Transforming Ordinal Riskless Utility into Cardinal Risky Utility:A Comment on Chung, Glimcher, and Tymula (2019)

Chung, Glimcher, and Tymula (2019) observed both consumers’ choices over commodity bundles and choices under risk. They assumed a cardinal riskless utility function V representing consumer choices and a cardinal risky utility function U. The two were inconsistent. This note shows that the two functions can be reconciled if we assume that V is ordinal. Then one utility function U can accommodate both risky and riskless choices</p

Subjective Expected Utility with Non-Increasing Risk Aversion

It is shown that assumptions about risk aversion, usually studied under the pre-supposition of expected utility maximization, have a surprising extra merit at an earlier stage of the measurement work: together with the sure-thing principle, these assumptions imply subjective expected utility maximization for monotonic continuous weak orders

A confirmed location in the Galactic halo for the high-velocity cloud 'chain A'

The high-velocity clouds of atomic hydrogen, discovered about 35 years ago, have velocities inconsistent with simple Galactic rotation models that generally fit the stars and gas in the Milky Way disk. Their origins and role in Galactic evolution remain poorly understood, largely for lack of information on their distances. The high-velocity clouds might result from gas blown from the Milky Way disk into the halo by supernovae, in which case they would enrich the Galaxy with heavy elements as they fall back onto the disk. Alternatively, they may consist of metal-poor gas -- remnants of the era of galaxy formation, accreted by the Galaxy and reducing its metal abundance. Or they might be truly extragalactic objects in the Local Group of galaxies. Here we report a firm distance bracket for a large high-velocity cloud, Chain A, which places it in the Milky Way halo (2.5 to 7 kiloparsecs above the Galactic plane), rather than at an extragalactic distance, and constrains its gas mass to between 10^5 and 2 times 10^6 solar masses.Comment: 8 pages, including 4 postscript figures. Letter to Nature, 8 July 199

Group decision rules and group rationality under risk

This paper investigates the rationality of group decisions versus individual decisions under risk. We study two group decision rules, majority and unanimity, in stochastic dominance and Allais paradox tasks. We distinguish communication effects (the effects of group discussions and interactions) from aggregation effects (mere impact of the voting procedure), which makes it possible to better understand the complex dynamics of group decision making. In an experiment, both effects occurred for intellective tasks whereas there were only aggregation effects in judgmental tasks. Communication effects always led to more rational choices; aggregation effects did so sometimes but not always. Groups violated stochastic dominance less often than individuals did, which was due to both aggregation and communication effects. In the Allais paradox tasks, there were almost no communication effects, and aggregation effects made groups deviate more from expected utility than individuals

Improving one’s choices by putting oneself in others’ shoes – an experimental analysis

This paper investigates how letting people predict others’ choices under risk affects subsequent own choices. We find an improvement of strong rationality (risk neutrality) for losses in own choices, but no such improvement for gains. There is no improvement of weak rationality (avoiding preference reversals). Overall, risk aversion in own choices increases. Conversely, for the effects of own choices on predicting for others, the risk aversion predicted in others’ choices is reduced if preceded by own choices, for both gains and losses. Remarkably, we find a new probability matching paradox at the group level. Relative to preceding studies on the effects of predicting others’ choices, we added real incentives, pure framing effects, and simplicity of stimuli. Our stimuli were maximally targeted towards our research questions

Revealed Likelihood and Knightian Uncertainty.

Abstract Nonadditive expected utility models were developed for explaining preferences in settings where probabilities cannot be assigned to events. In the absence of probabilities, difficulties arise in the interpretation of likelihoods of events. In this paper we introduce a notion of revealed likelihood that is defined entirely in terms of preferences and that does not require the existence of (subjective) probabilities. Our proposal is that decision weights rather than capacities are more suitable measures of revealed likelihood in rank-dependent expected utility models and prospect theory. Applications of our proposal to the updating of beliefs and to the description of attitudes towards ambiguity are presented

Under stochastic dominance Choquet-expected utility and anticipated utility are identical

The aim of this paper is to convince the reader that Choquet-expected utility, as initiated by Schmeidler (1982, 1989) for decision making under uncertainty, when formulated for decision making under risk naturally leads to anticipated utility, as initiated by Quiggin/Yaari. Thus the two generalizations of expected utility in fact are one

MF Calculator: A Web-Based Application for Analyzing Similarity

This paper presents the metric-frequency calculator (MF Calculator), an online application to analyze similarity. The MF Calculator implements a metric-frequency similarity algorithm for the quantitative assessment of similarity in ill-structured data sets. It is widely applicable as it can be used with nominal, ordinal, or interval data when there is little prior control over the variables to be observed regarding number or content. The MF Calculator generates a proximity matrix in CSV, XML or DOC format that can be used as input to traditional statistical techniques such as hierarchical clustering, additive trees, or multidimensional scaling. The MF Calculator also displays a graphical representation of outputs using additive similarity trees. A simulated example illustrates the implementation of the MF calculator. An additional example with real data is presented, in order to illustrate the potential of combining the MF Calculator with cluster analysis. The MF Calculator is a user-friendly tool available free of charge. It can be accessed from http : //mfcalculator.celiasales.org/Calculator.aspx, and it can be used by non-experts from a wide range of social sciences.info:eu-repo/semantics/publishedVersio