Document Type : Scientific - Research


1 Professor, Professor, Dept. of Mechanical Engineering K. N. Toosi Univ. of Technology

2 Khajeh Nasir Toosi University of Technology (KNTU), Tehran, Iran


In this paper, it is shown that in many cases, science can predict the future. Based on the available information of the real system, the scientific method for future prediction is to develop a virtual model with similar response to the real system. The classical models are usually presented with a set of linear / nonlinear, ordinary / partial differential equations. The necessary and sufficient condition for future prediction is given by three axioms; A model with accurate description of the real system, perfect knowledge of the initial conditions, and perfect knowledge of present and future values of inputs to the system. In reality, none of these conditions are perfectly realizable and each axiom has some uncertainties. The failure in future prediction is exactly due to these uncertainties. The model description as the major axiom is always accompanied with uncertainty and although, there is no way to vanish it, there are different methods to reduce it. Soft computing is one of the recent approaches in the past few decades, not only used for modeling of systems in the field of engineering systems, but also used in the field of human sciences such as economy, management and social sciences.


World Future Society (1966). Retreived from
World Futures Studies Federation (1973). Retreived from
Association of Professional Futurists (2002). Retreived from
The Millennium Project: Global Futures Studies & Research (2009). Retreived from
Tehran University (2010). Faculty of New Sciences and Technologies. PhD course in futures studies. Retreived from  [in Persian].
Amirkabir University (2012). Faculty of Management, Science and Technology. PhD course in futures studies. Retreived from:  [in Persian].
Inayatullah, S. (2013). Futures studies: Theories and methods. World Futures Studies Federation.
Masini, E. B. (2011). How to teach futures studies: Some experiences. The Journal of Futures Studies, 15(4), 111-120.
Kuosa, T. (2011). Evolution of futures studies. Futures, 43(3), 327-336.
Roney, C. W. (2010). Intersections of strategic planning and futures studies: Methodological complementarities. The Journal of Futures Studies, 15(2), 71-100.
Smith, A. C. T. (2005). Complexity theory for organizational futures studies. Foresight, 7(3), 22-30.
Phdungslip, A. (2011). Futures studies backcasting method used for strategic sustainable city planning. Futures, 43(7), 707-714.
Veeman, S. A. (2013). Futures studies and uncertainty in public policy: A case study on the ageing population in the Netherlands. Futures, 53, 42-52.
Kreibich, R., Oertel, B., & Evers-Wӧlk, M. (2011). Futures studies and future-oriented technology analysis principles, methodology and research questions. SSRN Electronic Journal.
Holland, J. H. (1992). Adaptation in natural and artificial systems. Massachusets Institute of Technology.
Kosko, B. (1994). Fuzzy thinking: The new science of fuzzy logic. Hyperion.
Meadows, D. H., et al. (1972). The limits to growth: A report for the club of Rome’s project on the predicament of mankind. New York :Universe Books.
Hughes, B. B. (2019). International futures: Building and using global models. Academic Press (Elsevier Ltd).
Hubbard, J. H., & West, B. H. (1995). Differential equations: A dynamical systems approach: Higher dimensional systems. Springer.
Ruth, M., & Hannon, B. (2012). Modeling dynamic economic systems. Springer.
Dolfin, M., Leonida, L., & Outada, N. (2017). Modeling human behavior in economics and social science. Physics of Life Reviews, 22, 1-21.
Driscoll, J. C., & Holden, S. (2014). Behavioral economics and macroeconomic models. Journal of Macroeconomics, 41, 133-147.
Geweke, J., Horowitz, J. L., & Pesaran, H. (2007). Econometrics: A bird’s eye view. Cambridge University, Faculty of Economics.
Zadeh, L. A. (1994). Fuzzy logic, neural networks, and soft computing. Communications on the ACM, 37(3), 77-84.