scholarly journals Revealed Preferences over Risk and Uncertainty

2020 ◽  
Vol 110 (6) ◽  
pp. 1782-1820 ◽  
Author(s):  
Matthew Polisson ◽  
John K.-H. Quah ◽  
Ludovic Renou
Keyword(s):  
Portfolio Choice ◽  
Efficiency Index ◽  
Revealed Preferences ◽  
Choice Data ◽  
Risk And Uncertainty ◽  
Infinite Domains ◽  
Disappointment Aversion ◽  
Rank Dependent Utility ◽  
Using Data ◽  
Budgetary Choice

We develop a nonparametric method, called Generalized Restriction of Infinite Domains (GRID), for testing the consistency of budgetary choice data with models of choice under risk and under uncertainty. Our test can allow for risk-loving and elation-seeking attitudes, or it can require risk aversion. It can also be used to calculate, via Afriat’s efficiency index, the magnitude of violations from a particular model. We evaluate the performance of various models under risk (expected utility, disappointment aversion, rank-dependent utility, and stochastically monotone utility) using data collected from several recent portfolio choice experiments. (JEL C14, D11, D12, D81)

scholarly journals Discrete Arrow–Pratt indexes for risk and uncertainty

2020 ◽  
Author(s):  
Aurélien Baillon ◽  
Olivier L’Haridon
Keyword(s):  
Gold Standard ◽  
Decision Models ◽  
Risk Attitudes ◽  
Choice Data ◽  
Risk And Uncertainty ◽  
Multiple Priors ◽  
Probability Transformation ◽  
Non Parametric

Abstract The Arrow–Pratt index, a gold standard in studies of risk attitudes, is not directly observable from choice data. Existing methods to measure it rely on parametric assumptions. We introduce a discrete Arrow–Pratt index, and its relative counterpart, that can be directly obtained from choices. Our approach is general: it is (i) non-parametric, (ii) applicable to both risk and uncertainty, (iii) and robust to probability transformation, non-additive beliefs and multiple priors. Our index can also be used to characterize various decision models through various simple consistency requirements. We analyze its properties and demonstrate how it can be measured.

Abstract interpretation by dynamic partitioning

1992 ◽  
Vol 2 (4) ◽  
pp. 407-435 ◽  
Author(s):  
François Bourdoncle
Keyword(s):  
Data Structures ◽  
Abstract Interpretation ◽  
A Priori ◽  
Minimal Function ◽  
Infinite Domains ◽  
Dynamic Partitioning ◽  
Abstract Domains ◽  
Using Data ◽  
New Framework

AbstractThe essential part of abstract interpretation is to build a machine-representable abstract domain expressing interesting properties about the possible states reached by a program at runtime. Many techniques have been developed which assume that one knows in advance the class of properties that are of interest. There are cases however when there are no a priori indications about the 'best' abstract properties to use. We introduce a new framework that enables non-unique representations of abstract program properties to be used, and expose a method, called dynamic partitioning, that allows the dynamic determination of interesting abstract domains using data structures built over simpler domains. Finally, we show how dynamic partitioning can be used to compute non-trivial approximations of functions over infinite domains and give an application to the computation of minimal function graphs.

scholarly journals A Revealed Preference Ranking of U.S. Colleges and Universities *

2012 ◽  
Vol 128 (1) ◽  
pp. 425-467 ◽  
Author(s):  
Christopher N. Avery ◽  
Mark E. Glickman ◽  
Caroline M. Hoxby ◽  
Andrew Metrick
Keyword(s):  
Revealed Preference ◽  
National Sample ◽  
Preference Ranking ◽  
Revealed Preferences ◽  
Admissions Policies ◽  
High Achieving Students ◽  
Strategic Manipulation ◽  
Early Decision ◽  
Using Data ◽  
Undergraduate Programs

Abstract We present a method of ranking U.S. undergraduate programs based on students’ revealed preferences. When a student chooses a college among those that have admitted him, that college “wins” his “tournament.” Our method efficiently integrates the information from thousands of such tournaments. We implement the method using data from a national sample of high-achieving students. We demonstrate that this ranking method has strong theoretical properties, eliminating incentives for colleges to adopt strategic, inefficient admissions policies to improve their rankings. We also show empirically that our ranking is (1) not vulnerable to strategic manipulation; (2) similar regardless of whether we control for variables, such as net cost, that vary among a college’s admits; (3) similar regardless of whether we account for students selecting where to apply, including Early Decision. We exemplify multiple rankings for different types of students who have preferences that vary systematically.

