Complexity Sciences - The Unicist School

What are Complexity Sciences?

The objective of the Unicist Approach to Complexity Sciences developed by Peter Belohlavek was to find a scientific approach to understand nature and provide a structure to emulate it when designing, building or managing complex adaptive systems.

Belohlavek developed the epistemological structure for complexity sciences, by developing the unicist ontological methodology for complex systems research, which substituted the systemic approach to research adaptive systems and was materialized in the unicist logical approach to deal with adaptiveness.

This is an excerpt comparing the different approaches that intended to deal with Complexity Sciences.

It needs to be stated that the unicist approach developed the first integrated structure to manage complex adaptive systems.

Until the existence of this approach the methods of systemic sciences were used as a palliative to deal with complex adaptive behaviors.

Expanding the Boundaries of Sciences

As it is known, the management of complexity has been an unsolved challenge for sciences. This challenge was faced in 1976 by The Unicist Research Institute that was a pioneering organization in finding a structural solution for complexity without using artificial palliatives.

The paradigm shift, based on the emulation of nature, was developed to solve the need of having reliable knowledge to deal with complex environments. It was provoked by the fallacy of considering empirically-justified knowledge as reliable knowledge.

It allowed managing complex environments as a unified field.

The paradigm shift was triggered by the need to understand complex adaptive systems. The shift implies having subordinated the empirical approach to sciences to a pragmatic, structuralist and functionalist approach to deal with complex environments that integrates, at an operational level, the preexisting empiricism.

Unicist Paradigm Shift in Sciences

This is a superior level in sciences that integrates complexity sciences with systemic sciences using the double-dialectical logic to emulate the ontogenetic intelligence of nature and using objects to emulate the organization of nature.

The scientific evidences of the Unicist Theory

In this document you will find seven scientific evidences of the Unicist Theory, which confirm its functionality, that allows dealing with complex systems. These evidences are:

The functionality of amino acids
The structure of atoms
The structure of biological entities
The nervous system
Similarity between natural and social objects
The homology between the unicist concepts and the stem cells
The homology between thinking processes and the functionality of electricity

Access the scientific evidences: https://www.unicist-school.org/complexity-sciences/scientific-evidences/

Complexity Science Research

The unicist theory expanded the frontiers of sciences making the scientific approach to complex adaptive systems possible without needing to use arbitrary palliatives to transform complex systems into systemic systems in order to be able to research them.

Paradoxically, this is a breakthrough and a back to basics. On the one hand, it is a breakthrough because it changed the paradigms of scientific research. On the other hand, it is a back to basics because it drives sciences to deal with the nature of reality.

The unicist logical approach opened the possibilities of managing complexity sciences using a pragmatic, structured and functionalist approach.

The unicist approach to complexity is based on the research of the unicist ontological structure of a complex adaptive system which regulates its evolution. This is based on emulating the structure of the unicist ontogenetic intelligence of nature considering that every functional aspect of reality has a unique unicist ontological structure.

The approach to ontological structures of reality requires going beyond the dualistic thinking approach and being able to use the double dialectical logic to approach complex adaptive systems.

The research in complexity science needs to have its own format for its presentation that has a structural difference with the papers for systemic sciences (abstract, introduction, materials and methods, discussion, literature).

The Comparison with Alternative Approaches

The structure of the unicist approach to complexity sciences implies the integration of a unicist ontological approach, which defines the structure of the nature of a specific reality with the use of unicist objects that allow emulating the organization of nature to predict the behavior of complex adaptive systems, design them, built them or manage them.

