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  1. J. McKenzie Alexander (2012). Decision Theory Meets the Witch of Agnesi. Journal of Philosophy 109 (12):712-727.
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  2. Gustaf Arrhenius & Wlodek Rabinowicz (2005). Value and Unacceptable Risk. Economics and Philosophy 21 (2):177-197.
    Consider a transitive value ordering of outcomes and lotteries on outcomes, which satisfies substitutivity of equivalents and obeys “continuity for easy cases,” i.e., allows compensating risks of small losses by chances of small improvements. Temkin (2001) has argued that such an ordering must also – rather counter-intuitively – allow chances of small improvements to compensate risks of huge losses. In this paper, we show that Temkin's argument is flawed but that a better proof is possible. However, it is more difficult (...)
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  3. Marcus Arvan (2015). How to Rationally Approach Life's Transformative Experiences. Philosophical Psychology 28 (8):1199-1218.
    In a widely discussed forthcoming article, “What you can't expect when you're expecting,” L. A. Paul challenges culturally and philosophically traditional views about how to rationally make major life-decisions, most specifically the decision of whether to have children. The present paper argues that because major life-decisions are transformative, the only rational way to approach them is to become resilient people: people who do not “over-plan” their lives or expect their lives to play out “according to plan”—people who understand that beyond (...)
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  4. Robert Audi (1986). Action Theory as a Resource for Decision Theory. Theory and Decision 20 (3):207-221.
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  5. S. K. Berninghaus, S. J. Brams, P. H. Edelman, J. Esteban, I. Fischer, P. C. Fishburn, G. Gigliotti, W. Güth, R. D. Luce & P. Modesti (2003). Theory and Decision. Theory and Decision 55 (392).
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  6. Magnus Boman (1999). Norms in Artificial Decision Making. Artificial Intelligence and Law 7 (1):17-35.
    A method for forcing norms onto individual agents in a multi-agent system is presented. The agents under study are supersoft agents: autonomous artificial agents programmed to represent and evaluate vague and imprecise information. Agents are further assumed to act in accordance with advice obtained from a normative decision module, with which they can communicate. Norms act as global constraints on the evaluations performed in the decision module and hence no action that violates a norm will be suggested to any agent. (...)
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  7. Michael Clark & Nicholas Shackel (2003). Decision Theory, Symmetry and Causal Structure: Reply to Meacham and Weisberg. Mind 112 (448):691-701.
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  8. Franz Dietrich, How to Reach Legitimate Decisions When the Procedure is Controversial.
    Imagine a group that faces a decision problem but does not agree on which decision procedure is appropriate. In that case, can a decision be reached that respects the procedural concerns of the group? There is a sense in which legitimate decisions are possible even if people disagree on which procedure to use. I propose to decide in favour of an option which maximizes the number of persons whose judged-right procedure happens to entail this decision given the profile. This decision (...)
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  9. Franz Dietrich & Christian List (2016). Reason-Based Choice and Context-Dependence: An Explanatory Framework. Economics and Philosophy 32 (2):175-229.
    We introduce a “reason-based” framework for explaining and predicting individual choices. It captures the idea that a decision-maker focuses on some but not all properties of the options and chooses an option whose motivationally salient properties he/she most prefers. Reason-based explanations allow us to distinguish between two kinds of context-dependent choice: the motivationally salient properties may (i) vary across choice contexts, and (ii) include not only “intrinsic” properties of the options, but also “context-related” properties. Our framework can accommodate boundedly rational (...)
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  10. Franz Dietrich, Christian List & Richard Bradley (2016). Belief Revision Generalized: A Joint Characterization of Bayes's and Jeffrey's Rules. Journal of Economic Theory 162:352-371.
    We present a general framework for representing belief-revision rules and use it to characterize Bayes's rule as a classical example and Jeffrey's rule as a non-classical one. In Jeffrey's rule, the input to a belief revision is not simply the information that some event has occurred, as in Bayes's rule, but a new assignment of probabilities to some events. Despite their differences, Bayes's and Jeffrey's rules can be characterized in terms of the same axioms: "responsiveness", which requires that revised beliefs (...)
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  11. R. Allen Gardner & John B. Forsythe (1961). Supplementary Report: Two-Choice Decision Behavior with Many Alternative Events. Journal of Experimental Psychology 62 (6):631.
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  12. Hilary Greaves (2013). Epistemic Decision Theory. Mind 122 (488):915-952.
    I explore the prospects for modelling epistemic rationality (in the probabilist setting) via an epistemic decision theory, in a consequentialist spirit. Previous work has focused on cases in which the truth-values of the propositions in which the agent is selecting credences do not depend, either causally or merely evidentially, on the agent’s choice of credences. Relaxing that restriction leads to a proliferation of puzzle cases and theories to deal with them, including epistemic analogues of evidential and causal decision theory, and (...)
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  13. York Hagmayer & Björn Meder (2008). Causal Learning Through Repeated Decision Making. In B. C. Love, K. McRae & V. M. Sloutsky (eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. Cognitive Science Society 179--184.
