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 behavioural research to investigate understanding of dynamic systems. In these studies, the dynamic system is often represented in the form of a simulation model, and they almost exclusively focus on how the structure of a system (i.e. the simulation model) affects human's inference of system behaviour. In doing so, these studies fail to consider that human's performance on dynamic decision making tasks might also be a function of the complexity embedded in other task components like goals, input, processes, output, time and presentation. Hence, the objective of this paper is to carve 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) inaccuracy; (7) novelty; (8) incongruity; (9) connectivity; and (10) temporal demand.