.doi.org/10.1037/0096-3445.121.2.177.

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34

Khrennikov, Andrei. "Quantum Bayesianism as the basis of general theory of decision-making." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 374, no. 2068 (2016): 20150245. http://dx.doi.org/10.1098/rsta.2015.0245.

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We discuss the subjective probability interpretation of the quantum-like approach to decision making and more generally to cognition. Our aim is to adopt the subjective probability interpretation of quantum mechanics, quantum Bayesianism (QBism), to serve quantum-like modelling and applications of quantum probability outside of physics. We analyse the classical and quantum probabilistic schemes of probability update, learning and decision-making and emphasize the role of Jeffrey conditioning and its quantum generalizations. Classically, this type of conditioning and corresponding probability update is based on the formula of total probability—one the basic laws of classical probability theory.
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35

DasGupta, Anirban. "A General Theorem on Decision Theory for Nonnegative Functionals: with Applications." Annals of Statistics 17, no. 3 (1989): 1360–74. http://dx.doi.org/10.1214/aos/1176347275.

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36

Bleichrodt, Han, Martin Filko, Amit Kothiyal, and Peter P. Wakker. "Making Case-Based Decision Theory Directly Observable." American Economic Journal: Microeconomics 9, no. 1 (2017): 123–51. http://dx.doi.org/10.1257/mic.20150172.

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Case-based decision theory (CBDT) provided a new way of revealing preferences, with decisions under uncertainty determined by similarities with cases in memory. This paper introduces a method to measure CBDT that requires no commitment to parametric families and that relates directly to decisions. Thus, CBDT becomes directly observable and can be used in prescriptive applications. Two experiments on real estate investments demonstrate the feasibility of our method. Our implementation of real incentives not only avoids the income effect, but also avoids interactions between different memories. We confirm CBDT's predictions except for one violation of separability of cases in memory. (JEL D12, D81, R30)
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37

Lowes, Lesley. "Ethical decision-making: theory to practice." Paediatric Nursing 5, no. 9 (1993): 10–11. http://dx.doi.org/10.7748/paed.5.9.10.s11.

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38

Mowrer, Robert R., and William B. Davidson. "Academic Decision Making and Prospect Theory." Psychological Reports 109, no. 1 (2011): 289–300. http://dx.doi.org/10.2466/01.07.20.pr0.109.4.289-300.

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Two studies are reported that investigate the applicability of prospect theory to college students' academic decision making. Exp. 1 failed to provide support for the risk-seeking portion of the fourfold pattern predicted by prospect theory but did find the greater weighting of losses over gains. Using a more sensitive dependent measure, in Exp. 2 the results of the first experiment were replicated in terms of the gain-loss effect and also found some support for the fourfold pattern in the interaction between probabilities and gain versus loss. The greatest risk-seeking was found in the high probability loss condition.
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39

Boles, Terry L., and Charles G. McClintock. "The Socialization of Behavioral Decision Theory." Contemporary Psychology: A Journal of Reviews 40, no. 8 (1995): 748–49. http://dx.doi.org/10.1037/003864.

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40

SCHILIRO, Daniele. "Economics and Psychology. The Framing of Decisions." Journal of Mathematical Economics and Finance 2, no. 2 (2017): 77. http://dx.doi.org/10.14505//jmef.v2.2(3).05.

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In the Theory of Rational Decision Making the psychological aspects are set aside. This contribution seeks to point out the relevance of psychology into eco- nomic decisions. The essay treats the “framing of decisions”, which is a pillar of Kahneman’s behavioral theory. Framing must be considered a special case of the more general phenomenon of dependency from the representation. The best-known risky choice-framing problem, i.e. the “Asian Disease Problem”, is shown where an essen- tial aspect of rationality: invariance, is violated. In addition, the contribution ex- plains Kahneman and Tversky’s Prospect Theory and illustrates their value function. Finally, it discusses the reversals of preference in framing and framing of contingen- cies. The framing manipulation is viewed as a public tool for influencing the decision maker’s private framing of the problem in terms of gains or losses, which determines the decision maker’s evaluation of the options. In conclusion, the psychology of choice is relevant both for the descriptive question of how decisions are made and for the nor- mative question of how decisions ought to be made.
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41

Ben Amor, Sarah, and Bertrand Mareschal. "Integrating imperfection of information into the promethee multicriteria decision aid methods: a general framework." Foundations of Computing and Decision Sciences 37, no. 1 (2012): 9–23. http://dx.doi.org/10.2478/v10209-011-0002-0.

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Abstract.Multicriteria decision aid methods are used to analyze decision problems including a series of alternative decisions evaluated on several criteria. They most often assume that perfect information is available with respect to the evaluation of the alternative decisions. However, in practice, imprecision, uncertainty or indetermination are often present at least for some criteria. This is a limit of most multicriteria methods. In particular the PROMETHEE methods do not allow directly for taking into account this kind of imperfection of information. We show how a general framework can be adapted to PROMETHEE and can be used in order to integrate different imperfect information models such as a.o. probabilities, fuzzy logic or possibility theory. An important characteristic of the proposed approach is that it makes it possible to use different models for different criteria in the same decision problem.
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42

FRENCH, SIMON. "An introduction to decision theory and prescriptive decision analysis." IMA Journal of Management Mathematics 6, no. 2 (1995): 239–47. http://dx.doi.org/10.1093/imaman/6.2.239.

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43

Roe, Robert M., Jermone R. Busemeyer, and James T. Townsend. "Multialternative decision field theory: A dynamic connectionst model of decision making." Psychological Review 108, no. 2 (2001): 370–92. http://dx.doi.org/10.1037/0033-295x.108.2.370.

