Decision settings analysis – a tool for analysis and design of human activity systems

The paper describes a methodology to be used for analysis and design of human activity systems. The methodology is based on an analysis of the decision settings whereas most other decision analysis methodologies are analysing the process. The decision concept is analysed and discussed. A distinction between programmed and programmable as well as non-programmed and non-programmable decisions is proposed. A classification of different information types for decision making is presented. A methodology based on a systemic and systematic analysis of the information requirements of an organization is proposed. This methodology also indicates organizational discrepancies and information imbalances. The methodology focuses the settings of the decisions on all levels of organizations. The methodology can be regarded as a dynamic, learning system. The author proposes further research on the individuals decision making abilities.     

Differential equation models for statistical functions

Differential equations have been used in statistics to define functions such as probability densities. But the idea of using differential equation formulations of stochastic models has a much wider scope. The author gives several examples, including simultaneous estimation of a regression model and residual density, monotone smoothing, specification of a link function, differential equation models of data, and smoothing over complicated multidimensional domains. This paper aims to stimulate interest in this approach to functional estimation problems, rather than provide carefully worked out methods.     

A semi-parametric method for estimating the beta coefficients of the hidden two-sided asset return jumps

ABSTRACT

We introduce a new methodology for estimating the parameters of a two-sided jump model, which aims at decomposing the daily stock return evolution into (unobservable) positive and negative jumps as well as Brownian noise. The parameters of interest are the jump beta coefficients which measure the influence of the market jumps on the stock returns, and are latent components. For this purpose, at first we use the Variance Gamma (VG) distribution which is frequently used in modeling financial time series and leads to the revelation of the hidden market jumps' distributions. Then, our method is based on the central moments of the stock returns for estimating the parameters of the model. It is proved that the proposed method provides always a solution in terms of the jump beta coefficients. We thus achieve a semi-parametric fit to the empirical data. The methodology itself serves as a criterion to test the fit of any sets of parameters to the empirical returns. The analysis is applied to NASDAQ and Google returns during the 2006–2008 period.     

Extending Bayesian back-calculation to estimate age and time specific HIV incidence

Lifetime Data Analysis - CD4-based multi-state back-calculation methods are key for monitoring the HIV epidemic, providing estimates of HIV incidence and diagnosis rates by disentangling their...     

Associations Between Romantic Relationship Factors and Body Mass Index Among Weight Loss Surgery Patients

The current study utilized a sample of 183 patients, in romantic relationships, who were either pre‐ or post‐weight loss surgery (WLS), to assess (a) associations between romantic relationship factors and pre‐ and post‐surgery body mass index (BMI), (b) the positive and negative influences of obesity in romantic relationships, and (c) the influence of romantic relationship factors on BMI and the reciprocal. Correlations, confirmatory factor analysis, and multiple linear regression were conducted. Patients endorsed greater negative influence of obesity in their romantic relationships compared to positive influences, and their romantic relationship quality was predicted by several variables, including BMI, in pre‐ and post‐surgery patient groups.     

Over‐indebtedness and microcredit in Cambodia: Moving beyond borrower‐centric frames

Over the past two decades, Cambodia has experienced an unprecedented credit boom, a growth in lending so rapid that the International Monetary Fund (IMF) referred to it as “one of the fastest financial deepening episodes by historical cross‐cultural standards” (IMF, 2016, p. 4). This deepening has been driven by the expansion of microcredit. In tandem, over‐indebtedness has increased among microcredit borrowers, and debt has become a significant political and economic concern. This article explores how over‐indebtedness is understood and explained by stakeholders across microcredit value chains. To do so, we draw on interviews with microfinance institution (MFI) executives, investors, branch managers, partners, financial literacy trainers, loan officers and borrowers in Siem Reap and Phnom Penh. We find that across the sector, dominant framings of over‐indebtedness privilege borrower‐centric explanations, while discounting the structural drivers of excessive lending and borrowing. As a consequence, current efforts to limit over‐indebtedness are unlikely to produce the kinds of solutions that are most needed to reduce the debt stress among borrowers. These arguments have implications across the Global South, particularly for contexts where microfinance is rapidly expanding.     

New Price,New Hope? An Examination of the Effects of Doubling the Ticket Price in UK Lotto

The UK Lotto game was introduced in November 1994 with a standard 6/49 format and an entry fee of £1 per ticket. After several years, revenue began to fall despite extensive publicity and a variety of inducements. By 2013, nominal weekly revenue was less than half the 1995 level. In October 2013, the operator doubled the price of a ticket to £2 and made a number of changes to the pay-out structure of smaller prizes. The intent of the changes was to reverse the long downward trend in game revenue by encouraging higher jackpots and offering more pay-out opportunities for each ticket. We use draw by draw revenue and other data to evaluate how players responded to these changes and find that, while ticket sales fell dramatically, total revenue rose following the changes. Primarily this appears to have been the consequence of increased frequency of rollovers (and therefore of more frequent high jackpot draws) rather than ticket price inelasticity. However, although there was a short-term gain in revenue, the changes did not arrest, and indeed seem to have accentuated, the long-run trend decrease in the revenue generated by the game.     

Identifying Mental Disorder from the Faces of Women with Borderline Personality Disorder

Although appearance-based cues can help to diagnose physical illness, visual manifestations of mental disorder may be more elusive. Here, we investigated whether individuals could distinguish women with a serious mental disorder (borderline personality disorder) from demographically- and IQ-matched non-psychiatric controls. Participants rated mentally ill targets as more likely to have a mental disorder from photos more accurately than chance, despite not believing that such judgments were possible. The configuration of facial cues played an important role in these judgments, as interfering with the spatial relationships between facial features reduced participants’ accuracy to chance guessing. Further investigation showed similar results when participants rated the targets for specific mental disorders (borderline personality disorder, major depressive disorder) and rated the mentally ill targets as more depressed, angry, anxious, disgusted, emotionally unstable, distressed, and less happy. Moreover, the depression ratings significantly correlated with the targets’ actual depressive symptoms. Thus, individuals may be able to infer aspects of mental disorder from minimal facial cues.