ABSTRACT
Introduction: Decision support systems and uncertainties This chapter discusses scienti‰c uncertainty (Beer, 2006; Benjamin and Cornell, 1970), fuzziness, and application of stochastic-fuzzy models in urban transit, water resources, energy planning, and education (universities’ admission process and other higher educational institutes [HEIs] in developing economies). It enunciates the prime place of decision support systems (DSS) models in providing a robust platform for enabled action on developmental issues. Scientists now recognize the importance of studying scienti‰c phenomenon having complex interactions among their components. These components include not only electrical or mechanical parts but also “soft science” (human behavior, etc.) and how information is used in models. Most real-world data for studying models are uncertain. Uncertainty exists when facts, state, or outcome of an event cannot be determined with probability of 1 (in a scale of 0-1). If uncertainty is not accounted for in model synthesis and analysis, deductions from such models become at best uncertain. The “lacuna” in understanding the concept of uncertainty and developmental policy formulation/implementation is not only due to the non-acceptability of its existence in policy foci, but also the radically different expectations and modes of operation that scientists and policymakers use. It is therefore necessary to understand these differences and provide better methods to incorporate uncertainty into policy making and developmental strategies (Figure 2.1) (Ibidapo-Obe, 1996; Ibidapo-Obe and Asaolu, 2006; Ibidapo-Obe and Ogunwolu, 2004).
