Emergent Probability - A directed Scale-Free Network Approach to Lonergan's Generic Model of Development

TL;DR

This paper proposes a network-based computational model inspired by Lonergan's heuristic of emergent probability, aiming to simulate development and change across various complex systems.

Contribution

It introduces a novel directed scale-free network approach to formalize Lonergan's qualitative model of development into a computational framework.

Findings

Preliminary network model demonstrates emergent properties of development.

Model captures recursive growth and decline patterns.

Potential applicability across diverse complex systems.

Abstract

An intriguing heuristic model of development, decline, and change conceived by Bernard J.F. Lonergan (BL) in the late 1940's was laid out in a manner now recognizable as representing an early model of complexity. This report is a first effort toward eventually translating that qualitative vision, designated Emergent Probability, into a viable network computer program. In his study of human understanding, Lonergan saw the task of constructing a cohesive body of explanatory knowledge as a convoluted building process of schemes of recurrence that act as foundational elements to further growth. Although BL's kernal recurrent scheme was composed of the cognitional dynamics surrounding Insight, other examples abound in nature: resource cycles, motor skills, biological routines, autocatalytic processes, etc. The corresponding growing generic World Process can alternatively be thought of as chemical, environmental, evolutionary, social, organizational, economical, psychological, or ethical, and its generality might be of particular interest to complex systems researchers.