SIMID – Simulation Modeling to Improve Decision Making in Complex Dynamic Environments

Short Description

The overall aim of this project is to improve decision making in the presence of dynamic complexity, which is a challenging task. The main reason behind this challenge is the inadequacy of our intuitive skills in coping with complex dynamic decision-making situations. Dynamic complexity often overwhelms human decision makers, leading to poor understanding, which further leads to poor decisions. For the same reasons, effective learning does not typically take place in complex decision-making environments. Simulation game-based human experiments show that performances of participants initially improve, but quickly plateau at substantially non-optimum levels after a few trials with the same game (Paich and Sterman, 1993; Diehl and Sterman, 1995; Langley and Morecroft, 2004; Moxnes, 2004; Yasarcan, 2010). Most interestingly, the dynamic complexity we refer to does not necessitate hundreds or even tens of variables. It has been established that the dynamics of just 2-3 variables can be very complex if their interactions involve delayed feedback loops and non-linear relations (Langley and Morecroft, 2004; Moxnes, 2004; Moxnes and Saysel, 2009; Sterman 2000 and 2002; Yasarcan and Barlas, 2005; Yasarcan, 2007, 2010, and 2011). This type of complexity involving only a few variables is also called systemic-dynamic complexity. A typical real-life example of the problems caused by systemic-dynamic complexity is supply-chain oscillations. Inventories on a supply chain oscillate as a result of over or under ordering mainly caused by the weakness of human decision making in taking into account the delays in shipments. Managing sustainable growth in public and private domains is another typical example dynamic complexity. In early growth phases, growth is relatively easy and the decision makers do not recognize the approaching limits, especially since such limits are not fixed, but dynamically created by the very growth process itself. When the growth limits are recognized, it is often too late to avoid the imminent collapse, due to delays and non-linear effects in the system. Boom-and-bust histories of many private companies and the current global debate about how to prevent a catastrophic climate change are examples of systemic complexity of growth management. The main objective of this project is to explore the roots of such decision complexities on relatively small simulation models and suggest methods and heuristics to improve decision making in complex environments.

•     Diehl, E.; Sterman, J.D.; 1995. Effects of Feedback Complexity on Dynamic Decision Making. Organizational Behavior and Human Decision Processes, Vol. 62, Issue 2; pp. 198-215.
•     Langley, P.A.; Morecroft, J.D.W.; 2004. Performance and Learning in a Simulation of Oil Industry Dynamics. European Journal of Operations Research, Vol. 155, Issue 3; pp. 715-732.
•     Moxnes, E.; 2004. Misperceptions of Basic Dynamics: The Case of Renewable Resource Management. System Dynamics Review, Vol. 20, Issue 2; pp. 139-162.
•     Moxnes, E.; Saysel, A.K.; 2009. Misperceptions of Global Climate Change: Information Policies. Climatic Change, Vol. 93, Issues 1-2; pp.15-37.
•     Paich, M.; Sterman, J.D.; 1993. Boom, Bust, and Failures to Learn in Experimental Markets. Management Science, Vol. 39, Issue 12; pp. 1439-1458.
•     Sterman, J.D.; 2000. Business Dynamics: Systems Thinking and Modeling for a Complex World. Irwin/McGraw-Hill: Boston, MA.
•     Sterman, J.D.; 2002. All Models are Wrong: Reflections on Becoming a Systems Scientist. System Dynamics Review, Vol. 18, Issue 4; pp. 501-531.
•     Yasarcan, H.; 2007. Stock Management in the Presence of Perception Delays, Proceedings of The 2007 Conference on Systems Science, Management Science & System Dynamics, Shanghai - China.
•     Yasarcan, H.; 2010. Improving Understanding, Learning and Performances of Novices in Dynamic Managerial Simulation Games: A Gradual-Increase-in-Complexity Approach. Complexity, Vol. 15, Issue 4; pp 31-42.
•     Yasarcan, H.; 2011. Stock Management in the Presence of Significant Measurement Delays. System Dynamics Review, Vol. 27, Issue 1; pp 91-109.
•     Yasarcan H.; Barlas, Y.; 2005. A Generalized Stock Control Formulation for Stock Management Problems Involving Composite Delays and Secondary Stocks. System Dynamics Review, Vol. 21, Issue 1; pp. 33-68.

Final Results and their Potential Impact and Use

Improving dynamic decision making is of great importance for very diverse types and levels of managing complex dynamic systems: it is important for individuals trying to control their own weight; for doctors and nurses managing the health of their patients with chronic illnesses; for managers at all levels in both public and private industries; for rectors, deans, and heads of schools managing their universities and schools; for ministers and bureaucrats managing various public institutions in EU, and so forth. In order to improve our ways of dealing with the increasingly complex issues of the modern world, we first need to understand the dynamic complexities of the world we live in. Researchers increasingly argue that systems perspective and understanding of dynamic complexity will play a crucial role in the future of humanity because without such a perspective, the unintended consequences of our decisions about important complex problems will render our efforts ineffective, even harmful. Unless we understand the complex counterintuitive behaviors of dynamic systems that we face in modern world, the ‘solutions’ that we implement today are likely to be our ‘problems’ of tomorrow.

This research contribute to the improvement of dynamic decision making in three different ways:

1. This research serves as a step towards having a better understanding of dynamic complexity.

2. Simulation experiments carried out using artificial decision making agents demonstrate counter-intuitive results. Soon-to-be completed experiments using human decision makers will demonstrate further interesting results as suggested by the pilot studies we have conducted.

3. Some generic dynamic decision making heuristics (i.e. control heuristics) are proposed, which can be used in diverse simulation problems with different contexts but with similar underlying structures.

With additional work, these three classes of research results can all be generalized to different types of real life dynamic decision problems illustrated above.