A generalized model of science-economy-society moral inclusiveness in complex pandemic regime


A mathematical theory of evolutionary learning process that can represent a universal and unique model to study issues and problems spanning all disciplines is unknown in existing socio-scientific studies. This existing state of affairs in socio-scientific methodology is contrary to the permanent existence of an epistemic worldview of unity of knowledge in understanding soul-mind-matter organic interrelations. A theory of socio-scientific system, namely of ‘everything’ that organically explains unification in and between the social and natural sciences by the episteme of unity of knowledge is present to a degree in the natural sciences. But it is absent in political economy, economics, and the social sciences.

The modelling and analysis of complexly evolutionary learning systems that are epistemologically driven by unity of knowledge present non-linear dynamics. A theory of process-oriented modelling in non-linear evolutionary learning spaces underlying economy, science, and society interrelationship is formalized in this chapter.

An implication of empirical work illustrates the treatment of non-linear problems in modelling the interrelations between knowledge-induced interactive variables. The influence of organic relationship characterizing each variable in terms of the other ones is formalized. Non-linearity in such complex knowledge-induced interaction is exhibited either by evolutionary equilibrium or by disequilibrium, as the case may be between convergence to normalcy or mutation, respectively. The empirical method that emerges is referred to as circular causation between the variables of interactive and integrative evolutionary learning process model. We explain this aspect of non-linearity by modelling the interrelationship between economic growth rate, poverty rate, and wellbeing. The substantive concept of wellbeing is defined by means of the objectified organic relations that ought to be explained nonnatively by circular causal relations between the variables following the inductive nature of the prevailing state of complexity.

This chapter is largely a reproduction of the published paper (Choudhury, Mariyanti & Hossain, 2014).1 Its objective is to point out the complex and non-linear nature of the science-economy-society model of moral inclusiveness. Such a creative model is found to be appropriate for application to the study of pandemic treatments, cin e, and normalization. The underlying rationale for selecting such a model for studying the complex pandemic problem is the need for establishing an abstracto-empirical rigorously analytical model of science-economy-society-wide moral inclusiveness. The model formalized for the study of pandemic regime is derived on ontological foundations followed by its wellbeing perspective of application and sustainability.


Socio-scientific models to date have sparsely matured in the area encompassing the broader explanation of knowledge-embedded social processes and events. A remarkable exception is the viewpoint of Hayek’s (1945)2 and Myrdal’s (1958)3 knowledge-embedded systems are characterized by the complexity of system-dynamics concerning the variables that represent the complexity of the knowledge-induced sub-systems. Instead, socio-scientific modelling has proceeded on as an isolated nicety within its own niche of specialization, but at the loss of social realism. Mathematics and statistics have thus continued on to be used in economic modelling in isolation of the greater outlook of social, cultural, and multifaceted decision-making processes (Beed & Kane, 1991).4 These together characterize socio-scientific interaction. This kind of deepening academic development in socio-scientific studies have been to the detriment of studying and enriching the field of credible prediction of the events as we experience them (Soros, 1998).3

Yet the system modelling and inferences gained in the field of knowledge-embedded system, being overarched across sub-systems of the human entirety, reflects a semblance of methodological universality of such broader formalism of modelling. And if the field of such overarching extensions across multidisciplinary sub-systems expands, the emerging models are likely to converge into uniqueness of the modelling enterprise by their capability for higher dimensional analysis. Boland (1991)6 writes in this regard that a higher level of falsification possibility of a theory may supplant a lower-level criticizability. Such a conjectural aspect of episteme and modelling would comprise ever-expanding domains of universality of emergent theories over previously conceptualized ones (Popper, 1998).7

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