Predictions play a key role in assuring the status of “rationality” indecisions. Nevertheless, in the ﬁeld of social sciences and economics, predictionsfail to correctly depict the oncoming scenarios. Why is it so difﬁcult to achievequantitative prediction of social and economic systems? Can science provide reliablepredictions of social and economic paths that can be used to implementeffective interventions? As in the notorious “El Farol bar problem” depicted byBrian Arthur (Am Econ Rev 84:406–411, 1994), the validity of predictive models ismore a social issue than a matter of good mathematics. Predictability in socialsystems is due to limited knowledge of society and human behavior. We do not yethave worldwide, quantitative knowledge of human social behavior; for instance, theperception of certain issues or the predisposition to adopt certain behaviors. Thoughtremendous progress has been made in recent years in data gathering thanks to thedevelopment of new technologies and the consequent increase in computationalpower, social and economic models still rely on assumptions of rationality thatundermine their predictive effectiveness. Through some theoretical and epistemologicalreﬂections, we propose a way in which the cybernetic paradigm of complexitymanagement can be used for better decision-making in complex scenarioswith a comprising, dynamic, and evolving approach. We will show how a cyberneticapproach can help to overcome the fear of uncertainty and serve as aneffective tool for improving decisions and actions.
|Title of host publication||Chaos, Complexity and Leadership 2013|
|Number of pages||7|
|Publication status||Published - 2015|
|Name||SPRINGER PROCEEDINGS IN COMPLEXITY|
- Applied Mathematics
- Modelling and Simulation
- Computer Science Applications