Realistic Decision Theory: Rules for Nonideal Agents in Nonideal Circumstances
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Skickas inom vardagar. Laddas ned direkt. Within traditional decision theory, common decision principles - e. In Realistic Decision Theory, Paul Weirch adds practicality to decision theory by formulating principles applying to nonideal agents in nonideal circumstances, such as real people coping with complex decisions.
Bridging the gap between normative demands and psychological resources, Realistic Decision Theory is essential reading for theorists seeking precise normative decision principles that acknowledge the limits and difficulties of human decision-making. Passar bra ihop. Moreover, it does not establish that an evidential theory of rational desire agrees with a causal theory of rational desire. He concludes that even in cases where evidential decision theory yields the right recommendation, it does not yield it for the right reasons.
Price proposes a blend of evidential and causal decision theory and motivates it with an analysis of cases in which an agent has foreknowledge of an event occurring by chance.
Rationality and Relativism. Optimizing Optimizing is performing the best of available acts, or, more precisely, an act at least as good as every other available act. In realistic cases in which sentences are only partially interpreted and perhaps without a truth value, it still has a role to play in a theory of truth. Lack of information impedes compliance with this principle. My approach to decision theory adopts traditional philosophical objectives. As for an intuitionist conception of rationality, if the relevant self-evident truths do not concern what is morally appropriate, then rational immorality will be possible.
Causal decision theory on its own accommodates such cases, argues Adam Bales Arif Ahmed champions evidential decision theory and advances several objections to causal decision theory. His objections assume some controversial points about rational choice, including a controversial principle for sequences of choices.
A common view distinguishes principles for evaluating choices from principles for evaluating sequences of choices. She realizes a sequence of multiple choices only by making each choice in the sequence at the time for it; she cannot at will immediately realize the entire sequence. Adopting this common method of evaluating sequences of choices fends off objections to causal decision theory that assume rival methods. Decision theory is an active area of research. Current work addresses a number of problems. Principles of causal decision theory use probabilities and utilities.
The interpretation of probabilities and utilities is a matter of debate. One tradition defines them in terms of functions that representation theorems introduce to depict preferences. The representation theorems show that if preferences meet certain structural axioms, then if they also meet certain normative axioms, they are as if they follow expected utility. That is, preferences follow expected utility calculated using probability and utility functions constructed so that preferences follow expected utility.
Rationality
Expected utility calculated this way differs from expected utility calculated using probability and utility assignments grounded in attitudes toward possible outcomes. Defining probability and utility using the representation theorems thus weakens the traditional principle of expected utility. It becomes merely a principle of coherence among preferences.
This employment of the representation theorems allows decision theory to advance the traditional principle of expected utility and thereby enrich its treatment of rational decisions. Decision theory may justify that traditional principle by deriving it from general principles of evaluation, as in Weirich A broad account of probabilities and utilities takes them to indicate attitudes toward propositions.
They are rational degrees of belief and rational degrees of desire, respectively. The account relies on arguments that degrees of belief and degrees of desire, if rational, conform to standard principles of probability and utility. Bolstering these arguments is work for causal decision theory. Besides clarifying its general interpretation of probability and utility, causal decision theory searches for the particular probabilities and utilities that yield the best version of its principle to maximize expected utility.
The causal probabilities in its formula for expected utility may be probabilities of subjunctive conditionals or various substitutes. Versions that use probabilities of subjunctive conditionals must settle on an analysis of those conditionals. Lewis Chap.
Joyce — advances probability images, as Lewis introduces them, as substitutes for probabilities of subjunctive conditionals. Does causal decision theory need an alternative, more causally-sensitive utility for an act-state pair? Weirich argues that it does. A person contemplating a wager that the capital of Missouri is Jefferson City entertains the consequences if he were to make the wager given that St. The next subsection elaborates this point.
Partition invariance ensures that various representations of the same decision problem yield solutions that agree.
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Dominance does not apply to this representation. If expected utilities are partition-sensitive, then acts that maximize expected utility may be partition-sensitive.
In that case an act is not a solution to a decision problem simply because it maximizes expected utility under some accurate representation of the problem. Too many acts have the same credential. This result agrees with the verdict of causal decision theory given other accurate representations of the problem. Its results depend on the partition of states. If a state is a set of worlds with equal utilities, then with respect to a partition of such states every act has the same expected utility.
Lewis overcomes this problem by using only partitions of dependency hypotheses. His basic formula is. Next, Sobel searches for other computations, using coarse-grained states, that are equivalent to the canonical computation. A suitable specification of utilities achieves partition invariance given his assumptions. According to a theorem he proves ,. He achieves partition invariance, assuming that. Weirich Secs. Then the formula. A more complex formula,. One issue concerning outcomes is their comprehensiveness. Gibbard and Harper [] — mention the possibility of narrowing outcomes to causal consequences, as practical applicability advocates.
The narrowing must be judicious, however, because the expected-utility principle requires that outcomes include every relevant consideration. The formula, from one perspective, omits states of the world because the outcomes themselves form a partition. The distinction between states and outcomes dissolves because worlds play the role of both states and outcomes. States are dispensable means of generating outcomes that are exclusive and exhaustive. Granting that the utilities of their realizations are additive, the utility of a world is a sum of the utilities of their realizations. In this formula for its expected utility, states play no explicit role:.
The formula considers for each basic desire and aversion the prospect of its realization if the act were performed. It eliminates states and obtains expected utility directly from outcomes taken as realizations of basic desires and aversions. The utility of health is 4, and the utility of wisdom is 8. In the formula for expected utility, a world covers only matters about which the agent cares.
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In the example, a world is a proposition specifying whether the agent has health and whether he has wisdom. In deliberations, a first-person action proposition represents an act. The proposition has a subject-predicate structure and refers directly to the agent, its subject, without the intermediary of a concept of the agent. A centered world represents the proposition.
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Such a world not only specifies individuals and their properties and relations, but also specifies which individual is the agent and where and when his decision problem arises. Realization of the act is realization of a world with, at its center, the agent at the time and place of his decision problem. Isaac Levi objects to any decision theory that attaches probabilities to acts. He holds that deliberation crowds out prediction. While deliberating, an agent does not have beliefs or degrees of belief about the act that she will perform. It need not assign a probability to an act.
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May an agent deliberating assign probabilities to her possible acts? Yes, a deliberator may sensibly assign probabilities to any events, including her acts. Causal decision theory may accommodate such probabilities by forgoing their measurement with betting quotients. According to that method of measurement, willingness to make bets indicates probabilities.
Joyce — considers whether Newcomb problems are genuine decision problems despite strong correlations between states and acts. He concludes that, yes, despite those correlations, an agent may view her decision as causing her act.
According to a principle of evidential autonomy ,. A deliberating agent who regards herself as free need not proportion her beliefs about her own acts to the antecedent evidence that she has for thinking that she will perform them. She should proportion her beliefs to her total evidence, including her self-supporting beliefs about her own acts. Those beliefs provide new relevant evidence about her acts. How should an agent deliberating about an act understand the background for her act? She should not adopt a backtracking supposition of her act.