An accurate qualitative and comprehensive assessment of human potential is one of the most important challenges in any company or collective. We apply Bayesian networks for developing more accurate overall estimations of psychological characteristics of an individual, based on psychological test results, which identify how much an individual possesses a certain trait. Examples of traits could be a stress resistance, the readiness to take a risk, the ability to concentrate on certain complicated work.
Słowa kluczowe: Bayesian network, graphical probability model, psychological test, probabilistic reasoning
Jakościowa oraz kompleksowa ocena potencjału ludzkiego jest jednym z najważniejszych wyzwań dla każdej firmy lub grupy. Sieci Bayesowskie stosowane są do opracowywania dokładniejszych ogólnych oszacowań cech psychologicznych jednostki, w oparciu o wyniki testów psychologicznych, które określają, jak dużo dana osoba posiada pewnych cech.
Keywords: Sieci Bayesowskie, graficzny model prawdopodobieństwa, test psychologiczny, rozumowanie probabilistyczne
In this article we discuss applications of Bayesian network methods for solving typical and highly demanding tasks in psychology. We compute overall estimates of the psychological personality traits, based on given answers on offered psychological tests, as well as a comprehensive study of the social status of the individual, their religious beliefs, educational level, intellectual capabilities, the influence of a particular social environment, etc. We believe that the most optimal mathematical model for solving this problem is a graphical probabilistic model with strongly expressed cause-effect relations. Therefore, we chose the Bayesian network as our model. Advantages of the Bayesian network are as follows: 1) The Bayesian network reflects the causal-effect relationship very well. 2) The mathematical apparatus of Bayesian networks is well developed and thus, there are many software implementations of the Bayesian network methods available. Bayesian framework is very popular in various kinds of applications: parameter identification, Bayesian update, uncertainty quantification, inverse problems , and classification. Bayesian network is a graphical probabilistic model that represents a set of random variables and their conditional dependencies via a directed acyclic graph , , . For example, a Bayesian network could represent the probabilistic connections between overall economical situations, average salaries and nationalism in society. It can give recommendations to local governments of which steps to undertake to decrease the level of political tensions. Other promising applications are in Human Resource (HR) departments and in marriage agencies. Bayesian networks, by analyzing psychological properties of each individual, and sociological connections between individuals, may help to select a better group for a certain task, prevent possible conflicts and increase performance. In this work we will apply Ba [...]
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