On the performance of projects under uncertainty: an agent-based simulation modelling

This paper investigates the effects of environmental complexity on project performance by means of simulation modelling. There are few works that provide a bottom-up, mechanism-based account of project-environment interactions. Rather, some statistical, top-down models have been developed which fall short of explaining how projects can handle uncertainties. This research is an attempt to fill this gap. The question is how environmental complexity affects project performance. The conceptual model developed for this research comprised of three propositions. The first proposition is about environmental complexity. The results of the model show that the environmental complexity can have positive effects on project performance if project team have required capabilities to learn and adapt. The second proposition is about learning memory. To tackle environmental complexity, the model suggests that shorter memory cycles improve project performance in that projects should give more priority to recent outcomes to offset effects of complexity. Finally, the third proposition is about internal complexity. The results demonstrate that the internal complexity modelled as randomness in the activity times is detrimental to the project performance as the project teams are not able to focus on efficiency and execution. The results have been validated by empirical data from 18 infrastructure projects.