20200123 14:06 
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20200113 08:29 
MeanRisk Optimization Problem via Scalarization, Stochastic Dominance, Empirical Estimates
Kaňková, Vlasta
Many economic and financial situations depend simultaneously on a random element and on a decision parameter. Mostly it is possible to influence the above mentioned situation by an optimization model depending on a probability measure. We focus on a special case of onestage two objective stochastic “MeanRisk problem”. Of course to determine optimal solution simultaneously with respect to the both criteria is mostly impossible. Consequently, it is necessary to employ some approaches. A few of them are known (from the literature), however two of them are very important: first of them is based on a scalarizing technique and the second one is based on the stochastic dominance. First approach has been suggested (in special case) by Markowitz, the second approach is based on the second order stochastic dominance. The last approach corresponds (under some assumptions) to partial order in the set of the utility functions.\nThe aim of the contribution is to deal with the both main above mentioned approaches. First, we repeat their properties and further we try to suggest possibility to improve the both values simultaneously with respect to the both criteria. However, we focus mainly on the case when probability characteristics has to be estimated on the data base.
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20200113 08:29 
Second Order Optimality in Markov and SemiMarkov Decision Processes
Sladký, Karel
SemiMarkov decision processes can be considered as an extension of discrete and continuoustime Markov reward models. Unfortunately, traditional optimality criteria as longrun average reward per time may be quite insufficient to characterize the problem from the point of a decision maker. To this end it may be preferable if not necessary to select more sophisticated criteria that also reflect variabilityrisk features of the problem. Perhaps the best known approaches stem from the classical work of Markowitz on meanvariance selection rules, i.e. we optimize the weighted sum of average or total reward and its variance. Such approach has been already studied for very special classes of semiMarkov decision processes, in particular, for Markov decision processes in discrete  and continuoustime setting. In this note these approaches are summarized and possible extensions to the wider class of semiMarkov decision processes is discussed. Attention is mostly restricted to uncontrolled models in which the chain is aperiodic and contains a single class of recurrent states. Considering finite time horizons, explicit formulas for the first and second moments of total reward as well as for the corresponding variance are produced.
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20191209 08:15 
ESP32CAM – POSTAVME SI OČIČKO
Zajíček, Milan
Mikrokontroler ESP32 je možné zakoupit jako vývojovou desku ve spojení s 2Mpixel kamerou OV2640. Tento modul je souhrnně označován ESP32cam. Tutoriál ukazuje možnost použití uvedeného modulu pro snímání obrazu ve formě statických snímků i videa a možnosti komunikace s okolím, či ukládání dat na SD kartu. Pro komunikaci s PC je použit USBSerial převodník CP2102.
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20191209 08:15 
On Experimental Part of Behavior under Ambiguity
Kratochvíl, Václav ; Jiroušek, Radim
People are risktakers, riskaverse, or neutral. In the literature, one can find experiments illustrating the ambiguity aversion of human decisionmakers. Recently, a personal coefficient of ambiguity aversion has been introduced. We have decided to measure the coefficient and its stability during the time. In this paper, we describe performed experiments and their structure to launch a discussion of possible design weaknesses or to suggest other methods of measuring it.
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20191209 08:15 
Preliminary Results from Experiments on the Behavior under Ambiguity
Jiroušek, Radim ; Kratochvíl, Václav
In the literature, some experiments proving that human decisionmakers manifest an ambiguity aversion are described. In our knowledge, no one has studied a possibility to measure the strength of this aversion and its stability in time. The research, we have recently started to realize should find out answers to these and similar questions. The goal of this paper is to present some preliminary results to initiate a discussion that should help us to modify either the process of data collection and/or the analysis of the collected data.
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20191209 08:15 
A Step towards Upperbound of Conflict of Belief Functions based on Nonconflicting Parts
Daniel, M. ; Kratochvíl, Václav
This study compares the size of conflict based on nonconflicting parts of belief functions $Conf$ with the sum of all multiples of bbms of disjoint focal elements of belief functions in question. In general, we make an effort to reach a simple upper bound function for $Conf$. (Nevertheless, the maximal value of conflict is, of course, equal to 1 for fully conflicting belief functions). We apply both theoretical research using the recent results on belief functions and also experimental computational approach here.
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20191209 08:15 
Proceedings of the 22nd CzechJapan Seminar on Data Analysis and Decision Making
Inuiguchi, M. ; Jiroušek, Radim ; Kratochvíl, Václav
The history of the series of the CzechJapan seminars started in 1999. Thus, it is now more than 20 years ago when the first CzechJapan Seminar on Data Analysis and Decision Making under Uncertainty was held in JAIST, Hokuriku. Since that time, these seminars were held in eleven splendid places in Japan, offering the Czech participants possibility to discover different parts of the Japanese islands. In reciprocity, it was the goal of the Czech partners organizing the past ten seminars to show the beauty of Czechia to Japanese colleagues, who, during the long Japan–Czech cooperation, became our close friends. This is also why the seminar has never visited one place two times.
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20191209 08:15 
Theory of SSB Representation of Preferences Revised
Pištěk, Miroslav
A continuous skewsymmetric bilinear (SSB) representation of preferences has recently been proposed in a topological vector space, assuming a weaker notion of convexity of preferences than in the classical (algebraic) case. Equipping a linear vector space with the socalled inductive linear topology, we derive the algebraic SSB representation on a topological basis, thus weakening\nthe convexity assumption. Such a unifying approach to SSB representation permits also to fully discuss the relationship of topological and algebraic axioms of continuity, and leads to a stronger existence result for a maximal element. By applying this theory to probability measures we show the existence of a maximal preferred measure for an infinite set of pure outcomes, thus generalizing all available existence theorems in this context.
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20191125 10:16 
A Robustified Metalearning Procedure for Regression Estimators
Kalina, Jan ; Neoral, A.
Metalearning represents a useful methodology for selecting and recommending a suitable algorithm or method for a new dataset exploiting a database of training datasets. While metalearning is potentially beneficial for the analysis of economic data, we must be aware of its instability and sensitivity to outlying measurements (outliers) as well as measurement errors. The aim of this paper is to robustify the metalearning process. First, we prepare some useful theoretical tools exploiting the idea of implicit weighting, inspired by the least weighted squares estimator. These include a robust coefficient of determination, a robust version of mean square error, and a simple rule for outlier detection in linear regression. We perform a metalearning study for recommending the best linear regression estimator for a new dataset (not included in the training database). The prediction of the optimal estimator is learned over a set of 20 real datasets with economic motivation, while the least squares are compared with several (highly) robust estimators. We investigate the effect of variable selection on the metalearning results. If the training as well as validation data are considered after a proper robust variable selection, the metalearning performance is improved remarkably, especially if a robust prediction error is used.
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