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Economics of Biased Estimation
Drvoštěp, Tomáš ; Špecián, Petr (advisor) ; Tříska, Dušan (referee)
This thesis investigates optimality of heuristic forecasting. According to Goldstein a Gigerenzer (2009), heuristics can be viewed as predictive models, whose simplicity is exploiting the bias-variance trade-off. Economic agents learning in the context of rational expectations (Marcet a Sargent 1989) employ, on the contrary, complex models of the whole economy. Both of these approaches can be perceived as an optimal response complexity of the prediction task and availability of observations. This work introduces a straightforward extension to the standard model of decision making under uncertainty, where agents utility depends on accuracy of their predictions and where model complexity is moderated by regularization parameter. Results of Monte Carlo simulations reveal that in complicated environments, where few observations are at disposal, it is beneficial to construct simple models resembling heuristics. Unbiased models are preferred in more convenient conditions.
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Tools for Decision Making under Uncertainty
Sečkárová, Vladimíra
In this paper we focus on two often considered distinct aims, namely maximizing of an utility function (e.g. an investment profit) and getting a more reliable global description of considered situation based on observed data (e.g. the final outcome of databases merging). In both cases we face the problem, that the data are unreliable, since they contain uncertainty caused by their source (i.e. human being). If we are looking for the optimum of the former aim, a game theory reformulation of the decision making task brings a smoother way to reach it. If the latter aim is considered, a merging procedure (also called fusion) processing the data should help us. This paper describes four recently developed methods dealing with decision making under uncertainty in two considered directions and one tool used for comparison of the fusion algorithms.
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