Steef Peters

By Steef Peters and Karen Stephenson

There are three volumes in this body of work. In volume one, we lay the foundation for a general theory of organizing. We propose that organizing is a continuous process of ongoing mutual or reciprocal influence between objects (e.g., human actors) in a field, whereby a field is infinite and connects all the objects in it much like electromagnetic fields influence atomic and molecular charged objects or gravity fields influence inanimate objects with mass such as planets and stars. We use field theory to build what we now call the Network Field Model. In this model, human actors are modeled as point-like objects in the field. Influence between and investments in these point-like human objects are explained as energy exchanges (potential and kinetic) which can be described in terms of three different types of capital: financial (assets), human capital (the individual) and social (two or more humans in a network). This model is predicated on a field theoretical understanding about the world we live in. We use historical and contemporaneous examples of human activity and describe them in terms of the model. In volume two, we demonstrate how to apply the model. In volume 3, we use experimental data to prove the reliability of the model. These three volumes will persistently challenge the reader’s understanding of time, position and what it means to be part of an infinite field.

Part of the book: Toward a General Theory of Organizing

By Steve Peters and Karen Stephenson

In this addendum, which was the basis for an article published at the network conference 2021, we discuss a mathematical description of a network field. We describe the exchange of capital between objects in a team which we call a network. We make the assumption that exchanging capital between the actors in the field is the same as exchanging kinetic and potential energy. In our model, we use three types of capital: financial, human, and social to represent the qualifications of an object. By analogy, a non-relativistic gravitational field can be described by a time dependent Kinetic Energy part minus a position-dependent Potential Energy part. Here we describe a non-relativistic network field as Lagrangian with a time-dependent Financial Capital part minus a relative position-dependent Potential energy part. The description of the network field and especially the potential energy for a certain area in the field is comparable to the description of a Graph Neural Network for a set of nodes, a concept from deep learning theory. We use the Graph Neural Network to analyze the effects of exchanging potential energy in a network. We also use it to calculate the optimum distribution of qualifications of the actors in a team.

Part of the book: Toward a General Theory of Organizing