This paper introduces the foundations of Bayesian probability theory and Bayesian decision method. The main goal of Bayesian decision theory is to minimize the expected loss of a decision or minimize the expected risk. The purposes of this study are to review the decision process on the issue of flood occurrences and to suggest possible process for decision improvement. This study examines the ...

Individuals in nature frequently face decision problems where the information available to them is uncertain and their reproductive success depends on the outcome of their decisions. In these cases natural selection should be expected to favour individuals whose behavioural strategies yield the best reproductive payoffs. It is accepted that decision-makers in nature should evolve to behave as i...

The paper presents fraud detection method to predict and analyze fraud patterns from data. To generate classifiers, we apply the Naïve Bayesian Classification, and Decision Tree-Based algorithms. A brief description of the algorithm is provided along with its application in detecting fraud. The same data is used for both the techniques. We analyze and interpret the classifier predictions. The m...

The degree of confidence in one’s choice or decision is a critical aspect of perceptual decision making. Attempts to quantify a decision maker’s confidence by measuring accuracy in a task have yielded limited success because confidence and accuracy are typically not equal. In this paper, we introduce a Bayesian framework to model confidence in perceptual decision making. We show that this model...

Bayesian networks are limited in differentiating between causal and spurious relationships among decision factors. Decision making without differentiating the two relationships cannot be effective. To overcome this limitation of Bayesian networks, this study proposes linking Bayesian networks to structural equation modeling (SEM), which has an advantage in testing causal relationships between f...

The field of Judgement and Decision Making has for some time been dominated by normative theories which attempt to explain behaviour in mathematical terms. We argue that such approaches provide little insight into the cognitive processes which govern human decision making. The dominance of normative theories cannot be accounted for by the intractability of processing models. In support of this ...

Among the lessons learned from recent conflicts stands the dramatic change in the very way wars are fought. There are no more clear-cut enemies or allies; rules of engagement have become increasingly fuzzy; guerrilla and insurgent tactics are now commonplace: in short, the battlespace is a very different place from what it used to be. Furthermore, advances in sensor technology and network compu...

Computational models of learning have proved largely successful in characterizing potential mechanisms which allow humans to make decisions in uncertain and volatile contexts. We report here findings that extend existing knowledge and show that a modified reinforcement learning model, which has separate parameters according to whether the previous trial gave a reward or a punishment, can provid...