Decision makers often take risky decisions on behalf of others rather than for themselves. Competing theoretical models predict both; higher as well as lower levels of risk aversion when taking risk for others, and the experimental evidence is mixed. In our comprehensive within-subject design, student subjects in the role of money managers have substantial social responsibility by taking invest...

This paper presents a method for condition monitoring of systems that operate in a repetitive manner. A data driven method is proposed that considers changes in the distribution of data samples obtained from multiple executions of one or several tasks. This is made possible with the use of kernel density estimators and the Kullback-Leibler distance measure between distributions. To increase rob...

Different users of geospatial information have different requirements of that information. Matching information to users’ requirements demands an understanding of the ontological aspects of geospatial data. In this paper, we present an ontology-driven map generalization algorithm, called DMin, that can be tailored to particular users and users’ tasks. The level of detail in a generated map is a...

Elicitation methods in decision-making under risk allow us to infer the utilities of outcomes as well as the probability weights from the observed preferences of an individual. An optimally efficient elicitation method is proposed, which takes the inevitable distortion of preferences by random errors into account and minimizes the effect of such errors on the inferred utility and probability we...

We study whether probability weighting is observed when individuals are presented with a series of choices between lotteries consisting of real non-monetary adverse outcomes, electric shocks. Our estimation of the parameters of the probability weighting function proposed by Tversky and Kahneman (1992) are similar to those obtained in previous studies of lottery choice for negative monetary payo...

This paper presents a new feature weighting method for distance-based classifiers. It is based on a generalized least squares minimization of a criterion function to estimate a feature relevance metric. Experiments over both artificial and real data sets illustrate the behaviour of this algorithm when irrelevant attributes and/or features with varying relevance are present. Effectiveness of the...

This paper presents two methods for constructing an approximate performance function of a desired parameter using correlated parameters. The methods are useful when real-time measurements of a desired performance function are not available to applications such as extremum-seeking control systems. The first method approximates an a priori measured or estimated desired performance function by com...

In this paper, we propose a novel intensity-based similarity measure for medical image registration. Traditional intensity-based methods are sensitive to intensity distortions, contrast agent and noise. Although residual complexity can solve this problem in certain situations, relative modification of the parameter can generate dramatically different results. By introducing a specifically desig...