Premise: innovation in a digital economy requires treating value production in an enterprise, regardless of its mission and scope, as a computation.
Due to the effects of engaged human beings there is ambiguity, and therefore clarity, in equating “enterprise” with a “quantifiable unit of computation.” It is caused partly semantic, the meaning of enterprise, and partly in the belief that we can automate whatever can be exactly specified, especially the precise meaning of value production.
In any social group there are several possibly competing definitions of the term enterprise. For some it is a noun, signifying an organizational unit (e.g., commercial, religious or political) of some unspecified size and mission. For others, it is an adjective, modifying a noun, such as “Mary is an enterprising person.” And still others might associate the term with a project or process, a verb signifying a specific kind of action.
Implicitly underlying these interpretations is the concept of a performance metric, a value proposition, defining the benefits of an organizational unit, achievements derived from Mary’s industriousness, or results achieved by a process. Value propositions are the cause celebre that justify an enterprise’s existence, the benefits of being enterprising, and the achievements of enterprise activities.
Value propositions are predicates, conditional statements of the form
if <condition>, then <perform action A>, else <perform action B>
Value propositions are computable expressions, defined in possibly nested “if…, then…, else…” statements, and implemented in neural networks, Petri nets, fuzzy logic, mathematical or algorithmic software, firmware, wetware or hardware expressions.
Essentially all value production is a computation, whether in the form of an enzyme catalyzing a biochemical reaction or a multinational company offering a new commercial service for a customer in one of its regional market segments. Consequently, we can think in algorithmic terms and study the dimensions and processes of value creation. With availability of high performance computing and associated modeling, simulation, data analytics and visualization tools, the dawn of the 21st century is witnessing uprecidented growth in our understanding of physical and synthetic sciences (e.g., physics, biology, chemistry, economics, medicine, environment), developing their representative computational sciences as enablers supporting new insights and discoveries.
Admittidly, some computations define value propositions that are determinsitc (predictable), where others, perhaps the most important ones, are nondeterminstic and governed by probablistic or chaotic effects. Nevertheless, attempts to provide automation of these computations, intelligent or otherwise, requires expressing them with as much fidelity as our computational sciences permit.
Jay Bayne, PhD
Principal, Stratum 4
Research Professor, Mathematics, Statistics & Computer Science, Marquette University