Unicist Functionalist Approach

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Unicist Functionalist Approach

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The Unicist Standard


The Unicist Standard is the formal structure that ensures the functionalist approach, going beyond empiricism, to manage adaptive systems and environments. It was established in 2014 to confirm the functionality of technologies, tools and solutions in adaptive environments.

The standard is based on the use of the unicist ontological approach that defines the functionality of things and functional knowledge that includes the know-how and the know-why of the functionality and operation of adaptive solutions.

All the solutions developed at The Unicist Research Institute include the application of the Unicist Standard.

Unicist Logic

The confirmation of solutions using the Unicist Standard requires:

  1. Being focused on acquiring functional knowledge.
  2. Managing the unicist ontological approach that defines the functionality of things.
  3. Confirming the validity of the ontogenetic maps of the adaptive functions.
  4. Managing the fundamental and technical analysis of the solutions.
  5. Using functional design to build adaptive solutions.
  6. Using binary actions to ensure results.
  7. Developing destructive and non-destructive tests to confirm the validity of the solutions.

1. Unicist Functional Knowledge

Functional knowledge is defined as the integration of the “know how” of adaptive systems and environments with the “know why” of their functionality. These two approaches are integrated by a reasoning process that allows making knowledge reasonable, understandable, and provable.

The access to the functional knowledge of adaptive systems requires accepting that all types of adaptive systems have a functional structure that is implicit in the intelligence of nature, which means that they have an implicit purpose, an active function that defines their possibility to expand, and an energy conservation function that ensures their survival.

This requires approaching adaptive systems using a unicist ontological approach that defines the existence of adaptive systems and environments based on their functionality. The functionality of adaptive systems can be understood when these two conditions are given.

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2. The Unicist Ontological Functionalist Approach

Emulating the ontogenetic intelligence of nature

The Unicist Ontology describes the nature and functionality of facts, ideas, individuals and things, regarded from their essential, causative / functional and operational aspects, erasing the existent barrier between the human arbitrary division of philosophy, science and action, and defining concepts that integrate them in a unified field. In the short or long run, living beings and their deeds are consistent with their nature.

The ontogenetic intelligence of nature defines the nature and functionality of an entity. The ontogenetic intelligence of nature is defined by a purpose, an active principle and an energy conservation principle that are integrated in their oneness defining the functionality of the entity. The active principle drives the evolution while the energy conservation principle sustains the purpose. The ontogenetic intelligence of an entity defines its intrinsic concepts that regulates its evolution.

The Unicist Ontology describes the nature and functionality of reality by emulating the ontogenetic intelligence of nature. Therefore, there is an ontological logic to understand the nature of reality. Nature is not a question of opinion. From a functional point of view, the nature of a specific reality is unique. That is why there can only be “one” unicist ontology of something.

This development made complex adaptive systems reasonable, understandable and predictable in those cases in which the structure of the intelligence that underlies their nature has been found. The research began in the field of social, economic and behavioral sciences. Then it evolved, driven by homologies with confirmed knowledge, towards life sciences and ended with physics to confirm the validity of the unified field.

The Unicist Ontology defines and describes the functionality of things. Its knowledge is needed to deal with adaptive entities. A metaphor clarifies this:

The cost of a glass is in its solid;
its value is in its hollow.
Its cost has no value.
Its value has no cost.
But both of them are within the glass.


The cost of a process is given by its operation;
its value is given by its functionality.
Operation has no value.
Functionality has no cost.
But both of them are within the process.

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3. The Confirmation of Ontogenetic Maps

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

The dynamic research methodology is:

  1. Develop the hypothetical structure of the ontology.
  2. Analyze the ontology and divide it into sub-ontologies following the laws of complementation and supplementation (only when necessary and possible).
  3. Define the observable results needing to be considered to validate the ontology.
  4. Define the application fields of the ontology to validate its functionality.
  5. Develop the applications beginning with destructive and non-destructive pilot tests to forecast reality.
  6. Develop at least five experiences in the application field differing completely one from the other (neither analogous nor homologous).
  7. Develop forecasts of at least three periods with full certainty.
  8. Restart the research process every time a deviation occurs.

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.

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4. Fundamentals and Technical Analysis

Fundamental Analysis

Fundamental analysis is the approach that defines the limits of the possibilities of the evolution of a given reality. Fundamentals define the boundaries implicit in the functionality of that given reality.

knowledge acquisition

Although adaptive systems and complex systems have open boundaries, they can only be managed when limits have been defined.

Defining limits based on the fundamentals of a given reality implies dealing with its nature and accepting its evolution laws. In the short or the long run the evolution of a given reality will drive towards its nature.

Fundamental analysis provides the tools to describe the nature of a reality in order to forecast its evolution. Evolution can be inhibited and catalyzed by human actions; but it cannot be changed.

Technical Analysis

Technical analysis deals with the cause-effect relation between “variables” that have been identified by making a systemic compromise.

In order to be able to manage a reality in everyday actions it is necessary to define it with systemic tools.

