A Unified Theory of Human Judgements and Decision-Making under Uncertainty

Pisano, Raffaele; Sozzo, Sandro

doi:10.3390/e22070738

Cited by 10 publications

(5 citation statements)

References 80 publications

(163 reference statements)

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“…More recently, several papers work to apply various forms of entropy to decision making by individuals and organizations [41,42], but one is particularly interesting to the present research. In A Unified Theory of Human Judgement and Decision-Making under Uncertainty, Raffaele Pisano and Sandro Sozzo draw the conclusion that quantum theory (i.e., statistical mechanics) is representative of human cognition and that quantum state probability is subjective, which supports this research approach [43]. However, the Authors avoid directly applying entropy and make the assumption that the Born rule (or law) of quantum mechanics defines the relationship between subjective probability as the square root of the objective probability.…”

Section: Entropymentioning

confidence: 57%

Derivation and Application of the Subjective–Objective Probability Relationship from Entropy: The Entropy Decision Risk Model (EDRM)

Monroe

Beruvides

Tercero-Gómez

2020

Systems

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The uncertainty, or entropy, of an atom of an ideal gas being in a certain energy state mirrors the way people perceive uncertainty in the making of decisions, uncertainty that is related to unmeasurable subjective probability. It is well established that subjects evaluate risk decisions involving uncertain choices using subjective probability rather than objective, which is usually calculated using empirically derived decision weights, such as those described in Prospect Theory; however, an exact objective–subjective probability relationship can be derived from statistical mechanics and information theory using Kullback–Leibler entropy divergence. The resulting Entropy Decision Risk Model (EDRM) is based upon proximity or nearness to a state and is predictive rather than descriptive. A priori EDRM, without factors or corrections, accurately aligns with the results of prior decision making under uncertainty (DMUU) studies, including Prospect Theory and others. This research is a first step towards the broader effort of quantifying financial, programmatic, and safety risk decisions in fungible terms, which applies proximity (i.e., subjective probability) with power utility to evaluate choice preference of gains, losses, and mixtures of the two in terms of a new parameter referred to as Prospect. To facilitate evaluation of the EDRM against prior studies reported in terms of the percentage of subjects selecting a choice, the Percentage Evaluation Model (PEM) is introduced to convert choice value results into subject response percentages, thereby permitting direct comparison of a utility model for the first time.

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Section: Entropymentioning

confidence: 57%

Derivation and Application of the Subjective–Objective Probability Relationship from Entropy: The Entropy Decision Risk Model (EDRM)

Monroe

Beruvides

Tercero-Gómez

2020

Systems

View full text Add to dashboard Cite

show abstract

“…EDRM uses proximity as the probability and power utility for the magnitude. Statistical mechanics and information theory entropy (they are synonymous) are representative of human cognition, which was further demonstrated in EDRM derivation and validation [1,[16][17][18]. Starting with basic state entropy from information theory or statistical mechanics in terms of subjective probability (proximity), H = −τ log 2 τ, the Kullback-Leibler entropy divergence from certainty is calculated [19,20]:…”

Section: Entropy Decision Risk Modelmentioning

confidence: 99%

Quantifying Risk Perception: The Entropy Decision Risk Model Utility (EDRM-U)

Monroe

Beruvides

Tercero-Gómez

2020

Systems

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Risk perception can be quantified in measurable terms of risk aversion and sensitivity. While conducting research on the quantization of programmatic risk, a bridge between positive and normative decision theories was discovered through the application of a novel a priori relationship between objective and subjective probabilities and the application of Bernoulli’s expected utility theory. The Entropy Decision Risk Model (EDRM) derived using the Kullback–Liebler entropy divergence from certainty serves as a translation between objective and subjective probability, referred to as proximity, and has proven its applicability to various positive decision theories related to Prospect Theory. However, EDRM initially assumes the validity of the standard exponential power utility function ubiquitous to positive decision theory models as the magnitude of a choice to isolate and validate proximity. This research modifies the prior model by applying Daniel Bernoulli’s expected utility as the measure of choice magnitude in place of power utility. The revised model, EDRM Utility (EDRM-U), predicts the subject choices for both small and large ranges of values and shows that Prospect Theory’s neutral reference point is actually centered about an assumed initial wealth value, called neutral wealth, that correlates to a power utility exponent value. This hypothesis is confirmed by demonstrating that EDRM-U presents an equivalent or better correlation with prior research in eleven landmark studies of college students spanning more than 26 years and comprising over 300 problems, including those with widely varying values. This research contributes to the fields of risk management and decision engineering by proposing a decision model that behaves according to both positive and normative decision theories and provides measures of risk perception.

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“…unit vectors, self-adjoint operators, unitary dynamics, probabilistic Born rule and tensor products (see, e.g. [19][20][21][22]). On the other side, the analogies in physical and cognitive realms are so deep that we have also developed a new interpretation of quantum theory in which quantum entities themselves do not behave as physical objects but, rather, as concepts [23,24].…”

Section: Introductionmentioning

confidence: 99%

Development of a thermodynamics of human cognition and human culture

Aerts¹,

Arguëlles²,

Beltran³

et al. 2023

Phil. Trans. R. Soc. A.

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Inspired by foundational studies in classical and quantum physics, and by information retrieval studies in quantum information theory, we prove that the notions of ‘energy’ and ‘entropy’ can be consistently introduced in human language and, more generally, in human culture. More explicitly, if energy is attributed to words according to their frequency of appearance in a text, then the ensuing energy levels are distributed non-classically, namely, they obey Bose–Einstein, rather than Maxwell–Boltzmann, statistics, as a consequence of the genuinely ‘quantum indistinguishability’ of the words that appear in the text. Secondly, the ‘quantum entanglement’ due to the way meaning is carried by a text reduces the (von Neumann) entropy of the words that appear in the text, a behaviour which cannot be explained within classical (thermodynamic or information) entropy. We claim here that this ‘quantum-type behaviour is valid in general in human language’, namely, any text is conceptually more concrete than the words composing it, which entails that the entropy of the overall text decreases. In addition, we provide examples taken from cognition, where quantization of energy appears in categorical perception, and from culture, where entities collaborate, thus ‘entangle’, to decrease overall entropy. We use these findings to propose the development of a new ‘non-classical thermodynamic theory’ for human cognition, which also covers broad parts of human culture and its artefacts and bridges concepts with quantum physics entities. This article is part of the theme issue ‘Thermodynamics 2.0: Bridging the natural and social sciences (Part 2)’.

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A Unified Theory of Human Judgements and Decision-Making under Uncertainty

Cited by 10 publications

References 80 publications

Derivation and Application of the Subjective–Objective Probability Relationship from Entropy: The Entropy Decision Risk Model (EDRM)

Derivation and Application of the Subjective–Objective Probability Relationship from Entropy: The Entropy Decision Risk Model (EDRM)

Quantifying Risk Perception: The Entropy Decision Risk Model Utility (EDRM-U)

Development of a thermodynamics of human cognition and human culture

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