Psychological aspects of equation-based modelling

Abstract

Psychological aspects of equation-based modelling languages like Modelica are under-represented in literature. This does not reflect the growth of the corresponding userbase. In this paper we try to close this gap by tackling the problem from three sides: we conduct expert interviews, we conduct an experiment based on self-reported timings to analyse the effects of inheritance on understandability, and we conduct an online experiment to analyse the effects of model representations on the performance at modelling tasks. The expert interviews suggest that experienced modelling experts tend to develop their models from the top-down, while novices do the opposite. Results from the second experiment indicate that the effect of inheritance on the time to understand a model is both significant and large. The results of the last experiment imply that graphical representations outperform block-diagrams for several metrics. These results open a broad research field on the theory ofgoodmodelling practice.

Modeling and simulation of large-scale systems: A systematic comparison of modeling paradigms

“…These results do not confirm that acausal approaches have a growing user base; this was indicated e.g. in [13] for Modelica. Publications with the keyword Simulink are about 20 times more frequent in 2017 than those with the keyword Modelica.…”

“…The utilization of tools based on causal and acausal modeling approaches is investigated using a keyword analysis on Scopus. The candidates were identified based on the knowledge of the authors and the relevant literature such as [5,4,11,13]. We have only considered those languages that have listed more than 50 publications with the respective keyword on Scopus since 2000.…”

“…Pollok et al [13] analyse psychological aspects of acausal modeling approaches. They conducted (i) expert interviews, (ii) an experiment based on self-reported timings to analyse the effects of inheritance on ease of understanding, and (iii) an online experiment to analyse the effects of model representations on the performance in modeling tasks.…”

“…Wetter and Haugstetter[12] reach a similar conclusion. Their study indicates that the model development time in causal languages is five to ten times longer compared with acausal languages.Pollok et al[13] analyse psychological aspects of acausal modeling ap-proaches. They conducted (i) expert interviews, (ii) an experiment based on self-reported timings to analyse the effects of inheritance on ease of understanding, and (iii) an online experiment to analyse the effects of model representations on the performance in modeling tasks.…”

Predictive Modeling in Medicine

“…This methodology can prove highly beneficial in forecasting outcomes associated with a specific ailment due to its simplicity in construction and ease of assessment. Therefore, it is often utilized in predictive analysis when copious amounts of information are available, and the outcome variable has previously been comprehensively characterized [3]. However, challenges arise when establishing an association between the variables used for model building and the precise outcome variable under scrutiny.…”