Future of socio-scientific modelling

The formalism on convergence to a universal and unique theory in all the sciences is a project of realizing a universal and unique model that can answer the nature of scientific arguments in general in ‘everything’ (Bar- row, 1991)8 and in the derived particulars of the general-system modelling. This course of research invites a burst of diverse issues and problems that are specific to different disciplines yet are embedded in the generality of interactive system. But they are all capable of smdying by the self-same model of socio-scientific investigation. Thus, we coin the term “the socio- scientific” to represent the holistic systemic worldview of integr ated science and society. Within this lies economics as social science.

As well, since universality and uniqueness of such a model applies to all issues and problems across diverse disciplines the modelling enterprise implies the epistemology of unity of knowledge between the diversely emergent categories of issues, problems, and disciplines. The resulting theory of the socio-scientific ‘everything’ now combines a new substantive theory of the social sciences and the natur al sciences together in one intellectual enterprise. The organic linkage and explanation of concepts and implications emanating from the application of the unique atrd universal model has its methodology premised on the epistemology of unity of knowledge to study the dynamics of organically unified systems. These by their fuzzy space of lack of determinateness reflect non-linear relations.

With such extensions of the modelling enterprise the aspects of the overarching exercise combined with its emerging character of universality and uniqueness in studying the organic unity and process orientation of the embedded sub-systems bring forth the investigation of the embedded social political economy. The corresponding theory of such a process- oriented extensively systemic study as social political economy yields a study of political economy that is different from the received one. Now the traditional definition of political economy ceases to be simply the study of differentiated and disequilibrium dynamics of competing agency and power in ownership, production, and distribution of wealth and resources (George, 1897)9 between opposing agents in economy and society.

The field of social political economy is different. Its interactive field of diverse phenomena appropriately characterizes the pandemic regime. Social political economy as embedded socio-scientific system of interactions is understood as the epistemological study of multitudes of organically unifying sub-systems of the human order. The forces underlying the meeting grounds of interaction between such systems show conflict by marginalism between competing opposites. Interaction, integration, and organically relational unification remain absent (Sztompka, 1991).10 When conflicts exist the epistemological groundwork of unity of knowledge between the ‘de-knowl- edge’-induced multivariate sub-systems denotes an opposite state to moral and ethical field. Such a state of the inter-variables requires moral reconstruction. Thereby, the positivistic nature of the evaluated ‘de-knowledge’ situation becomes an unwanted situation of conflicts and differentiation. This state represents a socio-scientific nature of opposite complementarities and methodological individualism as singularity between the variables and ‘ de-knowledge ’ -induced systems.

Non-linearity underlying reconstruction of the ‘de-knowledge’ state of socio-scientific modelling to the framework of knowledge-induced system modelling bears the properties of interaction leading to integration and then to evolutionary epistemology by intra- and inter-systemic dynamics. In the re-modelled case of knowledge-induction the reconstructed socio-scientific modelling gets premised in the episteme of unity of knowledge. These properties of the reconstructed ‘de-knowledge’ socio-scientific model revert into the knowledge-induced socio-scientific model. It is then accordingly transformed into the model of moral inclusiveness with its econometric properties of predictability and controllability. The transformed sub-systems now establish complementarities in the sense of system-ensemble. The entire evaluation of the wellbeing function conveys analytical consequences that are premised on the epistemological worldview of the unified socio-scientific whole.

Now evolution as history of the future of system-ensemble and a fresh way of understanding the pandemic socio-scientific ensemble represents a reconstructed possibility of sustainability driven by the epistemological nature of organic unity of multidisciplinary systems. We explain such extensively organic unity of relations in terms of pervasive complementarities between the variables and agencies of the embedded sub-systems. The principle of pervasive complementarities in multidisciplinary embedding of the scientific, economic, social, and other forces is equivalent to having intra- systernic and inter-systemic participation between agents, agencies, and their characterizing variables (Choudhury, 2007).11 Such a multidisciplinary ensemble of diversity of the socio-scientific reconstruction establishes the model of holistic moral inclusiveness.


In this chapter we introduce this substantive area of new research of systemic interaction, integration, and evolutionary dynamics driven by the epistemic worldview of unity of knowledge. This episteme overarches across economy, science, and society interrelations in the context of moral inclusiveness embodying the wellbeing objective criterion. The underlying propexties all the more form the complexity of a pandemic episode. The epistemological groundwork of such organic interrelations is based on unity of systemic knowledge. It addresses the phenomenon of organic linkages between multidisciplinary areas overarching all issues and problems that are represented by selected multidisciplinary system-variables interconnecting the systemic relations. Here first, deductive reasoning leads into inductive analytics. In continuation inductive reasoning subsequently re-emerges as deductive continuity of learning across processes of organic unity of knowledge. The governing worldview of unity of knowledge in non-linear and complex learning dynamics is required to endow the emergent processes with sustained predictability and controllability. The emergent modelling of science-economy-society moral inclusiveness of treatment, curative, and their sustainability presented in this work forms the altogether fresh overview of the moral ensemble along with the cognitive field of materiality that embodies the mechanical part of the holistic model of science-economy- society moral inclusion.


