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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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On Approximate Fully Probabilistic Design of Decision-Making Units
Kárný, Miroslav
An efficient support of a single decision maker is vital in constructing scalable systems addressing complex decision-making (DM) tasks. Fully probabilistic design (FPD) of DM strategies, an extension of dynamic Bayesian DM, provides a firm basis for such a support. The limited cognitive and evaluation resources of the supported decision maker cause that theoretically optimal solutions are realised only approximately. Thus, the truly efficient support has to include reliable means for constructing approximate solutions of DM subtasks. The current paper deals with the design of the approximately optimal DM strategy for a known environment model and adequately described DM preferences. The design relies on: a) the explicit minimiser found within FPD; b) randomised nature of the strategy provided by FPD.
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A unified view on roots of imperfection
Kárný, Miroslav
Decision making (DM), broadly interpreted as an active choice among alternatives, is ubiquitous. A range of normative theories has arisen aiming at support and analysis of DM. Classical Savage's axiomatisation led to Bayesian DM, which suits DM with a non-negligible uncertainty. Observed discrepancies between recommendations of the normative theory and DM practice represent the major challenge of the related research. The talk discusses these discrepancies, and: a) respects the presence of imperfect decision maker; b) considers neglecting of importance of closed-loop behaviour as their major cause; c) provides tasks where a) and b) do matter and where a unified view on imperfection roots can help.
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Estimating Efficiency Offset between Two Groups of Decision-Making Units
Macek, Karel
The comparison of two groups of decision-making units (DMUs) has been already subject of scientific reflection. So far, some statistical tests have been developed. This article addresses estimating the difference between expected outputs of two groups of DMUs. In contrast to other efficiency evaluation methods, this publication focuses on quantitative assessment of this difference, not on the hypothesis testing. The article focuses on single output DMUs and the designed statistical tests are examined on various simulated data sets as well as on one realworld example. Some of them stem from the data envelopment analysis, others are related to the local regression.
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A note on weighted combination methods for probability estimation
Sečkárová, Vladimíra
To successfully learn from the information provided by avail- able information sources, the choice of automatic method combining them into one aggregate result plays an important role. To respect the reliability in the source’s performance each of them is assigned a weight, often subjectively influenced. To overcome this issue, we briefly describe the method based on Bayesian decision theory and elements of infor- mation theory. In particular we consider discrete-type information, rep- resented by probability mass functions (pmfs) and obtain an aggregate result, which has also form of pmf. This result of decision making pro- cess is found to be a weighted linear combination of available information. Besides the brief description of the novel method, the paper focuses on its comparison with other combination methods. Since we consider the available information and unknown aggregate as pmfs, we mainly focus on the case when the parameter of binomial distribution is of interest and the sources provide appropriate pmfs.
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Preliminaries of probabilistic hierarchical fault detection
Jirsa, Ladislav ; Pavelková, Lenka ; Dedecius, Kamil
The paper proposes a novel probabilistic fault detection and isolation (FDI) system that enables to evaluate dynamically the industrial system condition (health) at any level of its functional hierarchy. The investigated industrial system is considered as a set of interconnected individual components. Each component acts in its noisy environment as an imperfect participant, more or less dependent on neighbouring components and, in turn, influencing some others. The nature of the problem prevents us from expressing sufficiently hard propositions about the health of the system as a whole at once but we can observe and construct propositions at lower system hierarchies. These propositions (opinions) are combined at higher levels using the rules of probabilistic logic, retaining the ignorance and finally yielding a single opinion on the health of the whole monitored system.
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