Abstract for: Task complexity in individual stock control tasks for laboratory experiments on human understanding of dynamic systems
Dynamic stock control tasks have been frequently used in laboratory experiments in behavioral research to illustrate poor human understanding of dynamic systems. System dynamics modeling has regularly been used as a method to design simulation based stock control tasks. Studies applying these simulations are almost exclusive focused on how the structure of a system (represented in the form of the simulation model) affects human’s inference of system behavior. In doing so, these studies hardly ever take into account that dynamic stock control tasks are more complex than ‘just’ the complexity of the underlying system structures. The concept of ‘task complexity’ is nothing new, but its application to research on human understanding of dynamic systems using stock control tasks applying system dynamics modeling remains virtually absent. Hence, the objective of this paper is to make a first attempt at carving out what task complexity entails when applied to dynamic stock control tasks in order to determine its usefulness for future research on human understanding of such tasks. In this paper, task complexity is conceptualized consisting of ten complexity dimensions: 1) size, 2) variety, 3) redundancy, 4) ambiguity, 5) variability, 6) unreliability, 7) novelty, 8) incongruity, 9) connectivity, and 10) temporal demand.