Natural, Artificial, and Social Intelligence for Decision-Making

Louis Anthony Cox
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Louis Anthony Cox: Cox Associates and University of Colorado

Chapter Chapter 3 in AI-ML for Decision and Risk Analysis, 2023, pp 65-101 from Springer

Abstract: Abstract Risk analysis is largely about how to think more clearly and usefully about what actions to take when perceptions and understanding of the current situation are incomplete and consequences of different choices are uncertain. Suggestions for improving decision-making under uncertainty come from many sources, including the mathematical prescriptions of expected utility theory and decision analysis; decision and risk psychologists cautioning about cognitive heuristics and biases that distort risk perceptions and undermine the logical coherence of preferences and plans (see Chap. 1); business and analytics books emphasizing collecting and using data to inform decisions (see Chap. 2); and legal scholars discussing frameworks for making defensible decisions in the face of existing laws and regulations. This chapter examines perspectives on rational thinking and decision-making from cognitive neuroscience, psychology of thinking and reasoning, artificial intelligence and machine learning (AI/ML), social science, and social statistics and data analysis.

Keywords: Decision science; Decision psychology; Cognitive neuroscience; Artificial intelligence; Machine learning; Deep learning (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-031-32013-2_3

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DOI: 10.1007/978-3-031-32013-2_3

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