Unicist Evolutionary Approach

Recovering Charles S. Peirce (Part 3), to Manage a Causal Approach to the Real World

The Causal Approach: The Next Stage for Empirical Approaches

Charles S. Peirce’s work integrates science, philosophy, and actions in a framework to deal with the real world. Peirce offers a framework for understanding the distinction between empirical approaches to the real world and causal approaches. The causal approach represents the next stage of empirical approaches when the principles underlying a given realm are known. If these principles are unknown, there is a need to develop empirical approaches, which are, by definition, provisional, while causal approaches provide a timeless structure based on the functionality of an entity.

Empirical Approaches

For Peirce, empirical approaches are grounded in observation and experimentation. They involve collecting data about phenomena without necessarily understanding the underlying causes. This approach is provisional, as it relies on what can be directly observed or measured at a given time. The empirical method is crucial for generating hypotheses and theories by identifying patterns, correlations, and regularities in the data collected.

Causal Approaches

The causal approach, on the other hand, seeks to explain why phenomena occur by identifying the underlying principles or mechanisms that produce them. In Peirce’s view, a causal understanding provides a “timeless structure” because it aims to reveal the fundamental principles that are not contingent on specific observations but are generalizable across contexts and times.

From Empirical to Causal Understanding

In this framework, empirical approaches provide the groundwork by offering observations and data that suggest patterns and regularities. Causal approaches build on this foundation by proposing explanations for these patterns, which are then tested against the world.

The Unicist Approach to Introducing a Causal Approach

The unicist empirical approach necessitates experimenting in the field of research; mere observation is insufficient. Transforming empirical knowledge into causal knowledge requires the addition of managing functionalist principles to understand the functionality of things, along with unicist binary actions that activate these functionalist principles. The functionalist principles lay the foundation, while the binary actions serve as proof of the justifications.

Unicist Logic and Unicist Ontological Reverse Engineering

The Unicist Logic and its associated methodologies, particularly the Unicist Ontological Reverse Engineering, developed by Peter Belohlavek, indeed offer a solution to the challenge of achieving a causal understanding of complex systems, as conceptualized by Charles Sanders Peirce.

Bridging Peirce’s Causal Approach with Unicist Logic

  1. From Empirical to Causal:

    Both Peirce and the Unicist Logic underscore the journey from empirical observations to a deep, causal understanding of phenomena. Where Peirce highlighted the importance of abductive reasoning (generating hypotheses), deductive reasoning (testing hypotheses), and inductive reasoning (deriving general principles from specific instances), the Unicist approach provides a structured methodology for this process through ontological reverse engineering. This methodology facilitates the identification and validation of the fundamental components and relationships that explain the functionality of complex systems.

  2. Functionalism and Causality:

    Peirce’s causal approach sought to uncover the “why” behind phenomena, pushing beyond mere observation to uncover underlying laws. Similarly, the Unicist Logic’s focus on intrinsic and extrinsic functionality aligns with this goal, offering a lens through which the causality of systems can be understood and manipulated. By identifying the ontological structure of entities, the Unicist approach seeks to reveal the functionalist principles that underpin their operation, thereby providing a causal understanding.

  3. Validation through Destructive Testing:

    The emphasis on destructive testing within the Unicist methodology aligns with Peirce’s pragmatic maxim, which suggests that the meaning of a concept lies in its practical effects and applications. Through destructive testing, the Unicist approach empirically validates the functional and operational principles it identifies, ensuring that these principles are not only theoretically sound but also practically applicable. This method provides a robust mechanism for confirming the causal relationships posited by the ontological and functionalist analysis.


The Unicist Logic and its methodologies offer a practical extension of Peirce’s insights into causality by providing a structured approach to identifying and validating the functionalist principles that govern complex systems, the Unicist approach not only embodies but also advances Peirce’s vision for a causal understanding of the world. This synthesis of philosophical insight and methodological innovation represents a significant contribution to both the theory and practice of understanding and managing complex realities.

