Egalitarian justice and expected value

According to all-luck egalitarianism, the differential distributive effects of both brute luck, which defines the outcome of risks which are not deliberately taken, and option luck, which defines the outcome of deliberate gambles, are unjust. Exactly how to correct the effects of option luck is, however, a complex issue. This article argues that (a) option luck should be neutralized not just by correcting luck among gamblers, but among the community as a whole, because it would be unfair for gamblers as a group to be disadvantaged relative to non-gamblers by bad option luck; (b) individuals should receive the warranted expected results of their gambles, except insofar as individuals blamelessly lacked the ability to ascertain which expectations were warranted; and (c) where societal resources are insufficient to deliver expected results to gamblers, gamblers should receive a lesser distributive share which is in proportion to the expected results. Where all-luck egalitarianism is understood in this way, it allows risk-takers to impose externalities on non-risk-takers, which seems counterintuitive. This may, however, be an advantage as it provides a luck egalitarian rationale for assisting ‘negligent victims’

The Expected Value Premium

Fama and French (2002) estimate the equity premium using dividend growth rates to measure the expected rate of capital gain. We use similar methods to study the value premium. From 1941 to 2002, the expected HML return is on average 5.1% per annum, consisting of an expected-dividend-growth component of 3.5% and an expected-dividend-to-price component of 1.6%. The ex-ante HML return is also countercyclical: a positive, one-standard-deviation shock to real consumption growth rate lowers this premium by about 0.45%. Unlike the equity premium, there is only mixed evidence suggesting that the value premium has declined over time.

AN EVALUATION OF EXPECTED VALUE AND EXPECTED VALUE-VARIANCE CRITERIA IN ACHIEVING RISK EFFICIENCY IN CROP SELECTION

This article evaluates the performance of expected value and expected value-variance criteria in achieving risk efficiency in crop selection. Results indicate that the expected returns criterion achieves risk efficiency in many situations because of constraints. However, in the absence of many constraints the expected returns criterion performs poorly except when highly mean-dominant activities are present. The expected value-variance criterion achieves a high degree of risk efficiency for all situations examined. This result implies that criteria more complex than expected value-variance are not necessary for crop selection analysis, given empirical returns distributions.Crop Production/Industries,

Multiphase sampling using expected value of information

This paper explores multiphase or infill sampling to reduce uncertainty after an initial sample has been taken and analysed to produce a map of the probability of some hazard. New observations are iteratively added by maximising the global expected value of information of the points. This is equivalent to minimisation of global misclassification costs. The method accounts for measurement error and different costs of type I and type II errors. Constraints imposed by a mobile sensor web can be accommodated using cost distances rather than Euclidean distances to decide which sensor moves to the next sample location. Calculations become demanding when multiple sensors move simultaneously. In that case, a genetic algorithm can be used to find sets of suitable new measurement locations. The method was implemented using R software for statistical computing and contributed libraries and it is demonstrated using a synthetic data set

An Expected Value Approach to the Dual-Use Problem

In this chapter I examine how expected-value theory might inform responses to what I call the dual-use problem. I begin by defining that problem. I then outline a procedure, which invokes expected-value theory, for tackling it. I first illustrate the procedure with the aid of a simplified schematic example of a dual-use problem, and then describe how it might also guide responses to more complex real-world cases. I outline some attractive features of the procedure. Finally, I consider whether and how the procedure might be amended to accommodate various criticisms of it

Bounds for the expected value of one-step processes

Mean-field models are often used to approximate Markov processes with large state-spaces. One-step processes, also known as birth-death processes, are an important class of such processes and are processes with state space $\{0,1,\ldots,N\}$ and where each transition is of size one. We derive explicit bounds on the expected value of such a process, bracketing it between the mean-field model and another simple ODE. Our bounds require that the Markov transition rates are density dependent polynomials that satisfy a sign condition. We illustrate the tightness of our bounds on the SIS epidemic process and the voter model.Comment: 14 pages, 4 figures, revise

Expected-value bias in routine third-trimester growth scans.

OBJECTIVES: Operators performing fetal growth scans are usually aware of the gestational age of the pregnancy, which may lead to expected-value bias when performing biometric measurements. We aimed to evaluate the incidence of expected-value bias in routine fetal growth scans and assess its impact on standard biometric measurements. METHODS: We collected prospectively full-length video recordings of routine ultrasound growth scans coupled with operator eye tracking. Expected value was defined as the gestational age at the time of the scan, based on the estimated due date that was established at the dating scan. Expected-value bias was defined as occurring when the operator looked at the measurement box on the screen during the process of caliper adjustment before saving a measurement. We studied the three standard biometric planes on which measurements of head circumference (HC), abdominal circumference (AC) and femur length (FL) are obtained. We evaluated the incidence of expected-value bias and quantified the impact of biased measurements. RESULTS: We analyzed 272 third-trimester growth scans, performed by 16 operators, during which a total of 1409 measurements (354 HC, 703 AC and 352 FL; including repeat measurements) were obtained. Expected-value bias occurred in 91.4% of the saved standard biometric plane measurements (85.0% for HC, 92.9% for AC and 94.9% for FL). The operators were more likely to adjust the measurements towards the expected value than away from it (47.7% vs 19.7% of measurements; P < 0.001). On average, measurements were corrected by 2.3 ± 5.6, 2.4 ± 10.4 and 3.2 ± 10.4 days of gestation towards the expected gestational age for the HC, AC, and FL measurements, respectively. Additionally, we noted a statistically significant reduction in measurement variance once the operator was biased (P = 0.026). Comparing the lowest and highest possible estimated fetal weight (using the smallest and largest biased HC, AC and FL measurements), we noted that the discordance, in percentage terms, was 10.1% ± 6.5%, and that in 17% (95% CI, 12-21%) of the scans, the fetus could be considered as small-for-gestational age or appropriate-for-gestational age if using the smallest or largest possible measurements, respectively. Similarly, in 13% (95% CI, 9-16%) of scans, the fetus could be considered as large-for-gestational age or appropriate-for-gestational age if using the largest or smallest possible measurements, respectively. CONCLUSIONS: During routine third-trimester growth scans, expected-value bias frequently occurs and significantly changes standard biometric measurements obtained. © 2019 the Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of the International Society of Ultrasound in Obstetrics and Gynecology