Access to a synthetic comparison of the Unicist Approach with the different approaches based on their nature and functionality:

1) Complex Adaptive Systems
2) Ontologies
3) Objects

Comparison of the Approaches to Complexity Sciences

Aspect	Peter Belohlavek’s approach to Complexity Sciences	Preexisting approaches: Bateson, Förster, Lorenz, Maturana, Morin, Prigogine and others
Field of Study	Complex adaptive systems	Complex adaptive systems
Approach	Pragmatic – Structural – Functionalist	Empirical
Definition of the field of study	A specific reality as a unified field that includes the restricted and wide contexts and the emergence of the system	Based on the emergence of the system
Possibility of external observation	Inexistent	Inexistent
Research method	Unicist Ontological Research	Systemic research
Boundaries of the system	Open	Open
Self organization	Concepts – analogous to strange attractors	Strange Attractors / undefined
Structure	Double Dialectics Dynamics Purpose – active function – energy conservation function	Variables
Relationship between the elements	Following complementation and supplementation laws	Undefined
Evolution / Involution	Based on the evolution/involution laws of the ontogenetic intelligence of nature	Undefined
Processes	Object driven processes	Undefined
Certainty	Dealing with possibilities and probabilities	Dealing with probabilities
Demonstration	Real applications	Real applications
Emulation in mind	Double dialectical thinking (using ontointelligence)	Complex thought
Emergence	Results	Results
Chaos	Inexistent	Existent
Influence on the system	Based on actions and driving, inhibiting, entropy inhibiting, catalyzing and gravitational objects.	Based on actions
Validation	Destructive and non-destructive tests (real applications)	Systemic research validation methods

System Dynamics Approach vs. Unicist Approach

The system dynamics approach and the unicist approach to complexity were created for different purposes.

Introduction

Complex adaptive systems are integrated by unicist objects. The human body and social systems are evident examples. Unicist objects are complex adaptive systems that assume the role of generating a necessary output. These objects assume multiple shapes according to their functionality in a complex adaptive system. Complex systems might be adaptive or not. The level of adaptiveness increases the complexity of a system.

The System Dynamics Approach

The system dynamics approach was created to develop solutions in complex controllable adaptive environments by managing their operational structures. This approach manages univocal relationships and univocal cause-effect actions and their feedback.

This approach is functional in controllable environments, which implies having a low level of complexity. The definition of variables is an artificial pathway that is only functional in controllable complex adaptive systems. The system dynamics approach generates fallacious conclusions when it is applied in extremely adaptive or non-adaptive systems.

The Unicist Approach

The unicist approach was created to develop solutions in complex influenceable adaptive environments by managing their nature given by their functional concepts. This approach manages bi-univocal relationships and double dialectical actions and their feedback. Complex systems require managing the unicist objects they include, ensuring that they have the necessary critical mass to fulfill their function.

The unicist approach is necessary in environments that cannot be controlled but can be influenced, which implies having a high level of complexity. The influence depends on the capacity of building asymmetric complementation with negative slope in a system. This approach is based on the discovery of the structure of concepts that define the “nature of things” and their functionality emulating the triadic intelligence of nature.

Comparison:

Aspects	System Dynamics Approach Jay Wright Forrester	Unicist Approach Peter Belohlavek
Application fields	Complex systems with low level of complexity	Complex systems with high level of complexity
Model	Functional integration of the components	Unified field defined by the underlying concepts
Structure of the system	Functional operational structure of the system	Conceptual structure of the functionality of the system
Boundaries of the system	Controlled boundaries	Open boundaries
Epistemological framework	Dualistic – empiric framework	Triadic – pragmatic, structural, and functionalist framework
Knowledge	Technical analytical	Technical analytical + Conceptual (Fundamentals)
Knowledge model	Empirical	Rules and logical inference based
Tools	Empirical tools	Logical and empirical tools
Relationship between components	Univocal cause-effect relationships	Bi-univocal cause-effect relationships
Actions	Univocal actions and their feedback	Double dialectical actions and their feedback
Thinking	Systemic thinking	Unicist thinking
Confirmation – control	Simulation	Unicist Destructive and non-destructive testing
Type of environment	Controllable	Influenceable
Reliability of results	Probabilities	Possibilities + Probabilities
Epistemological foundations	Empirical	Based on the triadic structure of the concepts that define the functionality of things
Approach	Managing the interaction of components	Managing the concepts that define the nature and functionality of the systems and their objects
Modelling	Dynamic models	Conceptual models
Management	Of variables	Of unicist objects
Observers	The observers are not part of the system (controlled boundaries)	The observers are part of the system (open boundaries)
Research	Systemic research based on variables	Unicist ontological research based on objects
Future scenario	Designable	Influenceable