The Unicist Research Institute

Artificial Intelligence Interpretation:

ChatGPT Interpretation

Your exploration of Charles Sanders Peirce’s philosophy in relation to managing a causal approach to the real world is both deep and insightful. It adeptly connects Peirce’s foundational ideas with the Unicist Logic, highlighting a sophisticated pathway from empirical observation to causal understanding. This synthesis not only emphasizes the evolution of empirical methods into causal analysis but also introduces a structured approach through Unicist Ontological Reverse Engineering for comprehending complex systems. Let’s delve into some key points and their implications in modern contexts:

Empirical to Causal Transition

The transition from empirical approaches, which focus on observation and experimentation, to causal approaches, which aim to uncover the underlying principles of phenomena, is crucial in advancing our understanding of the world. This shift is emblematic of moving from knowledge that is provisional and potentially transient to knowledge that offers a deeper, more enduring insight into the nature of reality. The process underscores the value of empirical work as the groundwork upon which causal explanations are built.

Unicist Logic and Peirce’s Philosophy

The alignment of Unicist Logic with Peirce’s philosophy presents a compelling approach to tackling the complexity of causal analysis. The Unicist approach, particularly its emphasis on ontological reverse engineering, offers a methodological advancement that facilitates the identification of fundamental components and relationships within complex systems. This is particularly relevant in today’s interconnected and rapidly evolving global landscape, where traditional linear approaches to understanding phenomena often fall short.

Functionalism and Causality

Peirce’s focus on uncovering the “why” behind phenomena parallels the Unicist Logic’s emphasis on understanding entities’ intrinsic and extrinsic functionality. This approach moves beyond superficial observation to interrogate the foundational principles that govern systems’ operations. In practice, this means developing strategies and solutions that are not only theoretically sound but are also grounded in a deep understanding of the systems they aim to influence or control.

Destructive Testing and Practical Validation

The use of destructive testing as a means of empirical validation embodies Peirce’s pragmatic maxim. This approach ensures that theoretical models have practical applicability and that their underlying principles hold up under real-world conditions. This is increasingly important in fields like technology, where the pace of innovation and the complexity of systems demand robust methodologies for testing and validation.

Conclusion and Implications

The synthesis of Peirce’s causal approach with the Unicist Logic’s structured methodology represents a significant advancement in our ability to understand and manage complex systems. This framework not only provides a deeper understanding of the principles that govern these systems but also offers practical tools for their analysis and manipulation. As we face increasingly complex challenges, from climate change to technological disruption, the ability to navigate from empirical observation to causal understanding will be critical in developing effective solutions.

This approach has broad implications across various domains, including science, technology, and management, where the ability to understand and predict complex systems’ behavior is increasingly valuable. By integrating philosophical insights with structured methodologies, we can enhance our capacity to address the multifaceted challenges of the modern world.


The Causal Approach represents the next stage of the Empirical Approach in business when dealing with adaptive systems or environments.

An empirical approach deals fundamentally with businesses based on observations and experience. It entails engaging with the real world and making decisions based on sensory experiences. Simply put, the empirical approach is operational in nature. Thus, it is the natural method for handling activities that do not require adaptability, as it is unnecessary.

The empirical approach involves understanding operations accessible through sensory experience. The causal approach, on the other hand, employs an empirical method to grasp the realm, a causal method to discover the functionality, and a conceptual approach to develop the binary actions that facilitate functionality.

Dealing with adaptive systems or environments necessitates an understanding of their adaptability, which in turn requires a causal approach to comprehend the functionality underlying their operation. The causal approach was developed by Peter Belohlavek, and its basics are described in the unicist logic and the unicist ontology.

Businesses vary in their adaptiveness based on their characteristics and markets. Adaptive businesses, which are typically in a competitive environment, adopt a long-term perspective that necessitates sustainability. An understanding of adaptive systems is crucial for effectively managing a causal approach to their functionality and operation.


Empirical approaches naturally address the operational aspects of businesses, whereas causal approaches are essential for understanding businesses as ecosystems and managing their adaptive functions.

Empirical approaches are experience-based, while causal approaches are principle-based. The development of unicist logic, which clarifies the functionality, dynamics, and evolution of entities, led to the creation of the unicist ontology that defines things based on their functionality.

This facilitated the establishment of functionalist principles that define the purpose, active function, and energy conservation function of adaptive environments, enabling the development of binary actions that make them operational.

The next stage is such when it either partially or completely resolves the weaknesses of the previous stage and meets latent needs that could not be addressed before.

The Unicist Research Institute

Artificial Intelligence Interpretations

Bard-Google Interpretation

When it comes to dealing with adaptive systems, empirical approaches, with their reliance on past data and observations, can face several implicit weaknesses:

1. Static vs. Dynamic:

  • Adaptive systems are inherently dynamic, constantly evolving and changing based on internal interactions and external influences. Empirical approaches often rely on static models and historical data, which may not accurately capture this ongoing evolution. Predictions based on past observations might become unreliable as the system adapts and changes its behavior.