The inherent systemically unified methodology expresses two kinds of participative organic unity of being and becoming (Prigogine, 1980).12 First, there is the inner dynamics of interaction within sub-systems. This leads into integration (convergence). Interaction leading to integration continues on to evolutionary' equilibrium. Thus IIE-process dynamics sustain in learning processes. Such interactive, integrative, and evolutionary (HE) dynamics appear and melt away in learning processes within and across the multidiscipline ensemble in reference to the holistic approach to investigating the problems under study. All these properties comprise intra-system dynamics and both characterize and apply to pandemic problems. Second, simultaneously by continuous mathematical functionals there comes about evolution of the interactive and integrative processes into new phases of evolutionary learning intra- and inter-systems (Whitehead, 1978).13 Such emergent processes are referred to as evolutionary learning processes inter-systems.

The resulting organically unifying phases within any learning process are thereby universalized and remain unique in the interdependent socio-scien- tifrc issues by the dynamics of interaction leading to integr ation, and thereby to evolution (IIE). Such dynamics occur continuously in phases of learning and repetition of the same kind of causal functional relations between the multivariates and their circular causation relations.

Knowledge arising from the systemic understanding of embedded subsystems interrelations becomes the foundational force of consequences and change. While knowledge is epistemic in nature relating to the system-ensemble concept (Hubner, 1985),14 its degree of incidence to form organic unity of knowledge-embedded interrelated systems is evaluated by positivistic methods of analysis. The estimated results point out the nonnative futures to be constmcted as opposed to the socially unwanted estimated ones.

In such evolutionary learning dynamics of embedded sub-systems, ‘time’ enters in a peculiar way. Time does not cause change or reconstruction. Thereby, the science-economy-society moral inclusiveness in pandemic treatment and cure is benign of its occurrence and disappearance by the depth of consciousness of moral inclusiveness. In this respect ‘time’ simply plays the deterministic role of recording events and evaluations. Yet in the IIE-process methodology any state of the socio-scientific system is evolved into newer ones by knowledge-flows that are endogenously generated and continued on in the IIE-leaming processes over tune. ‘Events’ by definition are thereby a concrescence of knowledge induction in space and time. Consequences of the recurrent ‘events,’ as by socio-scientific valuations, acmalize in knowledge, space, and time dimensions (Choudlmry, 2009).15

All events in such an epistemic framework of learning dynamics are probabilistic in nature, spanning the self-same pattern of circular causality between knowledge-embedded systems. Thereby, there arise extensively complementary organic relations between the variables representing subsystems. All variables are driven by knowledge-flows. Hence, they remain endogenous in relations interrelating sub-systems by the IIE-evolutionary learning processes.

A brief review of the literature in the thematic field of embedded system modelling pertaining to pandemic regime of treatment and cure

In this brief section (see Choudhury & Hossain, 2007 for an extensive coverage)16 we point out a search for formalism along the type of IIE-model- ling. The emergent non-linear models generate perturbations caused by the multidisciplinary nature and the inherent complex aggregation that emerge (Bertuglia & Vaio, 2005).17 Mathematical functionals of non-linear topological spaces arise (Kupka & Peixoto, 1993).18

Such inferences on the nature of non-linear modelling that emerge by systemic knowledge-embedding go beyond Chichilnisky (1990),19 Gel’fand & Shenitzer (1961),20 and Debreu (1990)21 treatments of topological systems. But the idea of complex aggr egation of topological cells into higher dimensional manifolds of ensemble was started by Smale (1990).22 The multilinear economic relations forming tensor variations of the coefficients of the manifold functionals is referred to as ‘foliation.’ They also represent the scieiice-economy-society moral ensemble, that is moral inclusiveness for the study of pandemic causes, treatment, and curative modelling.

In the end the nonnative study of reconstructed systems is made amenable to complex events in interactive sub-systems. Consequently convexity of optimal surfaces and the assumptions of perfectly competitive markets, and steady-state equilibrium together with the ‘objective’ postulate of optimization, cannot mark the field of such mathematical applications in socio- scientific theory. On the other hand, linearity of the mathematical equations that characterize all of mathematical applications as in econometrics is due to the parametric constant or assumed patterns of probabilistic variations in estimated coefficients. At best, assumed Bayesian probability distributions are assigned to the estimated coefficients. In pandemic ensemble of moral inclusiveness all variables and parameters leam continuously by the consciousness caused by interaction, integration, and evolutionary learning of entities in multidisciplinary ensemble.

Contrary to the linear approach inter-system complexity caused by knowledge-embedding and foliation is exhibited by the learning values of the ‘simulated’ coefficients of the model. The estimated coefficients are probabilistic in nature. But the probability distribution function of the coefficients in the case of modelling sub-systemic knowledge-embedding is determined not by pre-assignment, such as by normal distribution. Rather, the probability distribution of ‘simulated’ coefficients in the IIE-process models of intra- and inter-systemic knowledge-embedding is determined by the prevalent socio-scientific states in which the organic relations appear in any sub-systemic embedding by knowledge-induction.

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