Rational Choice Using Imprecise Probabilities And Utilities
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Rational Choice Using Imprecise Probabilities and Utilities
Author: Paul Weirich
language: en
Publisher: Cambridge University Press
Release Date: 2021-02-28
An agent often does not have precise probabilities or utilities to guide resolution of a decision problem. I advance a principle of rationality for making decisions in such cases. To begin, I represent the doxastic and conative state of an agent with a set of pairs of a probability assignment and a utility assignment. Then I support a decision principle that allows any act that maximizes expected utility according to some pair of assignments in the set. Assuming that computation of an option's expected utility uses comprehensive possible outcomes that include the option's risk, no consideration supports a stricter requirement.
Rational Responses to Risks
Author: Paul Weirich
language: en
Publisher: Oxford University Press
Release Date: 2020-07-10
Good decisions account for risks. For example, the risk of an accident while driving in the rain makes a reasonable driver decide to slow down. While risk is a large topic in theoretical disciplines such as economics and psychology, as well as in practical disciplines such as medicine and finance, philosophy has a unique contribution to make in developing a normative theory of risk that states what risk is, and to what extent our responses to it are rational. Weirich here develops a philosophical theory of the rationality of responses to risk. He first distinguishes two types of risk: first, a chance of a bad event, and second, an act's risk in relation to its possible outcomes. He argues that this distinction has normative significance in the sense that one's attitudes towards these types of risks - and how one acts on them - are governed by different general principles of rationality. Consequently, a comprehensive account of risk must not only characterize rational responses to risk but also explain why these responses are rational. Weirich explains how, for a rational ideal agent, the expected utilities of the acts available in a decision problem explain the agent's preferences among the acts. As a result, maximizing expected utility is just following preferences among the acts. His view takes an act's expected utility, not just as a feature of a representation of preferences among acts, but also as a factor in the explanation of preferences among acts. The book's precise formulation of general standards of rationality for attitudes and for acts, and its rigorous argumentation for these standards, make it philosophical; but while mainly of interest to philosophers, its broader arguments will contribute to the conceptual foundations of studies of risk in all disciplines that study it.
Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications
This three volume set (CCIS 853-855) constitutes the proceedings of the 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2017, held in Cádiz, Spain, in June 2018. The 193 revised full papers were carefully reviewed and selected from 383 submissions. The papers are organized in topical sections on advances on explainable artificial intelligence; aggregation operators, fuzzy metrics and applications; belief function theory and its applications; current techniques to model, process and describe time series; discrete models and computational intelligence; formal concept analysis and uncertainty; fuzzy implication functions; fuzzy logic and artificial intelligence problems; fuzzy mathematical analysis and applications; fuzzy methods in data mining and knowledge discovery; fuzzy transforms: theory and applications to data analysis and image processing; imprecise probabilities: foundations and applications; mathematical fuzzy logic, mathematical morphology; measures of comparison and entropies for fuzzy sets and their extensions; new trends in data aggregation; pre-aggregation functions and generalized forms of monotonicity; rough and fuzzy similarity modelling tools; soft computing for decision making in uncertainty; soft computing in information retrieval and sentiment analysis; tri-partitions and uncertainty; decision making modeling and applications; logical methods in mining knowledge from big data; metaheuristics and machine learning; optimization models for modern analytics; uncertainty in medicine; uncertainty in Video/Image Processing (UVIP).