Business intelligence

Big data

Decision analysis

Engineering

Philosophy

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Statistics

Bounded rationality

Economics

Data mining

Computer science

Business decision mapping

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Management science

Mathematics

Management

Rationality

Epistemology

Data science

Artificial intelligence

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Journal of Decision System

Decisions continue to be important to researchers, organizations and societies. However, decision research requires re-orientation to attain the future of data-driven decision making, accommodating such emerging topics and information technologies as big data, analytics, machine learning, and automated decisions. Accordingly, there is a dire need for re-forming decision theories to encompass the new phenomena. This paper proposes a modern data-driven decision theory, DECAS, which extends upon classical decision theory by proposing three main claims: (1) (big) data and analytics (machine) should be considered as separate elements; (2) collaboration between the (human) decision maker and the analytics (machine) can result in a collaborative rationality, extending beyond the classically defined bounded rationality; and (3) meaningful integration of the classical decision making elements with data and analytics can lead to more informed, and possibly better, decisions. This paper elaborates the DECAS theory and clarifies the idea in relation to examples of data-driven decisions.

DECAS: a modern data-driven decision theory for big data and analytics