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  1. John Pollock, An Objectivist Argument for Thirdism.
    with the OSCAR seminar: Adam Arico, Nathan Ballantyne, Matt Bedke, Jacob Caton, Ian Evans, Don Fallis, Brian Fiala, Martin Frické, David Glick, Peter Gross, Terry Horgan, Jenann Ismael, Daniel Sanderman, Paul Thorn, Orlin Vakarelov, Analysis, forthcoming.
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  2. John Pollock, Language and Thought.
    Princeton University Press, 1982. This book is out of print, but can be downloaded as a pdf file (5 MB).
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  3. John Pollock, The Foundations of Philosophical Semantics.
    Princeton University Press, 984. This book is out of print, but can be downloaded as a pdf file (3.9 MB).
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  4. Jenann Ismael & John L. Pollock, So You Think You Exist? — In Defense of Nolipsism.
    Human beings think of themselves in terms of a privileged non-descriptive designator — a mental “I”. Such thoughts are called “de se” thoughts. The mind/body problem is the problem of deciding what kind of thing I am, and it can be regarded as arising from the fact that we think of ourselves non-descriptively. Why do we think of ourselves in this way? We investigate the functional role of “I” (and also “here” and “now”) in cognition, arguing that the use of (...)
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  5. John L. Pollock, An Easy “Hard Problem” for Decision-Theoretic Planning.
    This paper presents a challenge problem for decision-theoretic planners. State-space planners reason globally, building a map of the parts of the world relevant to the planning problem, and then attempt to distill a plan out of the map. A planning problem is constructed that humans find trivial, but no state-space planner can solve. Existing POCL planners cannot solve the problem either, but for a less fundamental reason.
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  6. John L. Pollock, Against Optimality: Logical Foundations for Decision-Theoretic Planning in Autonomous Agents.
    This paper investigates decision-theoretic planning in sophisticated autonomous agents operating in environments of real-world complexity. An example might be a planetary rover exploring a largely unknown planet. It is argued th a t existing algorithms for decision-theoretic planning are based on a logically incorrect theory of rational decision making. Plans cannot be evaluated directly in terms of their expected values, because plans can be of different scopes, and they can interact with other previously adopted plans. Furthermore, in the real world, (...)
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  7. John L. Pollock, Epistemology: Five Questions.
    As a high school student, I rediscovered Hume’s problem of induction on my own. For a while, I was horrified. I thought, “We cannot know anything!” After a couple of weeks I calmed down and reasoned that there had to be something wrong with my thinking, and that led me quickly to the realization that good reasons need not be deductive, and to the discovery of defeasible reasoning. From there it was a short jump to a more general interest in (...)
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  8. John L. Pollock, Locally Global Planning.
    It is conjectured that MDP and POMDP planning will remain unfeasible for complex domains, so some form of ÒclassicalÓ decision-theoretic planning is sought. However, local plans cannot be properly compared in terms of their expected values, because those values will be affected by the other plans the agent has adopted. Plans must instead be merged into a single Òmaster-planÓ, and new plans evaluated in terms of their contribution to the value of the master plan. To make both the construction and (...)
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  9. John L. Pollock, Probabilities for AI.
    Probability plays an essential role in many branches of AI, where it is typically assumed that we have a complete probability distribution when addressing a problem. But this is unrealistic for problems of real-world complexity. Statistical investigation gives us knowledge of some probabilities, but we generally want to know many others that are not directly revealed by our data. For instance, we may know prob(P/Q) (the probability of P given Q) and prob(P/R), but what we really want is prob(P/Q&R), and (...)
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  10. John L. Pollock, Part One: Virtual Machines.
    It’s morning. You sit down at your desk, cup of coffee in hand, and prepare to begin your day. First, you turn on your computer. Once it is running, you check your e-mail. Having decided it is all spam, you trash it. You close the window on your e-mail program, but leave the program running so that it will periodically check the mail server to see whether you have new mail. If it finds new mail it will alert you by (...)
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  11. John L. Pollock, Thinking About Acting.
    The objective of this book is to produce a theory of rational decision making for realistically resource-bounded agents. My interest is not in “What should I do if I were an ideal agent?”, but rather, “What should I do given that I am who I am, with all my actual cognitive limitations?” The book has three parts. Part One addresses the question of where the values come from that agents use in rational decision making. The most comon view among philosophers (...)
