# 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.

## Key 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.

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Source: https://tomesphere.com/paper/1906.06997