B. Hill’s ‘Confidence’ Approach to Decision Making Under Uncertainty Completely Overlooks the Contributions Made in J M Keynes’s Parts II -V of His a Treatise on Probability, 1921 and General Theory, 1936: Keynes’s Interval Valued Approach to Imprecise Probability and Decision Weight Approach Appeared Some 60–80 Years Before Hill Began His Research Program

The introduction of decision making under uncertainty by Ferejohn and Fiorina is an interesting addition to the literature on rational theories of citizen participation. Decision making under uncertainty assumes, however, that the actor has no knowledge of the probabilities of the various outcomes; this is obviously no more true than the assumption of perfect information about these probabilities made in the decision making under risk model. Voters have some, but not perfect, information about the probabilities of at least some of the different possible outcomes.Specifically, let us look at the two-party case. In Ferejohn's notation, (p3 + p4) is the probability of an individual's vote making a difference. We might expect a rational citizen to know that this probability is at most minuscule, even if he cannot calculate its exact value.

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Optimization

Machine Learning for Signal Processing ◽

10.1093/oso/9780198714934.003.0002 ◽

2019 ◽

pp. 41-70

Author(s):

Max A. Little

Keyword(s):

Machine Learning ◽

Decision Making ◽

Decision Making Under Uncertainty ◽

Central Topic ◽

Sensor Device ◽

Made In

Decision-making under uncertainty is a central topic of this book. A common scenario is the following: data is recorded from some (digital) sensor device, and we know (or assume) that there is some “underlying” signal contained in this data, which is obscured by noise. The goal is to extract this signal, but the noise causes this task to be impossible: we can never know the actual underlying signal. We must make mathematical assumptions that make this taskp possible at all. Uncertainty is formalized through the mathematical machinery of probability, and decisions are made that find the optimal choices under these assumptions. This chapter explores the main methods by which these optimal choices are made in DSP and machine learning.

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CARDIM DE CARVALHO’S CONTRIBUTION TO THE UNDERSTANDING OF DECISION MAKING IN CONTEMPORANEOUS FINANCIAL MARKETS

Revista de Economia Contemporânea ◽

10.1590/198055272426 ◽

2020 ◽

Vol 24 (2) ◽

Author(s):

Maryse Farhi ◽

Daniela Magalhães Prates

Keyword(s):

Decision Making ◽

General Theory ◽

Financial Markets ◽

Portfolio Allocation ◽

Decision Making Under Uncertainty ◽

Decision Making Process ◽

Financial Assets ◽

Monetary Economy ◽

Keynes General Theory

ABSTRACT According to the post-Keynesian approach, uncertainty is inherent to the loci in which investors decide the portfolio allocation of their wealth (or the wealth they manage) in a monetary economy. In financial assets’ markets, to deal with uncertainty, agents use instruments that evolved with time and encompass two elements of the decision-making process: (i) which premises will be considered to make a decision, the focus of Keynes’ General Theory; (ii) the sequel between the premises and the very decision, the focus of Keynes’ Treatise on Probability. The aim of this paper is twofold. The first one is to summarize the contribution of Fernando Cardim de Carvalho to the understanding of the decision-making under uncertainty. The second one is to analyse the decision-making instruments used in contempo-raneous financial assets’ markets in light of his contribution that is especially suitable for this goal due to his acquaintance with the Treatise.

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