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  12. John L. Pollock, The Need for an Epistemology.
    It is argued that we cannot build a sophisticated autonomous planetary rover just by implementing sophisticated planning algorithms. Planning must be based on information, and the agent must have the cognitive capability of acquiring new information about its environment. That requires the implementation of a sophisticated epistemology. Epistemological considerations indicate that the rover cannot be assumed to have a complete probability distribution at its disposal. Its planning must be based upon “thin” knowledge of probabilities, and that has important implications for (...)
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  13. John Pollock, Oscar: A Cognitive Architecture for Intelligent Agents.
    The “grand problem” of AI has always been to build artificial agents with human-like intelligence. That is the stuff of science fiction, but it is also the ultimate aspiration of AI. In retrospect, we can understand what a difficult problem this is, so since its inception AI has focused more on small manageable problems, with the hope that progress there will have useful implications for the grand problem. Now there is a resurgence of interest in tackling the grand problem head-on. (...)
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  14. John Pollock, The Foundations of Philosophical Semantics.
    Princeton University Press, 984. This book is out of print, but can be downloaded as a pdf file (3.9 MB).
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  15. John Pollock, A Recursive Semantics for Defeasible Reasoning.
    One of the most striking characteristics of human beings is their ability to function successfully in complex environments about which they know very little. In light of our pervasive ignorance, we cannot get around in the world just reasoning deductively from our prior beliefs together with new perceptual input. As our conclusions are not guaranteed to be true, we must countenance the possibility that new information will lead us to change our minds, withdrawing previously adopted beliefs. In this sense, our (...)
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  16. John Pollock, Direct Inference and Probable Probabilities.
    New results in the theory of nomic probability have led to a theory of probable probabilities, which licenses defeasible inferences between probabilities that are not validated by the probability calculus. Among these are classical principles of direct inference together with some new more general principles that greatly strengthen direct inference and make it much more useful.
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  17. John Pollock, Epistemology, Rationality, and Cognition.
    Since Gettier, much of epistemology has focused on analyzing “S knows that P”, but that is not my interest. My general interest is in rational cognition — both in what it is to be rational, and in how rational cognition works. The traditional epistemological question, “How do you know?”, can be taken as addressing part of the more general problem of producing a theory of rational cognition. It is about specifically epistemic rationality. I interpret this question literally, as a question (...)
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  18. John Pollock, Interest Driven Suppositional Reasoning.
    The aim of this paper is to investigate two related aspects of human reasoning, and use the results to construct an automated theorem prover for the predicate calculus that at least approximately models human reasoning. The result is a non-resolution theorem prover that does not use Skolemization. It involves two central ideas. One is the interest constraints that are of central importance in guiding human reasoning. The other is the notion of suppositional reasoning, wherein one makes a supposition, draws inferences (...)
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  19. John Pollock, Joint Probabilities.
    When combining information from multiple sources and attempting to estimate the probability of a conclusion, we often find ourselves in the position of knowing the probability of the conclusion conditional on each of the individual sources, but we have no direct information about the probability of the conclusion conditional on the combination of sources. The probability calculus provides no way of computing such joint probabilities. This paper introduces a new way of combining probabilistic information to estimate joint probabilities. It is (...)
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  20. John Pollock, Logic: An Introduction to the Formal Study of Reasoning.
    This is a text for an introductory symbolic logic course. It is based upon an old text that I wrote in 1969, which is long out of print. But it modifies the approach of that book to reflect theoretical work that I have done on theorem proving in the..
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  21. John Pollock, Natural Deduction.
    Most automated theorem provers are clausal-form provers based on variants of resolutionrefutation. In my [1990], I described the theorem prover OSCAR that was based instead on natural deduction. Some limited evidence was given suggesting that OSCAR was suprisingly efficient. The evidence consisted of a handful of problems for which published data was available describing the performance of other theorem provers. This evidence was suggestive, but based upon too meager a comparison to be conclusive. The question remained, “How does natural deduction (...)
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  22. John Pollock, Oscar: An Agent Architecture Based on Defeasible Reasoning.