Systemic tools are based on cause-effect relations and therefore the result of transforming a complex reality into a simple system downgrades the possibilities of success. In technical analysis success becomes probabilistic.

Fundamental analysis defines the possibilities (0 or 1) and technical analysis defines the probabilities (from 0 to 1).

Fundamental analysis has been downgraded during the last 30 years. As there were no objective tools to approach it, it was considered as the “subjective” aspects of technical analysis.

The discovery of the unicist ontology of evolution and the structure of the concept that regulate the evolution of living beings and their deeds, established the structure for fundamental analysis integrating it with technical analysis in order to develop reliable knowledge.

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5. Functional Design

The adaptiveness of an adaptive system or environment is fully dependent on the functionality of its processes. The adaptiveness of an entity is defined by the concepts and fundamentals of its functions while the consequent operational processes ensure the results.

The functionality of processes defines their potential energy while the effective energy is defined by the operationality of the processes. The effective energy cannot exceed the available potential energy of an entity.

Therefore, the functional design of an adaptive system or environment is essential to define its possibilities. It defines the value a system can generate.

This requires making its functional design that allows organizing based on its nature, which is defined by the underlying concepts and fundamentals. As it was mentioned, the functionality of these fundamentals defines the potential energy of any adaptive system.

The use of functional design differs structurally from operational design. While the functional design emulates processes, the operational design simulates processes. Therefore, there is a need to have sound knowledge to be able to emulate processes first and simulate these processes afterwards.

The purpose of the functional design of an adaptive system is the design of its operational process based on the knowledge of its functionality.

This begins to be achieved by developing a unicist conceptual engineering process that allows transforming universal ontogenetic maps into specific ontogenetic maps that describe the functionality of their specific functions. This description includes the indicators of the functionality or dysfunctionality of the fundamentals of processes.

The final stage to develop the operational process of an adaptive system is having the knowledge of the “know how” and the “know why” of the processes included in the system. When this knowledge is functional, the functionality, dynamics and evolution of the system is ensured.

The results of adaptive systems are omnipotent fantasies unless they have been tested. Adaptive system testing implies testing their functionality and requires a precise design of the tests. The “trial and error” use of objects is not a pilot test.

Pilot tests are the drivers of the unicist reflection processes. Pilot tests have two objectives:

  1. Defining the limits of knowledge
  2. Validation of knowledge

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6. Unicist Binary Actions in Adaptive Environments

Binary actions are two synchronized actions that expand businesses while they ensure their results. They were developed to manage the evolution of adaptive environments by managing actions to install maximal strategies to grow and minimum strategies to ensure results.

Binary actions empower the value of processes while they diminish their costs. They use catalysts to expand the boundaries of solutions and business objects to accelerate processes and ensure their functionality.

The are four types of UBAs:

  • UBAs Type 1: that integrates the influence of the environment with the specific function or object. It includes the integration of external catalysts.
  • UBAs Type 2: that integrates the active and the energy conservation function of the maximal strategy.
  • UBAs Type 3: that integrates the active and the energy conservation function of the minimum strategy.
  • UBAs Type 4: that develops the central binary actions of the essential function.

Each UBAs include two levels of actions:

  • Level a) that deals with the expansion of boundaries, which drives the maximal strategy of a function or object.
  • Level b) that deals with the assurance of results that drives the minimum strategy of a function or object.

The process of the use of UBAs begins by developing the UBAs Type 1, which implies expanding the boundaries of an activity, continues with the UBAs Type 2, to development the maximal strategies, UBAs Type 3 to develop the minimum strategies, and UBAs Type 4 to manage the essential function of the process.

In the case of processes with a low level of complexity, the UBAs 2 and 3 might become unnecessary.

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7. Destructive and Non-Destructive Testing

Destructive testing

The approach to complex problems, requires finding the limits of the validity of a given knowledge. To do so, it is necessary to develop experiences in homologous fields until the limits of validity are found.

Two elements are homologous when they have the same “nature”. A whale and a dog (an extreme example) are homologous if they are considered as mammals. A dollar and a yen are homologous considering that they are both money.

These two cases demonstrate that homology can be total or partial. When the knowledge necessary to influence a reality is confirmed in a totally homologous field, then it is naturally secure knowledge. The extreme condition of this example is the homology of two identical elements.

This confirmation process is a destructive test for knowledge that is applied to realities with incomplete homologies. The destruction occurs when a condition is found to demonstrate the fallacy of the knowledge.

Non-destructive testing

Validation implies the factual confirmation of the validity of knowledge. Validation is achieved when knowledge suffices to exert influence on a reality in a predictable way.

The validation process is homologous to a non-destructive test in the field of material research. Validation implies cause-effect relations.

Therefore, validation can only be applied to a simplified field of a complex reality.

Validation provides a reliable knowledge to operate under controlled conditions.

The knowledge is valid if the conditions of the application environment are analogous and homologous to the characteristics of the validation environment.

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