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  14. Alan Hájek & Harris Nover (2006). Perplexing Expectations. Mind 115 (459):703 - 720.
    This paper revisits the Pasadena game (Nover and Háyek 2004), a St Petersburg-like game whose expectation is undefined. We discuss serveral respects in which the Pasadena game is even more troublesome for decision theory than the St Petersburg game. Colyvan (2006) argues that the decision problem of whether or not to play the Pasadena game is ‘ill-posed’. He goes on to advocate a ‘pluralism’ regarding decision rules, which embraces dominance reasoning as well as maximizing expected utility. We rebut Colyvan’s argument, (...)
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  15. C. A. Hooker, J. J. Leach & E. F. Mcclennen (1980). Foundations and Applications of Decision Theory. Philosophy and Phenomenological Research 41 (1):252-254.
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  16. James Joyce (1995). Decision Theory. Philosophical Books 36 (4):225-237.
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  17. James Michael Joyce (1992). The Axiomatic Foundations of Bayesian Decision Theory. Dissertation, University of Michigan
    Bayesian decision theorists argue that rational agents should always perform acts that maximize subjective expected utility. To justify this claim, they prove representation theorems which are designed to show that any decision maker whose beliefs and desires satisfy reasonable axiomatic constraints will necessarily behave like an expected utility maximizer. The existence of such a representation result is a prerequisite for any adequate account of rational choice because one is only able to determine what a decision theory says about beliefs and (...)
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  18. Joseph B. Kadane, Teddy Seidenfeld & Mark J. Schervish, A Rubinesque Theory of Decision.
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  19. Mortimer Raymond Kadish (1950). Toward a Theory of Decision. Dissertation, Columbia University
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  20. Gary Malinas (1993). Reflective Coherence and Newcomb Problems: A Simple Solution. Theory and Decision 35 (2):151-166.
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  21. Laurence T. Maloney (2002). Statistical Decision Theory and Biological Vision. In Dieter Heyer & Rainer Mausfeld (eds.), Perception and the Physical World. Wiley 145--189.
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  22. Kenneth L. Manders (1979). The Theory of All Substructures of a Structure: Characterisation and Decision Problems. Journal of Symbolic Logic 44 (4):583-598.
    An infinitary characterisation of the first-order sentences true in all substructures of a structure M is used to obtain partial reduction of the decision problem for such sentences to that for Th(M). For the relational structure $\langle\mathbf{R}, \leq, +\rangle$ this gives a decision procedure for the ∃ x∀ y-part of the theory of all substructures, yet we show that the ∃ x 1x 2 ∀ y-part, and the entire theory, is Π 1 1 -complete. The theory of all ordered subsemigroups (...)
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  23. Howard Margolis (2000). Simple Heuristics That Make Us Dumb. Behavioral and Brain Sciences 23 (5):758-758.
    The simple heuristics that may indeed usually make us smart–or at least smart enough–in contexts of individual choice will sometimes make us dumb, especially in contexts of social choice. Here each individual choice (or vote) has little impact on the overall choice, although the overall choice is compounded out of the individual choices. I use an example (risk aversion) to illustrate the point.
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  24. Herman Mcdaniel (1970). Applications of Decision Tables a Reader.
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  25. Björn Meder & York Hagmayer (2009). Causal Induction Enables Adaptive Decision Making. In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society.
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  26. A. Morton (1999). Mark Kaplan, Decision Theory as Philosophy. British Journal for the Philosophy of Science 50:505-507.
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  27. Sahlin Nils-Eric, Vareman Niklas, Galavotti Maria Carla & Scazzieri Roberto (2008). Three Types of Decision Theory. In Maria-Carla Galavotti (ed.), Reasoning, Rationality and Probability. Csli Publications
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  28. Alastair Norcross (1998). Great Harms From Small Benefits Grow: How Death Can Be Outweighed by Headaches. Analysis 58 (2):152–158.
    Suppose that a very large number of people, say one billion, will suffer a moderately severe headache for the next twenty-four hours. For these billion people, the next twenty-four hours will be fairly unpleasant, though by no means unbearable. However, there will be no side-effects from these headaches; no drop in productivity in the work-place, no lapses in concentration leading to accidents, no unkind words spoken to loved ones that will later fester. Nonetheless, it is clearly desirable that these billion (...)
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  29. J. M. P. (1966). The Logic of Decision. [REVIEW] Review of Metaphysics 19 (4):813-814.
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  30. S. Pandey (1980). The Logic of Decision. Indian Philosophical Quarterly 8 (1):77.
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  31. Philip Pettit (1989). Decision Theory, Political Theory and the Hats Hypothesis. In Fred D'Agostino & I. C. Jarvie (eds.), Freedom and Rationality. Reidel 23--34.
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  32. Solomon L. Pollack, Harry T. Hicks & William J. Harrison (1971). Decision Tables Theory and Practice.
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  33. John L. Pollock (2011). Locally Global Planning. Thinking About Acting.