    Proceedings of the 2008 AAAI Spring Symposium on Architectures for Intelligent Theory-Based Agents. “OSCAR is a fully implemented architecture for a cognitive agent, based largely on the author’s work in philosophy concerning epistemology and practical cognition. The seminal idea is that a generally intelligent agent must be able to function in an environment in which it is ignorant of most matters of fact. The architecture incorporates a general-purpose defeasible reasoner, built on top of an efficient natural deduction reasoner for first-order (...)
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  23. John Pollock, Oscar: An Architecture for Generally Intelligent Agents.
    OSCAR is a fully implemented architecture for a cognitive agent, based largely on the author’s work in philosophy concerning epistemology and practical cognition. The seminal idea is that a generally intelligent agent must be able to function in an environment in which it is ignorant of most matters of fact. The architecture incorporates a general-purpose defeasible reasoner, built on top of an efficient natural deduction reasoner for first-order logic. It is based upon a detailed theory about how the various aspects (...)
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  24. John Pollock, Planning Agents.
    Agents are entities that act upon the world. Rational agents are those that do so in an intelligent fashion. What is essential to such an agent is the ability to select and perform actions. Actions are selected by planning, and performing such actions is a matter of plan execution. So the essence of a rational agent is the ability to make and execute plans. This constitutes practical cognition. In order to perform its principal function of practical cognition, a rational agent (...)
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  25. John Pollock, Perceiving and Reasoning About a Changing World.
    A rational agent (artificial or otherwise) residing in a complex changing environment must gather information perceptually, update that information as the world changes, and combine that information with causal information to reason about the changing world. Using the system of defeasible reasoning that is incorporated into the OSCAR architecture for rational agents, a set of reasonschemas is proposed for enabling an agent to perform some of the requisite reasoning. Along the way, solutions are proposed for the Frame Problem, the Qualification (...)
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  26. John Pollock, Probable Probabilities.
    In concrete applications of probability, statistical investigation gives us knowledge of some probabilities, but we generally want to know many others that are not directly revealed by our data. For instance, we may know prob(P/Q) (the probability of P given Q) and prob(P/R), but what we really want is prob(P/Q&R), and we may not have the data required to assess that directly. The probability calculus is of no help here. Given prob(P/Q) and prob(P/R), it is consistent with the probability calculus (...)
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  27. John Pollock, Reasoning Defeasibly About Plans.
    This technical report describes the construction of an experimental planner that finds plans by reasoning about them defeasibly rather than by running a search algorithm. The need for such a planner is defended in the paper “The Logical Foundations of Goal-Regression Planning”.
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  28. John Pollock, Rational Decision-Making in Resource-Bounded Agents.
    The objective of this paper is to construct an implementable theory of rational decision-making for cognitive agents subject to realistic resource constraints. It is argued that decision-making should select actions indirectly by selecting plans that prescribe them. It is also argued that although expected values provide the tool for evaluating plans, plans cannot be compared straightforwardly in terms of their expected values, and the objective of a realistic agent cannot be to find optimal plans. The theory of Locally Global planning (...)
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  29. John Pollock, Some Logical Conundrums for Decision-Theoretic Contingency Planning.
    There are two general approaches to handling contingencies in decision-theoretic planning. State-space planners reason globally, building a map of the parts of the world relevant to the planning problem, and then attempt to distill a plan out of the map. POCL planners reason locally, attempting to build the plan up from local relationships. A planning problem is constructed that humans find trivial, but no state-space planner can solve. This motivates an investigation of decision-theoretic POCL contingency planners. Existing POCL contingency planners (...)
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  30. John Pollock, The Logical Foundations of Decision-Theoretic Planning in Autonomous Agents.
    Decision-theoretic planning is normally based on the assumption that plans can be compared by comparing their expected-values, and the objective is to find an optimal plan. This is typically defended by reference to classical decision theory. However, classical decision theory is actually incompatible with this “simple plan-based decision theory”. A defense of plan-based decision theory must begin by showing that classical decision theory is incorrect insofar as the two theories conflict, so this paper begins by raising objections to classical decision (...)
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  31. John Pollock, The Logical Foundations of Goal-Regression Planning in Autonomous Agents.
    This paper addresses the logical foundations of goal-regression planning in autonomous rational agents. It focuses mainly on three problems. The first is that goals and subgoals will often be conjunctions, and to apply goal-regression planning to a conjunction we usually have to plan separately for the conjuncts and then combine the resulting subplans. A logical problem arises from the fact that the subplans may destructively interfere with each other. This problem has been partially solved in the AI literature (e.g., in (...)
