Productivity equation and the m distributions of information processing in workflows

TL;DR

This paper develops a theoretical productivity equation for workflows based on information theory and probabilistic distributions, enabling flexible and predictive analysis of organism-object interactions without heavy empirical data.

Contribution

It introduces a novel productivity equation derived from information theory, applicable to probabilistic workflows and organism-object interactions, enhancing flexibility and predictability.

Findings

The productivity equation is robust across probabilistic workflow definitions.

Mathematical derivations enable workflow prediction without strict empirical data.

Framework supports flexible workflows in organism-object environments.

Abstract

This research investigates an equation of productivity for workflows regarding its robustness towards the definition of workflows as probabilistic distributions. The equation was formulated across its derivations through a theoretical framework about information theory, probabilities and complex adaptive systems. By defining the productivity equation for organism-object interactions, workflows mathematical derivations can be predicted and monitored without strict empirical methods and allows workflow flexibility for organism-object environments.