Abstract

The question posed is whether the optimization methods of calculus that are often used in social and scientific analyses offer an appropriate analytical approach to analyze problems that are immersed in systemic complexity and its consequences. This paper refers to the portfolio of such complex problems belonging to social and scientific forces. We refer to such a complex combination by the term ‘socio-scientific’. In the study of socio-scientific complexity, dynamic preferences, intricate decisions, and uncertain behavior, endogenous relations and systemic perturbations abound. The arguments presented in this paper establish that the methodological approach of optimization and steady-state equilibrium turns out to be nicety rather than objectivity in the presence of complexity. Complex situations of the socio-scientific universe cause perturbations in the variables; there explaining complexity formed by the social embedding of variables. Indeed, human individuals, institutions and governments, and society at large are complex interrelated entities. Therefore, complex interrelations caused by social embedding remain submerged in social perturbations. However, interactions arising from social embedding also generate endogenous and complex relations. The contribution of this paper is in the area of endogenous learning in the wellbeing objective criterion function. The interrelationships between the emergent cognitive variables now cause interactive embedding, complexity, and social perturbations. Yet such perturbations are not altogether uncontrollable. They can be and need to be controlled for the purposes of social explanation—hence demanding predictability—even as the complex nature of evolutionary systemic learning proceeds. The controllability problem of extreme perturbations leads us to formalize a mathematical methodology to study controllable perturbations while avoiding extreme forms of perturbations in socio-scientific theory