The complexity of analysing dynamical systems often lies in the difficulty to monitor each of their dynamic properties. In this article, we use qualitative models to present an exhaustive way of representing every possible state of a given system, and combine it with Bayesian networks to integrate quantitative information and reasoning under uncertainty. The result is a combined model able to give explanations relying on expert knowledge to predict the behaviour of a system. We illustrate our approach with a deterministic model to show how the combination is done, then extend this model to integrate uncertainty and demonstrate its benefits
DOCUMENT
Background: The aim of this study is to validate a newly developed nurses' self-efficacy sources inventory. We test the validity of a five-dimensional model of sources of self-efficacy, which we contrast with the traditional four-dimensional model based on Bandura's theoretical concepts. Methods: Confirmatory factor analysis was used in the development of the newly developed self-efficacy measure. Model fit was evaluated based upon commonly recommended goodness-of-fit indices, including the χ2 of the model fit, the Root Mean Square Error of approximation (RMSEA), the Tucker-Lewis Index (TLI), the Standardized Root Mean Square Residual (SRMR), and the Bayesian Information Criterion (BIC). Results: All 22 items of the newly developed five-factor sources of self-efficacy have high factor loadings (range .40-.80). Structural equation modeling showed that a five-factor model is favoured over the four-factor model. Conclusions and implications: Results of this study show that differentiation of the vicarious experience source into a peer- and expert based source reflects better how nursing students develop self-efficacy beliefs. This has implications for clinical learning environments: a better and differentiated use of self-efficacy sources can stimulate the professional development of nursing students.
DOCUMENT
In social settings, people often need to reason about unobservablemental content of other people, such as their beliefs, goals, orintentions. This ability helps them to understand, to predict, and evento influence the behavior of others. People can take this ability furtherby applying it recursively. For example, they use second-order theory ofmind to reason about the way others use theory of mind, as in ‘Alicebelieves that Bob does not know about the surprise party’. However,empirical evidence so far suggests that people do not spontaneously usehigher-order theory of mind in strategic games. Previous agent-basedmodeling simulations also suggest that the ability to recursively applytheory of mind may be especially effective in competitive settings. Inthis paper, we use a combination of computational agents and Bayesianmodel selection to determine to what extent people make use of higherordertheory of mind reasoning in a particular competitive game, theMod game, which can be seen as a much larger variant of the well-knownrock-paper-scissors game.We let participants play the competitive Mod game against computationaltheory of mind agents. We find that people adapt their level oftheory of mind to that of their software opponent. Surprisingly, knowinglyplaying against second- and third-order theory of mind agents enticeshuman participants to apply up to fourth-order theory of mindthemselves, thereby improving their results in the Mod game. This phenomenoncontrasts with earlier experiments about other strategic oneshotand sequential games, in which human players only displayed lowerorders of theory of mind.
DOCUMENT