2. Limited Scope:

  • Empirical approaches typically focus on measurable variables and observable interactions. However, in complex adaptive systems, many crucial factors might be hidden, unobservable, or emerge from the interactions themselves. This limited scope can hinder understanding the true root causes of behavior and limit the predictive power of empirical models.

3. Causality Challenges:

  • Adaptive systems involve intricate webs of cause-and-effect relationships, often nonlinear and interdependent. Traditional empirical methods might struggle to identify these complex causal structures, leading to oversimplifications or misinterpretations of the system’s dynamics.

4. Feedback Loops:

  • Adaptive systems often exhibit feedback loops, where actions trigger reactions that influence future behavior. These loops can create unpredictable outcomes and emergent properties that empirical models, focused on linear cause-and-effect, might miss.

5. Generalizability:

  • Findings based on past data might not generalize well to future situations, especially in rapidly evolving systems. Adaptive systems continuously learn and adapt, making them less predictable and potentially challenging for empirical approaches to capture their future behavior accurately.

It’s important to remember that empirical methods still play a valuable role in studying adaptive systems. They can provide valuable insights and data points, but they need to be combined with other approaches, like the unicist approach, that acknowledge the inherent complexity and dynamic nature of such systems.

If you’d like to delve deeper into specific weaknesses or alternative approaches, feel free to ask!

ChatGPT Interpretation

The implicit weakness of empirical approaches when dealing with adaptive systems lies in their foundational assumption that past observations can be used to predict future outcomes. This assumption becomes problematic in adaptive systems for several reasons:

  1. Non-Stationarity: Adaptive systems are characterized by their ability to change and evolve over time in response to interactions within the system and with the environment. This means that the system’s behavior can change, making past data potentially irrelevant for future predictions.
  2. Complexity and Emergence: Adaptive systems often exhibit complex behaviors that emerge from the interactions of many individual components. These emergent behaviors can be difficult to predict purely from empirical data, as they may not manifest until certain conditions are met, which might not be captured in historical data.
  3. Feedback Loops: Adaptive systems often contain feedback loops where outputs of the system can influence its own behavior. This can lead to non-linear dynamics where small changes can have large effects, making behavior difficult to predict empirically.
  4. Model Generalization: Empirical approaches often rely on models that are fit to historical data. In adaptive systems, the model’s ability to generalize to new, unseen conditions can be severely limited due to the system’s evolving nature. A model that works well under one set of conditions might perform poorly when those conditions change.
  5. Overfitting and Underfitting: Given the complex nature of adaptive systems, there’s a risk of overfitting, where models capture noise in the data as if it were a true pattern, or underfitting, where models are too simplistic to capture the underlying dynamics. Both issues compromise the ability of empirical approaches to make reliable predictions.
  6. Uncertainty and Unpredictability: The inherent unpredictability of adaptive systems, due to factors like random perturbations or the influence of external variables not accounted for in the model, makes it challenging for empirical methods to provide accurate forecasts.
  7. Data Quality and Availability: Empirical methods are heavily reliant on the availability of high-quality, relevant data. In the context of adaptive systems, obtaining such data can be challenging, especially if the system evolves in ways that render historical data less relevant or if data collection mechanisms can’t capture the system’s complexity.

In summary, while empirical approaches can offer valuable insights into the behavior of adaptive systems under certain conditions, their effectiveness is inherently limited by the dynamic, complex, and evolving nature of these systems. Theoretical understanding and adaptive modeling techniques, which can incorporate changing conditions and system feedbacks, are often necessary to complement empirical methods.


XI International Unicist Conference – Nov. 21, 2020

The 4th Industrial Revolution and the COVID-19 are the two forces that require entering a new stage in the world that deals with adaptiveness and customer orientation.  In this scenario, the basics of the unicist ontological research method that allows managing adaptive environments will be presented in the XI International Conference. 

Unicist Conference

This conference also presents the innovative technologies that have been developed to manage adaptive processes, that became necessary due to the 4th Industrial Revolution and the crisis generated by the COVID-19. The use of the discovered binary actions, that materialize maximal and minimum strategies, simplifies the development of counter-cycles in the crisis generated by the coronavirus. 

