Complexity Sciences


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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.

Unicist Approach to Complexity SciencesBelohlavek 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.

History of the Evolution of Operational Knowledge

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:

  1. The functionality of amino acids
  2. The structure of atoms
  3. The structure of biological entities
  4. The nervous system
  5. Similarity between natural and social objects
  6. The homology between the unicist concepts and the stem cells
  7. 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.

Complexity Science Research1Paradoxically, 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

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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 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.

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