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Návrh dynamických rozhodovacích strategií pro obchodování na futures trzích
Vosáhlo, Jaroslav ; Guy, Tatiana Valentine (vedoucí práce) ; Lachout, Petr (oponent)
Práce se zabývá problematikou obchodování na komoditních trzích z hlediska investičního spekulanta a zaměřuje se na tzv. komoditní futures kontrakty. Cílem práce je pomocí metod dynamického programování a přibližného dynamického programování navrhnout optimální strategii a tuto strategii otestovat na reálných datech. K dosažení úspěšné strategie jsou použity prostředky bayesovské statistiky pro předpovědi chování náhodných veličin a rizikové ukazatele pro návrh míry opatrnosti při obchodování. Algoritmus je otestován v programu Matlab na více než 15 tisících obchodovacích dnech.
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Lazy Learning of Environment Model from the Past
Štěch, J. ; Guy, Tatiana Valentine ; Pálková, B. ; Kárný, Miroslav
The paper addresses a lazy learning (LL) approach to decision making (DM) problem described in fully probabilistic way. The key idea of LL is to simplify the actual DM problem by using past DM problems similar to the current one. The approach can decrease computation complexity and increase quality of learning when no rich alternative information available. The proposed LL approach helps to learn the environment model based on a proximity of the past and current DM problem with Kullback-Leibler divergence serving as a proximity measure. The implemented algorithm is verified on the real data. The results show that the proposed approach improves prediction quality.
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DEMO: What Lies Beneath Players' Non-Rationality in Ultimatum Game?
Avanesyan, Galina ; Kárný, Miroslav ; Knejflová, Zuzana ; Guy, Tatiana Valentine
The rational strategy suggested by the game theory predicts a human playing Ultimatum Game (UG) would have tendency to decide in accordance with the assumption of self-interested rationality, i.e. to choose more for oneself and offer the least amount possible for co-players [2]. This “utilitarian” and gametheoretically correct “rational” behaviour is however rarely observed when experiments are conducted with human beings [1]. Long-term research in experimental economics indicates that humans do not behave as selfish as traditional economics assume them to do. In UG, human-responders reject offers they find too low while human-proposers often offer more than the smallest amount. An intuitively plausible interpretation of this phenomenon is that responders would rather give up some profit than be treated unfairly. This “non-rational” behaviour provides an insight into human’s motivation as a social being. The work challenges this view and insists on human rationality.
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Scalable Decision Making: Uncertainty, Imperfection, Deliberation, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2013)
Guy, Tatiana Valentine ; Kárný, Miroslav
Machine learning (ML) and knowledge discovery both use and serve to decision making (DM), which has to cope with uncertainty, incomplete knowledge, problem and data complexity and imperfection (limited cognitive and evaluating capabilities) of the involved heterogeneous multiple participants (aka agents, decision makers, components, controllers, classifiers, etc.). Contemporary DM deals with complex systems characterised by heterogeneous components and their goal-motivated dynamic interactions. The individual participants are selfish, i.e. follow their individual goals. There is no well-justified way to influence or describe the resulting collective behaviour of such a system via a well-proved combination of the selfish components. Economic and natural sciences describe concepts governing the functioning of systems of selfish participants as well as ways influencing their behaviour. However, the majority of solutions rely on the human moderator/manager controlling such a system.
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Automated Preferences Elicitation
Kárný, Miroslav ; Guy, Tatiana Valentine
Systems supporting decision making became almost inevitable in the modern complex world. Their efficiency depends on the sophisticated interfaces enabling a user take advantage of the support while respecting the increasing on-line information and incomplete, dynamically changing user’s preferences. The best decision making support is useless without the proper preference elicitation. The paper proposes a methodology supporting automatic learning of quantitative description of preferences. The proposed elicitation serves to fully probabilistic design, which is an extension of Bayesian decision making.
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Sharing of knowledge and preferences among imperfect Bayesian decision makers
Kárný, Miroslav ; Guy, Tatiana Valentine
Bayesian decision theory provides a strong theoretical basis for a single-participant decision making under uncertainty, that can be extended to multi-participant problems. However Bayesian decision theory assumes unlimited abilities of a participant to probabilistically model participant´s environment and to optimise decision-making strategy. The paper proposes a methodology for sharing of knowledge and strategies among participants, that helps to overcome the non-realistic assumption on participant´s unlimited abilities.
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