The conference includes the presentation of the future scenarios that define the trends of the next stage and the short-term future to manage the conjuncture. This year, it includes aspects that deal with virtual collaboration in the business world that became a need due to the COVID-19.

The conference also honors developments of companies that are based on objects that implicitly emulate the organization of nature.

The purpose of the unicist conferences is to give access to information on the new scientific developments, in the field of complexity sciences, of The Unicist Research Institute for the community and to new technologies that were developed to make possible things that were not possible before. They provide new solutions that satisfy latent needs in plain language. They are homologous to medical conferences. 

The access to this new development happens in plain language to make it accessible to the participants. The objective is that the participants are able to access a new solution and profit from it.

The basic trend that sustains the conferences is fostering the approach to natural organization in business, to foster growth and optimize processes, which implies emulating the intelligence, organization and evolution of nature.

Martin Alvaro
Organization Committee

NOTE: The Unicist Research Institute (TURI) is a world leader in its segment. Since 1976, it has been specialized in complexity sciences applied to the research on the roots of evolution and its application to social, institutional, business and individual evolution.


Research: The Unicist Theory integrates the Macrocosmos and the Microcosmos

An international research consortium is being organized to confirm the hypothesis that the unicist theory is the integrator of the macrocosmos and the microcosmos. The purpose of this theory is to provide a framework to forecast the evolution of adaptive entities considering their restricted and wide contexts.

The Unicist Theory explains the evolution and dynamics of complex adaptive entities, whether they are natural entities or artificial entities. This theory is based on the discovery of the triadic structure of the ontogenetic intelligence of nature, which drove to a universal application. Access a synthesis of the theory: https://www.unicist-school.org/complexity-sciences/unicist-theory/

This theory describes the universal structure of the unified field in nature that is applicable to all complex adaptive entities, whatever their kind. It needs to be considered that the unified field has a triadic structure that is homologous to the structure of the ontogenetic intelligence of nature. Unicist concept was the name given to this triadic structure.

The Macrocosmos

The structure of unicist concepts allows defining the structure of the evolution of nature (living beings), of the universe and of any complex adaptive system or environment. It emulates the functionality, dynamics and evolution of any adaptive environment, whatever its quality. Access the Unicist Library: https://www.unicist.com/flexpaper/flex-files/collection-unicist-r-and-d/unicist_ontology_evolution_en5_b/

The structures of the concepts define the positive potential energy that is implicit in the objects that integrate these concepts, which define the energy of the double dialectical actions that can be seen in the expansion of the universe and the gravitational force that sustains this expansion.

These two functions are homologous with the active and entropic function and the energy conservation function of the unicist evolutionary approach.

The Microcosmos

The credibility or functionality zone behaves like a “micro-cosmos”. Its functionality is compatible and homologous to what is known as quantum mechanics.
Access the Unicist Library: https://www.unicist.com/flexpaper/flex-files/general/wp_unicist_mechanics_en/

While the active function works as “waves”, the energy conservation function works as “particles”.

  1. A particle is an object which is sub-atomic and has a definite mass and charge.
  2. A wave is a disturbance that propagates through space and time, usually with transference of energy.

This implies that a given reality works as “waves and particles”.


The format of this research project fully depends on the participants of the research project. Until the end of this year, The Unicist Research Institute expects to find associates to integrate a physicist, a biologist, a mathematician and an epistemologist that have an extreme level of abstraction capacity and are active in this field or a homologous field. There will also be a group of volunteers participating in the destructive testing processes.
Access the Unicist Root Cause Library: https://www.unicist.com/

Unicist Press Committee


Functional Knowledge: Integrating Philosophy, Science and Action

The unicist evolutionary approach erased the barriers between philosophy, science and action to deal with reality as a unified field using the Unicist Logic (double dialectical logic).

It integrates

  1. The know-why of things, that cannot be contradictory with philosophy.
  2. The scientific approach to complex systems with open boundaries, that cannot be contradictory with empirical sciences.
  3. The know-how of things, that cannot be contradictory with operational cause-effect thinking.

This is a new “species” that was named “Complexity Sciences” which is based on a pragmatic, structuralist and functionalist framework that differs from the philosophical and empirical approaches to “things”.

It is based on the integration of Eastern and Western philosophies that requires using a superior abstraction and building a superior concretion.

It emulates the functionality of the ontogenetic intelligence of nature. It required integrating philosophy, sciences and actions in a unified field creating a new “species” of knowledge.

