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Simulation Modeling to Improve Decision Making in Complex-Dynamic Environments

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Improving decision-making abilities

Researchers studied dynamic complexity challenges and how they often overwhelm decision makers. Behind this enormous challenge lay the inadequacy of our intuitive skills in coping with complex dynamic decision-making situations.

Industrial Technologies

Researchers on the project SIMID (Simulation modeling to improve decision making in complex-dynamic environments) investigated what leads to poor understanding and, further, what leads to poor decisions. For the same reasons, effective learning does not typically take place in complex decision-making environments. There were five main results achieved. The first was a rule of thumb that was suggested for parameter value selection for the well-known anchor-and-adjust heuristic used in stock management for a wide range of conditions. The second was a soft landing model that was developed and different heuristics were compared in detail. The third result was a basic inventory control model and a game developed based on this model. Experiments were designed and completed. The next result was a complex workforce and service backlog management model with an accompanying game based on this model. The design and execution of the experiments were completed. Lastly, a detailed mathematical model of a famous decision-making task known as The Beer Game was developed. It is coded in R and simulation experiments are carried out based on this model. 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. It is necessary for doctors and nurses managing the health of their patients with chronic illnesses. Managers at all levels in both public and private industries need good decision-making skills. In order to improve our ways of dealing with the increasingly complex issues of the modern world, the dynamic complexities of the world must be understood. Understanding the complexity of the modern world provides the solutions and not the problems of tomorrow. These research results can be generalised to all different types of real-life dynamic decision problems for practical use.


Intuitive skills, simulation modeling, decision making, complex-dynamic environments, anchor-and-adjust

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