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44

Yukalov, Vyacheslav I. "Evolutionary Processes in Quantum Decision Theory." Entropy 22, no. 6 (2020): 681. http://dx.doi.org/10.3390/e22060681.

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The review presents the basics of quantum decision theory, with an emphasis on temporary processes in decision making. The aim is to explain the principal points of the theory. How an operationally-testable, rational choice between alternatives differs from a choice decorated by irrational feelings is elucidated. Quantum-classical correspondence is emphasized. A model of quantum intelligence network is described. Dynamic inconsistencies are shown to be resolved in the frame of the quantum decision theory.
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45

Yukalov, Vyacheslav, and Didier Sornette. "Processing Information in Quantum Decision Theory." Entropy 11, no. 4 (2009): 1073–120. http://dx.doi.org/10.3390/e11041073.

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46

Marcellino, Massimiliano, and Mark Salmon. "ROBUST DECISION THEORY AND THE LUCAS CRITIQUE." Macroeconomic Dynamics 6, no. 1 (2002): 167–85. http://dx.doi.org/10.1017/s1365100502027086.

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In this paper, we reconsider the theoretical basis for the Lucas critique from the point of view of robust decision theory. We first emphasize that the Lucas critique rests on a weak theoretical paradigm in that it fails to consider the motivation for the policy change by the government and hence inconsistently assumes limited rationality by the government. When placed in a proper dynamic general equilibrium framework of a dynamic game between the government and the private sector, much of the force of the critique simply vanishes. We also reconsider the critique by adopting an alternative theoretical paradigm and notion of rationality based on robust decision theory. This view of rationality might be regarded as more relevant than the nonrobust rationality employed by Lucas and, critically, it is one in which the Lucas critique can be shown simply not to apply, provided the private sector has adopted suitably robust decision rules.
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47

Svetlova, E., and H. Van Elst. "How Is Non-knowledge Represented in Economic Theory?" Voprosy Ekonomiki, no. 8 (August 20, 2013): 82–105. http://dx.doi.org/10.32609/0042-8736-2013-8-82-105.

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In this article, we address the question of how non-knowledge about future events that influence economic agents’ decisions in choice settings has been formally represented in economic theory up to date. To position our discussion within the ongoing debate on uncertainty, we provide a?brief review of historical developments in economic theory and decision theory. Uncertainty is thereby understood as either based on decision-making in the context of a?state space representing the exogenous world, as in Savage’s axiomatisation and some successor concepts (ambiguity as situations with unknown probabilities), or, based on decision-making over a?set of menus of potential future opportunities, providingthe possibility of derivation of agents’ subjective state spaces (unawareness as situation with imperfect subjective knowledge of all future events possible).
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48

Hu, Limei, Chunqiao Tan, and Hepu Deng. "An evidence theory based approach for group decision making under uncertainty." Journal of Intelligent & Fuzzy Systems 40, no. 6 (2021): 10837–51. http://dx.doi.org/10.3233/jifs-201846.

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With the changing business environment and the active participation of various stakeholders in the decision making process, it plays an increasingly important role to the weight of decision makers and the preference information given by decision makers. This paper presents a novel approach for group decision making under uncertainty with the involvement of the third-party evaluator in the decision making process. Recognizing the challenge in adequately determining the weight of decision makers in group decision making, the evidence theory is appropriately used with the involvement of the third-party evaluator. To effectively model the uncertainty and imprecision in the decision making process, fuzzy preference relations are used for better representing the subjective assessment of individual decision makers. To adequately determine the ranking of available alternatives, the logarithmic least square method is applied for appropriately aggregating the fuzzy preference relation of individual decision makers. A group consensus index is developed for facilitating consensus building in group decision making. This leads to better group decisions being made. A real-world application is presented that shows the proposed approach is effective in solving group decision making problems under uncertainty.
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49

Pisano, Raffaele, and Sandro Sozzo. "A Unified Theory of Human Judgements and Decision-Making under Uncertainty." Entropy 22, no. 7 (2020): 738. http://dx.doi.org/10.3390/e22070738.

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Growing empirical evidence reveals that traditional set-theoretic structures cannot in general be applied to cognitive phenomena. This has raised several problems, as illustrated, for example, by probability judgement errors and decision-making (DM) errors. We propose here a unified theoretical perspective which applies the mathematical formalism of quantum theory in Hilbert space to cognitive domains. In this perspective, judgements and decisions are described as intrinsically non-deterministic processes which involve a contextual interaction between a conceptual entity and the cognitive context surrounding it. When a given phenomenon is considered, the quantum-theoretic framework identifies entities, states, contexts, properties and outcome statistics, and applies the mathematical formalism of quantum theory to model the considered phenomenon. We explain how the quantum-theoretic framework works in a variety of judgement and decision situations where systematic and significant deviations from classicality occur.
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Gilboa, Itzhak, and Larry Samuelson. "What were you thinking? Decision theory as coherence test." Theoretical Economics 17, no. 2 (2022): 507–19. http://dx.doi.org/10.3982/te4707.

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Abstract:
Decision theory can be used to test the logic of decision making—one may ask whether a given set of decisions can be justified by a decision‐theoretic model. Indeed, in principal–agent settings, such justifications may be required—a manager of an investment fund may be asked what beliefs she used when valuing assets and a government may be asked whether a portfolio of rules and regulations is coherent. In this paper we ask which collections of uncertain‐act evaluations can be simultaneously justified under the maxmin expected utility criterion by a single set of probabilities. We draw connections to the fundamental theorem of finance (for the special case of a Bayesian agent) and revealed‐preference results.
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