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Convexity in stochastic programming model with indicators of ecological stability
Houda, Michal
We develop an optimization model dealing with construction expenses that are prescribed as a result of the EIA (Environmental Impact Assessment) process. The process is an obligatory part of every large construction project and evaluates possible influences of the project to the environment, including population health, natural and other socio-economic aspects; the result of the process is a set of recommendation and arrangements the construction must meet. Our optimization model incorporates uncertainties in model parameters; we represent them through their probabilistic distribution. Furthermore, to overcome a problem with quantifying subjective utility function of ecological impacts, we measure them by so-called indicators of ecological stability. The resulting problem is stochastic programming problem formulated as (C)VaR model used traditionally in finance area. In our contribution we deal with convexity properties of this problem – these are especially important from the theoretical as well as from the computational point of view.
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On problem of optimization under incomplete information
Volf, Petr
The paper studies consequences of incomplete information to uncertainty of results of stochastic optimization. Stochastic characteristics of optimized system are evaluated from observed data, moreover, the data may be incomplete. Namely, we consider the random censoring of observations frequently encountered in time-to-event (of lifetime) studies. The analysis of uncertainty will be based both on theoretical properties of estimated stochastic characteristics and on simulated examples.
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Using indicators of ecological stability in stochastic programming
Houda, Michal
When building bigger construction the EU law impose the so-called EIA process - evaluation of possible influences of the construction on the environment and population health, grouped into several categories. Outputs of the EIA process are recommendations to the investors compensating the negative impacts of the constructions by additional arrangements. In our contribution we develop an innovative approach to model the expenses devoted to obey the EIA rules by stochastic programming tools: especially, we represent uncertainty in parameters by their probabilistic distributions, and subjective utility function representing the ecological demands is modelled via so-called indicators of ecological stability. The model takes into account budget limitations, several legislative obligations, and other ecological aspects; the goal is to help choose the optimal compensating constructions and arrangements. The resulting stochastic programming model is seen as parallel to V@R problem.
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Empirical Estimates in Economic and Financial Problems via Heavy Tails
Kaňková, Vlasta
Optimization problems depending on a probability measure correspond to many economic and financial applications. Complete knowledge of this measure is necessary to solve exactly these problems. Since this condition is fulfilled only seldom, the problem has to be usually solved on the data basis to obtain satistical estimates of an optimal value and optimal solutions. Great effort has been paid to investigate properties of these estimates; first under assumptions of disribution with thin tails and linear dependence on the probability measure. Recently, it has appeared an investigation in the case of nonlinear dependence on the probability measure and heavy tailed distributions with shape parameter greater two. We focus on the case of the stable and Pareto distributions with a shape parameter in the inteval (1, 2).
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Risk-Sensitive and Average Optimality in Markov Decision Processes
Sladký, Karel
This contribution is devoted to the risk-sensitive optimality criteria in finite state Markov Decision Processes. At first, we rederive necessary and sufficient conditions for average optimality of (classical) risk-neutral unichain models. This approach is then extended to the risk-sensitive case, i.e., when expectation of the stream of one-stage costs (or rewards) generated by a Markov chain is evaluated by an exponential utility function. We restrict ourselves on irreducible or unichain Markov models where risk-sensitive average optimality is independent of the starting state. As we show this problem is closely related to solution of (nonlinear) Poissonian equations and their connections with nonnegative matrices.
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Separable Utility Functions in Dynamic Economic Models
Sladký, Karel
In this note we study properties of utility functions suitable for performance evaluation of dynamic economic models under uncertainty. At first, we summarize basic properties of utility functions, at second we show how exponential utility functions can be employed in dynamic models where not only expectation but also the risk are considered. Special attention is focused on properties of the expected utility and the corresponding certainty equivalents if the stream of obtained rewards is governed by Markov dependence and evaluated by exponential utility functions.
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A Simple Decision Problem of a Market Maker
Šmíd, Martin
We formulate a simple decision model of a market maker maximizing an utility from his consumption. We reduce the dimensionality of the problem to one. We nd that, given our setting, the quotes set by the market maker depend on the inventory of the traded asset but not on the amount of cash held by the market maker.
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Dependent Data in Economic and Financial Problems
Kaňková, Vlasta
Optimization problems depending on a probability measure correspond to many economic and financial applications. The paper deals with the case when an empirical measure substitutes the theoretical one. Especially the paper deals with a convergence rate of the corresponding estimates. ``Classical" results for independent samples are recalled, situations in which the case of dependent sample can be (from the mathematical point of view) reduced to independent case are mentioned. A great attention is paid to weak dependent samples fulfilling the Phi-mixing condition.
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Analysis of occurrence of extremes in a time series with a trend
Volf, Petr
We consider a random series of values and are interested in the analysis and modeling the occurrence of extremes. One of approaches is the analysis of sequence of block maxima. As we assume that the series has a trend, we first select a proper regression model for the block maxima development. From it, a Markov chain of the sequence of extremes is derived. As the transition probabilities of the chain are not tractable analytically, we use the Monte Carlo generation of the chain behavior. Then, from the sample representing the series of block maxima we obtain the rediction of their future behavior.
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Empirical Estimates in Stochastic Optimization: Special cases
Kaňková, Vlasta
Classical optimization problems depending on a probability measure belong mostly to nonlinear deterministic optimization problems that are relatively complicated. On the other hand, these problems fulfil very often "suitable" mathematical properties guaranteing the stability (w.r.t. probability measure) and, moreover, giving a possibility to replace the "underlying" probability measure by an empirical one to obtain "good" stochastic estimates of the optimal value and the optimal solution. Properties of thess estimates have been investigated mostly for standard types of probability measures with suitable (thin) tails and independent random samples. However distributions with heavy tails correspond to many economic problems and, moreover, many applications do not correspond to the "classical" problems. The aim of the paper is, first, to try to recall stability results including also heavy tails and more general problems.
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