Automated Equation Formulation for Causal Loop Diagrams

The annotation of Business Dynamics models with parameters and equations, to simulate the system under study and further evaluate its simulation output, typically involves a lot of manual work. In this paper we present an approach for automated equation formulation of a given Causal Loop Diagram (CLD) and a set of associated time series with the help of neural network evolution (NEvo). NEvo enables the automated retrieval of surrogate equations for each quantity in the given CLD, hence it produces a fully annotated CLD that can be used for later simulations to predict future KPI development. In the end of the paper, we provide a detailed evaluation of NEvo on a business use-case to demonstrate its single step prediction capabilities.

The vertical dimension of social relations and accurate interpersonal perception: A meta-analysis

There is little consensus regarding how verticality (social power, dominance, and status) is related to accurate interpersonal perception. The relation could be either positive or negative, and there could be many causal processes at play. The present article discusses the theoretical possibilities and presents a meta-analysis of this question. In studies using a standard test of interpersonal accuracy, higher socioeconomic status (SES) predicted higher accuracy defined as accurate inference about the meanings of cues; also, higher experimentally manipulated vertical position predicted higher accuracy defined as accurate recall of others’ words. In addition, although personality dominance did not predict accurate inference overall, the type of personality dominance did, such that empathic/responsible dominance had a positive relation and egoistic/aggressive dominance had a negative relation to accuracy. In studies involving live interaction, higher experimentally manipulated vertical position produced lower accuracy defined as accurate inference about cues; however, methodological problems place this result in doubt.