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  32. John Pollock, The y-Function.
    Direct inference derives values for definite (single-case) probabilities from those of related indefinite (general) probabilities. But direct inference is less useful than might be supposed, because we often have too much information, with the result that we can make conflicting direct inferences, and hence they all undergo collective defeat, leaving us without any conclusion to draw about the value of the definite probabilities. This paper presents reason for believing that there is a function — the Y- function — that can (...)
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  33. John L. Pollock, Language and Thought.
    Princeton University Press, 1982. This book is out of print, but can be downloaded as a pdf file (5 MB).
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  34. John L. Pollock, Problems for Bayesian Epistemology.
    In the past, few mainstream epistemologists have endorsed Bayesian epistemology, feeling that it fails to capture the complex structure of epistemic cognition. The defenders of Bayesian epistemology have tended to be probability theorists rather than epistemologists, and I have always suspected they were more attracted by its mathematical elegance than its epistemological realism. But recently Bayesian epistemology has gained a following among younger mainstream epistemologists. I think it is time to rehearse some of the simpler but still quite devastating objections (...)
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  35. John L. Pollock (forthcoming). Chisholm on States of Affairs. Grazer Philosophische Studien 7:163-175.
    Chisholm's ontological objective is the reductionist one of translating statements which appear to be about propositions and generic events into statements about states of affairs, denying the existence of concrete events altogether. The paper questions this program by criticising the notion of concretization on which Chisholm heavily relies. It is argued that there are no convincing arguments in favor of eliminative reductionism. Translability of statements about one kind of entity into statements about another kind of entity has nothing to do (...)
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  36. John L. Pollock (2012). Oscar. Journal of Applied Non-Classical Logics 6 (1):89-113.
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  37. John Pollock (2011). Reasoning Defeasibly About Probabilities. Synthese 181 (2):317 - 352.
    In concrete applications of probability, statistical investigation gives us knowledge of some probabilities, but we generally want to know many others that are not directly revealed by our data. For instance, we may know prob(P/Q) (the probability of P given Q) and prob(P/R), but what we really want is prob(P/Q& R), and we may not have the data required to assess that directly. The probability calculus is of no help here. Given prob(P/Q) and prob(P/R), it is consistent with the probability (...)
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  38. John L. Pollock (2010). A Resource-Bounded Agent Addresses the Newcomb Problem. Synthese 176 (1):57 - 82.
    In the Newcomb problem, the standard arguments for taking either one box or both boxes adduce what seem to be relevant considerations, but they are not complete arguments, and attempts to complete the arguments rely upon incorrect principles of rational decision making. It is argued that by considering how the predictor is making his prediction, we can generate a more complete argument, and this in turn supports a form of causal decision theory.
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  39. John L. Pollock (2010). 10 Reasoning Defeasibly About Probabilities. In Joseph Keim Campbell, Michael O.’Rourke & Harry S. Silverstein (eds.), Knowledge and Skepticism. Mit Press. 219.
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  40. Permutation City, Vanilla Sky, John Pollock, Nick Bostrom & René Descartes (2009). Could I Be in a “Matrix” or Computer Simulation? In Susan Schneider (ed.), Science Fiction and Philosophy: From Time Travel to Superintelligence. Wiley-Blackwell.
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  41. John Pollock (2009). Brain in a Vat. In Susan Schneider (ed.), Science Fiction and Philosophy: From Time Travel to Superintelligence. Wiley-Blackwell. 17--19.
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  42. John L. Pollock (2008). Irrationality and Cognition. In Quentin Smith (ed.), Epistemology: New Essays. Oxford University Press.
    The strategy of this paper is to throw light on rational cognition and epistemic justification by examining irrationality. Epistemic irrationality is possible because we are reflexive cognizers, able to reason about and redirect some aspects of our own cognition. One consequence of this is that one cannot give a theory of epistemic rationality or epistemic justification without simultaneously giving a theory of practical rationality. A further consequence is that practical irrationality can affect our epistemic cognition. I argue that practical irrationality (...)
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  43. John L. Pollock (2008). What Am I? Philosophy and Phenomenological Research 76 (2):237-309.