    This chapter reiterates the proposition that practical cognition should not aim at finding optimal solutions to practical problems. A rational cognizer should instead look for good solutions, and replace them with better solutions if any are found. Solutions come in the form of plans. In general, a change to the master plan may consist of deleting several local plans and adding several others. This theory is still fairly schematic. It leaves most details to the imagination of the reader, and in (...)
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  34. Wlodek Rabinowicz (2000). Value and Choice Some Common Themes in Decision Theory and Moral Philosophy. Lund Universitetstrycheriet.
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  35. Gerard Radnitzky (1984). Sicence, Tecnology, and Political Decision. From the Creation of a Theory to the Consequences of its Application. Revista Portuguesa de Filosofia 40 (3):307-317.
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  36. Susanna Rinard (2015). A Decision Theory for Imprecise Probabilities. Philosophers' Imprint 15 (7).
    Those who model doxastic states with a set of probability functions, rather than a single function, face a pressing challenge: can they provide a plausible decision theory compatible with their view? Adam Elga and others claim that they cannot, and that the set of functions model should be rejected for this reason. This paper aims to answer this challenge. The key insight is that the set of functions model can be seen as an instance of the supervaluationist approach to vagueness (...)
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  37. Nils-Eric Sahlin & Niklas Vareman (2008). Three Types of Decision Theory. In Maria-Carla Galavotti (ed.), Reasoning, Rationality and Probability. Csli Publications 37--59.
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  38. Wolfgang Schwarz (2015). Lost Memories and Useless Coins: Revisiting the Absentminded Driver. Synthese 192 (9):3011-3036.
    The puzzle of the absentminded driver combines an unstable decision problem with a version of the Sleeping Beauty problem. Its analysis depends on the choice between “halfing” and “thirding” as well as that between “evidential” and “causal” decision theory. I show that all four combinations lead to interestingly different solutions, and draw some general lessons about the formulation of causal decision theory, the interpretation of mixed strategies and the connection between rational credence and objective chance.
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  39. John Shoemaker, A Critical Look At Mark Kaplan's "Decision Theory As Philosophy". Proceedings of the Heraclitean Society 21.
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  40. J. H. Sobel (1999). Kaplan, M.-Decision Theory as Philosophy. Philosophical Books 40:62-64.
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  41. Patrick Suppes (1967). Decision Theory. In Paul Edwards (ed.), The Encyclopedia of Philosophy. New York, Macmillan 2--310.
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  42. Patrick Suppes (1961). The Philosophical Relevance of Decision Theory. Journal of Philosophy 58 (21):605-614.
  43. Mc-T. Tai (2003). Who Makes the Decision? Synthesis Philosophica 18 (1/2):355-364.
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  44. Nassim N. Taleb & Avital Pilpel (2007). Epistemology and Risk Management. Risk and Regulation 13:6--7.
  45. Paul Weirich (1996). Mark Kaplan, Decision Theory as Philosophy. [REVIEW] Philosophy in Review 16 (3):179-180.
    Mark Kaplan proposes amending decision theory to accommodate better cases in which an agent's probability assignment is imprecise. The review describes and evaluates his proposals.
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Causal Decision Theory
  1. A. Ahmed (2013). Causal Decision Theory: A Counterexample. Philosophical Review 122 (2):289-306.
    The essay presents a novel counterexample to Causal Decision Theory (CDT). Its interest is that it generates a case in which CDT violates the very principles that motivated it in the first place. The essay argues that the objection applies to all extant formulations of CDT and that the only way out for that theory is a modification of it that entails incompatibilism. The essay invites the reader to find this consequence of CDT a reason to reject it.
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  2. Arif Ahmed, Smokers and Psychos: Egan Cases Don't Work.
    Andy Egan's Smoking Lesion and Psycho Button cases are supposed to be counterexamples to Causal Decision Theory. This paper argues that they are not: more precisely, it argues that if CDT makes the right call in Newcomb's problem then it makes the right call in Egan cases too.
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  3. Arif Ahmed (2015). Infallibility in the Newcomb Problem. Erkenntnis 80 (2):261-273.
    It is intuitively attractive to think that it makes a difference in Newcomb’s problem whether or not the predictor is infallible, in the sense of being certainly actually correct. This paper argues that that view is irrational and manifests a well-documented cognitive illusion.
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  4. Arif Ahmed (2014). Causal Decision Theory and the Fixity of the Past. British Journal for the Philosophy of Science 65 (4):665-685.
    Causal decision theory (CDT) cares only about the effects of a contemplated act, not its causes. The article constructs a case in which CDT consequently recommends a bet that the agent is certain to lose, rather than a bet that she is certain to win. CDT is plainly giving wrong advice in this case. It therefore stands refuted. 1 The Argument2 The Argument in More Detail2.1 The betting mechanism2.2 Soft determinism2.3 The content of P 2.4 The argument again3 The Descriptive (...)
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  5. Arif Ahmed (2014). Dicing with Death. Analysis 74 (4):587-592.
    You should rather play hide-and-seek against someone who cannot predict where you hide than against someone who can, as the article illustrates in connection with a high-stakes example. Causal Decision Theory denies this. So Causal Decision Theory is false.
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