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.
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:
- Complex Adaptive Systems
- Ontologies
- Objects
Comparison of the Approaches to Complexity Sciences
|
Aspect |
Peter Belohlavek’s approach |
Preexisting approaches: Bateson, Förster, Lorenz, Maturana, Morin, Prigogine |
| 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 |
Comparison of Ontologies with the Unicist Ontology
|
Comparison of: |
Ontology (Philosophy) |
Ontology (Information Science) |
Unicist Ontology (Complexity Sciences) |
| 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 | 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 |
| Validation model | Inexistent | Inexistent | Unicist logic |
| Taxonomic structure | Inexistent | Based on shared validation | Defined by the Unicist Algorithms |
| Mathematic validation | Inexistent | Inexistent | Following the Unicist logic |
| Deals with | Ideas | Categories and objects | Algorithms and business objects |
| Oneness | One ontology for each aspect of reality | Depending on the consensus of the expert opinions | One ontology for each functionality |
Comparison of the different types of objects
|
Objects Oriented Programming Main concepts of objects in IT programming |
Complex Adaptive Main concepts of |
Adaptive Systems Main concepts of objects in nature (e.g. a tree) |
| Class | Restricted Context | Species |
| Object | Business Object | Entity |
| Inheritance | Homologous Inheritance | Inheritance |
| Method | Method | Functionality |
| Event | Action | Action |
| Message | Information System | Nervous System |
| Attributes | Fundamentals | Morphology |
| Abstraction | Ontogenetic Map | Genotype |
| Encapsulation | Unified Field | Phenotype |
| Polymorphism | Polymorphism | Polymorphism |
| – | Synchronicity | Synchronicity |
| – | Critical Mass | Critical Mass |
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).
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.
The Unicist Research Institute
NOTE: The Unicist Research Institute was the pioneer in complexity science research and became a private global decentralized leading research organization in the field of human adaptive systems.
https://www.unicist-school.org/theoryofevolution/wp-content/uploads/2013/11/turi.pdf