Long-term evaluation of gauge-adjusted precipitation estimates from a radar in Norway

2010 ◽  
Vol 41 (3-4) ◽  
pp. 171-192 ◽  
Author(s):  
Yisak Abdella ◽  
Knut Alfredsen
Keyword(s):  
Efficiency Index ◽  
Absolute Difference ◽  
Mean Absolute Difference ◽  
Conditional Mean ◽  
Weather Radars ◽  
Precipitation Estimates ◽  
Adjustment Factors ◽  
Term Evaluation ◽  
Using Data

The implementation of weather radars in Norway by the Norwegian Meteorological Institute (met.no) has made radar a potential tool to improve hydrologic predictions through the use of distributed precipitation input. Met.no supplies gauge-adjusted quantitative hourly radar precipitation estimates. A key concern regarding the use of radar precipitation estimates in hydrology is their accuracy. In this study, the precipitation estimates from the Rissa radar in Norway were evaluated through a comparison with observations from 112 gauges used in the adjustment (dependent) and 15 gauges not included in the adjustment (independent). The comparison with daily measurements from the dependent gauges showed a decline in the radar's detection probability beyond a range of about 140 km, with a more severe decline in winter. The deviations between radar- and gauge-conditional mean precipitation were significantly higher in summer than in winter. There was an overestimation at most of the gauge locations during summer, while there were more underestimations during winter. A dependence of accuracy on range was identified from the spatial distribution of the Efficiency Index and mean absolute difference. The evaluation against the independent gauges revealed trends mostly similar to the ones obtained from comparison with the dependent gauges. The radar estimates exhibited better agreement with gauge measurements during winter. The main reasons for the errors remaining in the gauge-adjusted precipitation estimates are the absence of correction for the vertical profile of reflectivity, the use of average monthly adjustment factors, derivation of these factors using data from previous years and the use of a single reflectivity–precipitation rate (Z–R) relation.

scholarly journals Revealed preferences over risk and uncertainty

2015 ◽  
Author(s):  
John Quah ◽  
Ludovic Renou ◽  
Matthew Polisson
Keyword(s):  
Revealed Preferences ◽  
Risk And Uncertainty

Relaxed Optimization: How Close Is a Consumer to Satisfying First-Order Conditions?

2021 ◽  
pp. 1-45
Author(s):  
Geoffroy de Clippel ◽  
Kareen Rozen
Keyword(s):  
Portfolio Choice ◽  
Consumer Choice ◽  
Goodness Of Fit ◽  
Order Conditions ◽  
Choice Data ◽  
Reference Class ◽  
Progressive Taxation ◽  
First Order ◽  
Budget Sets

Abstract We propose relaxing the first-order conditions in optimization to approximate rational consumer choice. We assess the magnitude of departures with a new, axiomatically-founded measure that admits multiple interpretations. Standard inequality tests of rationality for any given reference class of preferences can be conveniently re-purposed to measure goodness-of-fit with that class. Another advantage of our approach is that it is applicable in any context where the first-order approach is meaningful (e.g., convex budget sets arising from progressive taxation). We apply these ideas to shed new light on existing portfolio-choice data.

Alternative Preferences

2017 ◽  
Author(s):  
Kerry E. Back
Keyword(s):  
Expected Utility ◽  
Ambiguity Aversion ◽  
Marginal Utility ◽  
Knightian Uncertainty ◽  
Stochastic Discount Factor ◽  
Ellsberg Paradox ◽  
First Order ◽  
Weighted Utility ◽  
Disappointment Aversion ◽  
Rank Dependent Utility

The Allais and Ellsberg paradoxes are presented. Various generalizations of expected utility motivated by these and other paradoxes are discussed, including betweenness preferences, rank‐dependent preferences, multiple prior max‐min preferences, and prospect theory. For betweenness preferences, which include weighted utility and disappointment aversion, an investor’s marginal utility is proportional to a stochastic discount factor. Disappointment averse utility and rank‐dependent utility have first‐order risk aversion. Multiple prior max‐min utility is one way to accomodate the Ellsberg paradox (ambiguity aversion or Knightian uncertainty). The dynamic consistency of updating multiple priors is discussed.

scholarly journals Efficiency of cancer treatments: in silico experiments

2020 ◽  
Vol 15 ◽  
pp. 19
Author(s):  
Elena Piretto ◽  
Marcello Delitala ◽  
Mario Ferraro
Keyword(s):  
In Silico ◽  
Therapeutic Strategy ◽  
Standard Dose ◽  
General Rule ◽  
Efficiency Index ◽  
Cancer Treatments ◽  
Medical Treatments ◽  
Sensitive Clone ◽  
Using Data ◽  
Clonal Composition

Despite the advances in the formulation of different therapies to fight cancer, the design of successful protocols is still a challenging problem. In order to provide some indications on the effectiveness of medical treatments, results from in silico experiments are presented based on a mathematical model comprising two cancer populations competing for resources and with different susceptibilities to the action of therapies. The focus is on the outcome of protocols in which the total dose can be administered with different time distributions. An efficiency index is proposed to quantify the effectiveness of different protocols. Simulations show that a standard dose chemotherapy is effective when the sensitive clone has a marked competitive advantage, whereas its outcome is much worse when a resistant clone emerges; obviously combinations of immune and chemotherapy work better. These results, in accord with previous finding reported in the literature, stress the importance to take into account competitive interactions among cancer clones to decide which therapeutic strategy should be adopted. However, it is not just the efficiency that changes in these different configurations of clonal composition and therapy timing. A general rule seems to emerge: when evolutionary pressures are strong, the best protocols entail and early starting of the treatment, whereas, on the contrary, when interactions among clones are weak, therapy should start later. Finally the model has been adapted to investigate the relative efficiency of different protocols, by using data reported in literature regarding experiments with breast cancer cells.

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