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 decisions. Effective learning cannot 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. 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. Managing sustainable growth in public and private domains is a typical example of systemic-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. 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. System dynamics method will be used in building models that will then be converted into interactive games on which decisions of subjects will be tested. A training method will be developed and tested for the subjects and decision heuristics will be developed and tested by mathematical formulations.
Fields of science
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