Conclusion

The complexity of a system is an intrinsic functional characteristic that defines its level of adaptiveness. Therefore, the use of the system dynamics approach, in complex environments that cannot be controlled, is just a palliative that does not ensure the generation of results. On the other hand, the use of the unicist approach in controllable environments allows expanding their functionality.

The management of complex adaptive functions requires the use of the unicist approach to make systems controllable and uses the system dynamics approach to operate them. The controllability depends on the level of influence that can be exerted in a system and needs to be measured using unicist destructive tests. The unicist approach uses the system dynamics approach as soon as the influence that can be exerted ensures the controllability.

Comparison of Ontologies with the Unicist Ontology

Comparison of:	Ontology (Philosophy) Aristotle, Wolff, Kant and others	Ontology (Information Science) Gruber, Sowa, Arvidsson and others	Unicist Ontology (Complexity Sciences) Peter Belohlavek
Purpose	Knowledge acquisition	Information and knowledge acquisition	Managing complex adaptive systems and adaptive processes
Foundations	Discovery	Shared expert opinions	Ontogenetic Intelligence of Nature and discovery of functionalities
Use in business	To apprehend reality	Artificial Intelligence and building of complex information systems	Manage human adaptive systems and adaptive processes
Scope of application	Universal	Artificial Intelligence, Information Systems	Development of ontogenetic maps for the individual, institutional, business and social fields.
Language used	Natural language	Web Ontology Language and others	Unicist Standard Language and natural language
Results to be achieved	True knowledge	Valid knowledge and information	Value generation
Evolution / Involution laws	Inexistent	Inexistent	Unicist laws of evolution
Formalization model	Inexistent	Inexistent	Unicist double dialectical logic
Functional structure	Inexistent	Based on shared validation	Emulating the ontogenetic intelligence of nature
Mathematical validation	Inexistent	Inexistent	Following the Unicist logic
Deals with	Ideas	Categories and objects	Functions, roles and objects
Oneness	One ontology for each aspect of reality	Depending on the consensus of the expert opinions	One ontology for each functionality

Comparison of Unicist Objects with existing types of Objects 3.1

Aspects	Objects in Information Technology	Objects in Nature	Unicist Objects in Adaptive Environments
Mindset to Apprehend Objects	Dualistic Logic, Class Logic, Propositional Logic	Integrative Logic, Fuzzy Logic, Predicate Logic	Integrative Logic, Fuzzy Logic, Predicate Logic
Purpose	Minimum Strategy	Maximal & Minimum Strategy	Maximal & Minimum Strategy
Example	Programming Objects	The Organs of the Human Body	Commercial Objects
Category	Class	Species (Role)	Concept
Dependence	Inheritance	Inheritance	Homologous Inheritance
Operation	Method	Adaptive Method	Adaptive Method
Value Generation	Event	Action	Action
Activation	Message	Nervous System	Business Intelligence
Functionality	Attributes	Functionality	Fundamentals
Essential Characteristics	Model	Genotype	Ontogenetic Map
Design	Encapsulation	Phenotype	Unified Field Diagram
Pluralism	Polymorphism	Polymorphism	Polymorphism
Dynamic	Synchronicity	Synchronicity	Synchronicity
Influence	Functional	Critical Mass	Critical Mass

Synthesis

The unicist approach to complexity sciences is a pragmatic, structural and functionalist approach.

This approach establishes the framework for the research on complexity sciences but also for the unicist logical approach that uses the conclusion of the researches in their application in the field of complex adaptive systems.