    It’s morning. You sit down at your desk, cup of coffee in hand, and prepare to begin your day. First, you turn on your computer. Once it is running, you check your e-mail. Having decided it is all spam, you trash it. You close the window on your e-mail program, but leave the program running so that it will periodically check the mail server to see whether you have new mail. If it finds new mail it will alert you by (...)
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  44. John L. Pollock (2008). What Am I? Virtual Machines and the Mind/Body Problem. Philosophy and Phenomenological Research 76 (2):237–309.
    When your word processor or email program is running on your computer, this creates a "virtual machine” that manipulates windows, files, text, etc. What is this virtual machine, and what are the virtual objects it manipulates? Many standard arguments in the philosophy of mind have exact analogues for virtual machines and virtual objects, but we do not want to draw the wild metaphysical conclusions that have sometimes tempted philosophers in the philosophy of mind. A computer file is not made of (...)
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  45. Ian Evans, Don Fallis, Peter Gross, Terry Horgan, Jenann Ismael, John Pollock, Paul D. Thorn, Jacob N. Caton, Adam Arico, Daniel Sanderman, Orlin Vakerelov, Nathan Ballantyne, Matthew S. Bedke, Brian Fiala & Martin Fricke (2007). An Objectivist Argument for Thirdism. Analysis 68.
    Bayesians take “definite” or “single-case” probabilities to be basic. Definite probabilities attach to closed formulas or propositions. We write them here using small caps: PROB(P) and PROB(P/Q). Most objective probability theories begin instead with “indefinite” or “general” probabilities (sometimes called “statistical probabilities”). Indefinite probabilities attach to open formulas or propositions. We write indefinite probabilities using lower case “prob” and free variables: prob(Bx/Ax). The indefinite probability of an A being a B is not about any particular A, but rather about the (...)
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  46. John Pollock (2006). Thinking About Acting: Logical Foundations for Rational Decision Making. Oxford University Press, Usa.
    Pollock argues that theories of ideal rationality are largely irrelevant to the decision making of real agents. Thinking about Acting aims to provide a theory of "real rationality.".
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  47. John L. Pollock & Jenann Ismael (2006). So You Think You Exist? — In Defense of Nolipsism. In Thomas M. Crisp, Matthew Davidson & David Vander Laan (eds.), Knowledge and Reality: Essays in Honor of Alvin Plantinga. Springer.
    Human beings think of themselves in terms of a privileged non-descriptive designator — a mental “I”. Such thoughts are called “_de se_” thoughts. The mind/body problem is the problem of deciding what kind of thing I am, and it can be regarded as arising from the fact that we think of ourselves non-descriptively. Why do we think of ourselves in this way? We investigate the functional role of “I” (and also “here” and “now”) in cognition, arguing that the use of (...)
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  48. John L. Pollock & Iris Oved (2005). Vision, Knowledge, and the Mystery Link. Noûs 39 (1):309-351.
    Imagine yourself sitting on your front porch, sipping your morning coffee and admiring the scene before you. You see trees, houses, people, automobiles; you see a cat running across the road, and a bee buzzing among the flowers. You see that the flowers are yellow, and blowing in the wind. You see that the people are moving about, many of them on bicycles. You see that the houses are painted different colors, mostly earth tones, and most are one-story but a (...)
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  49. Joe Cruz & John Pollock (2004). The Chimerical Appeal of Epistemic Externalism. In Richard Schantz (ed.), The Externalist Challenge. De Gruyter. 125--42.
    Internalism in epistemology is the view that all the factors relevant to the justification of a belief are importantly internal to the believer, while externalism is the view that at least some of those factors are external. This extremely modest first approximation cries out for refinement (which we undertake below), but is enough to orient us in the right direction, namely that the debate between internalism and externalism is bound up with the controversy over the correct account of the distinction (...)
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  50. John Pollock (2004). Plans And Decisions. Theory and Decision 57 (2):79-107.
    Counterexamples are constructed for classical decision theory, turning on the fact that actions must often be chosen in groups rather than individually, i.e., the objects of rational choice are plans. It is argued that there is no way to define optimality for plans that makes the finding of optimal plans the desideratum of rational decision-making. An alternative called “locally global planning” is proposed as a replacement for classical decision theory. Decision-making becomes a non-terminating process without a precise target rather than (...)
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