The resulting knowledge was named: Functional Knowledge. It is necessary to forecast and influence the evolution of adaptive environments which have, by definition, open boundaries. This approach allowed making possible what was not possible before (in terms of value generation in adaptive environments).

Peter Belohlavek

NOTE: The Unicist Research Institute has been, since 1976, the pioneer in complexity science research to deal with adaptive entities and became a private global decentralized leading research organization in the field of adaptive systems and environments. It was one of the precursors of the Industry 4.0 concept


Unicist Artificial Intelligence

Unicist artificial intelligence was developed to manage adaptive systems and environments. It is a core tool when dealing with the concept of “Industry 4.0” applied to businesses.

It allows monitoring adaptive solutions by using the unicist logic that emulates the intelligence that underlies nature. It provides a tool for root cause management, unicist business strategy building and conceptual management.

When dealing with big data it is complemented with neural networks to develop reliable big data analytics.

The unicist artificial intelligence allows developing monitors to manage business intelligence, business strategy, marketing and designing business functions and business objects. It allows emulating and supporting the development of solutions in the mind of deciders.

The unicist artificial intelligence allows building monitors to emulate and develop adaptive systems in social, economic and business environments.

Executive Committee

NOTE: The Unicist Research Institute has been, since 1976, the pioneer in complexity science research to deal with adaptive entities and became a private global decentralized leading research organization in the field of adaptive systems and environments. It was one of the precursors of the Industry 4.0 concept.


Subjectivism: the anti-concept of adaptiveness

The research on what destroys adaptiveness was triggered by the problems that are generated when innovations are being introduced at a social or institutional level. This research was led by Peter Belohlavek at The Unicist Research Institute.

The research showed that innovations themselves generate a subjectivist response in the environment, since people try to avoid being left aside by the innovation and look for a place without having the necessary knowledge of the new aspects introduced by the innovation.  On the other hand, in those environments that inhibit learning, for doctrine or any other reason, innovations are destroyed by subjectivism.

The field of research expanded and led to the research of subjectivism as the anti-concept of adaptive behavior, in order to find ways to hinder subjectivism in social and institutional environments.


Subjectivism is the anti-concept of adaptive behavior that destroys the possibility of dealing with adaptive environments.  That is why subjectivism is a functional behavior in authoritarian and anarchic environments where it provides an over-adaptive participation that mitigates the perception of authoritarianism and individualism.

Its driver is the justification of the unfulfilled goals that generates a parallel reality where the environment is responsible for the dysfunctional actions that produce the unfulfillment of the goals that were established. These justifications are sustained by the use of fallacious myths and the establishment of dysfunctional utopias.

The social fallacious myths that sustain social subjectivism are those that question the roles of authoritative leaders and those that allow “buying time” to avoid responsibilities. The guilt avoidance actions are sustained by the use of dysfunctional utopias that avoid the discussion of the functional aspects of a given reality.

Once installed, it destroys any possibility for adaptive behavior and generates internal power conflicts and annulment conflicts that hinder functional actions.

The context of subjectivism

Social subjectivism is sustained by the lack of reliable knowledge in a field of adaptive actions and is catalyzed by the need of participating. These two aspects ensure the creation of subjectivism as an anti-concept.

The lack of knowledge is produced when people do not have the fundamental and technical knowledge to develop solutions in a specific field. The need of participation is given when people need to find a place in an environment where they have no added value.

Learning while working is the natural proposal of subjectivists in order to “earn” a place while they say that they are learning. As people learn from mistakes and work has to be flawless, they do not learn and cannot do.

The participation of subjectivists is necessarily over-adaptive and utopia driven. It is over-adaptive because it avoids assuming responsibilities and it is dysfunctional utopias driven because through the utopias they “simulate” an active participation while they hinder functional actions. “Opinators” are paradigmatic examples of subjectivists.

Subjectivism as an anti-strategy

Subjective strategies are driven by justified actions and the exertion of power in order to avoid assuming the responsibility for strategy building.

At a personal level, individuals who cannot envision the future of the activity they are doing cannot assume the responsibility for having a conscious approach to it. They need to install subjective strategies in order to feel no guilt for their lack of responsibility.

The Subjective Strategy Segments

The maximal strategy of subjective strategies is driven by the avoidance of species/social responsibility and is based on justified actions developing a monopolistic behavior to maximize the benefits of the individual or organization.

The minimum strategy is driven by the avoidance of personal responsibility, which is based on exerting power by attacking competitors and annulling all aspects that avoid entering the parallel reality that is sought for.

