A probabilistic evolutionary learning model with epistemological meaning in Islamic economics and finance

Purpose - Learning field of events is characterized by the occurrenceof random and uncertain phenomena, all of which have probabilistic distributions. The meaning of learning is exchange by interdependence between interacting agents. Such agents are both the human entities and the non‐human ones. Thus, in a learning field of probabilistic events there are complex forms of interaction between the domains of mind (human cognition) and matter (world‐system). The purpose of this paper is to formalize and study such interactions by the epistemology of unity of being and becoming of relations between given variables in analytical perspective. Design/methodology/approach - The critical argumentation and search in this paper leads to the premise of the episteme of unity of knowledge. It is found singularly in the doctrine of the paired universe of the Quran. The episteme of oneness of the monotheistic law and its consequential forms establish the axiomatic basis of the criterion function representing the phenomenon of probabilistic learning field. The authors refer to this criterion as wellbeing. It conceptualizes and measures the degree of unity of being and becoming that exists between the variables of a specific problem under investigation. Findings - The results of this study formalize the probabilistic model of learning. The simulated evaluation of the probabilistic form of the wellbeing function brings out the synonymous results between unity of knowledge and its impact on the unity of the world‐system induced by the knowledge‐flows. Such a transformation of a world‐system presents the meaning of endogenous (or systemically self‐regenerated) ethics and morality in such broader fields of choices involving embedded learning systems. Originality/value - The dynamics of pervasive complementarities arising from learning by unity of knowledge, and considerations of ethics and morality remain exogenous factors in economic theory. This paper, instead, has formalized ethical endogeneity in models of decision‐making with probabilistic learning fields that remain embedded in complementarities by interaction and integration across economic, social and ethical systems.