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Supra-Bayesian Approach to Merging of Incomplete and Incompatible Data
Sečkárová, Vladimíra
In practice we often need to take every available information into account. Unfortunately the pieces of information given by different sources are often incomplete (with respect to what we are interested in) and have different forms. In this work we try to solve the problem of treating such data in order to get an optimal merger of them. We present a systematic and unified way how to combine the pieces of information by using a Supra-Bayesian approach and other mathematical tools, e.g. Kerridge inaccuracy, maximum entropy principle. To show how the proposed method works a simple example is given at the end of the work.
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Multi-dimensional trading problem in multi-participant settings
Zeman, Jan
The dimensionality of optimization problem arising within multi-market trading task grows exponentially with a growing number of markets. To prevent the dimensionality problem, multi-market trading is represented as a multi-participant decision making problem with finite common capital. Each local DM task is a single-market trading enriched by an ability to share a part of local capital with other local DM tasks (participants). The paper provides formulation of the problem and basic algorithmic steps. The approach is illustrated on the real market data.
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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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