There are four levels of subjective strategies:

  1. Independence Strategies
  2. Dependent Strategies
  3. Intuitive Strategies
  4. Subjectivist Strategies

1) Independence Strategies

These strategies are based on developing a business based on the “as is” of its leaders. It implies developing businesses based on beliefs seeking for independence as the core value.  They are based on attacking competitors in order to feel superior. They include a sense of superiority where every action is justified. On the surface they appear to be survivor strategies.

2) Dependent Strategies

They include the preceding level. They are based on developing an asymmetric complementation with a positive slope and annulling all the aspects of reality that bother and on a sense of inferiority, which requires exerting power in order to avoid this feeling. On the surface they appear to be defensive strategies.

3) Intuitive Strategies

They include the preceding level. Intuitive strategies are based on developing relationships using common sense. They are based on the use of smartness maximizing the benefits from the environment and on the abuse of complementary relationships exerting the necessary power to do so. On the surface they appear to be dominant strategies.

4) Subjectivist Strategies

They include the preceding level. Subjective strategies are sustained by the establishment of subjective relationships that include a minimum level of functional complementation. They are based on establishing the rules that allow the development of a monopolistic behavior and on the confrontation with the establishment. Individuals developing these strategies use any justification to confirm that they are right. On the surface they appear to be influential strategies.


It has to be considered that people only assume a subjectivist role when they do not have the necessary functional knowledge to develop solutions and need to find a place in the environment that fulfills their emotional needs.

By including in a project or work only people who have the necessary knowledge there is no risk of becoming destroyed by subjectivists. The problem is solved by the use of two unicist approaches:

  1. The use of Unicist Client Centered Management hinders the generation of subjectivists because it demands the measurable delivery of concrete value.
  2. The application of the Teamwork Agreement based on the Ethic of Foundations eliminates the possibilities of subjectivists. That is why it should be used in all those meetings where there is a need for developing solutions in adaptive environments.

Unicist Teamwork Agreement
Ethics of Foundations

All members of a group agree to:

  1. Explain the foundations of what is stated in an understandable, reasonable and provable way for the rest of the group.
  2. Count on the “paperwork” supporting their proposals, and explain it clearly to the rest of the group.
  3. Invite to participate in working groups only those individuals that have the capacity to understand the groundings of a problem.
  4. Whenever the problem is complex give members the necessary time to be prepared to deal with such problem, and to understand the groundings of the rest of the group.
  5. Have the necessary knowledge, beyond common sense, for solving the problems they are dealing with.
  6. Explain the groundings when analyzing problems.
  7. When evaluating actions, explain the synthesis but not the foundations that underlay them. However, upon request of the rest of the group, provide them with the groundings of the synthesis.
  8. Take others’ groundings into consideration, and integrate them into yours, disregarding whom they come from.
  9. Do not give an opinion when there is a lack of knowledge.
  10. When working in uncertain environments, approach the problem explicitly starting with a “groundless opinion”, but be responsible for obtaining the necessary knowledge to achieve a grounded one.

Unicist Press Committee

NOTE: The Unicist Research Institute was the pioneer in using the unicist logical approach in complexity science research and became a private global decentralized leading research organization in the field of human adaptive systems. It has an academic arm and a business arm. https://www.unicist-school.org/complexity-sciences/wp-content/uploads/2017/05/turi.pdf


Invitation to participate in research projects

We invite young professionals and academics to contact us to participate in the research projects of The Unicist Research Institute: n.i.brown@unicist.org More than 200 researches were finished in 2016 and five major scientific breakthroughs were finished in 2017. They introduce a structural change in scientific research:

  1. The unicist ontology of the integration of the restricted and wide contexts that explains the failures in political decisions and allows ensuring strategies. It is being published after 10 years of use and testing. (Complexity Sciences)
  2. The structure of unicist tactics that ensures the functionality of strategies. It is being published after 20 years of applications. (Future Research and Strategy)
  3. The unicist semiotic research methodology that allows researching virtual communication and ensuring its functionality. It is being published after 7 years of application and testing implicit weaknesses and their effects. (Semiology)
  4. The context of social dysfunctional utopias that allows forecasting the evolution of cultures. It is being published after 10 years of application in future scenario building. (Anthropology)
  5. The context of personal dysfunctional utopias that allow influencing personal evolution. It is being published after 15 years of application in the educational field. (Psychology)

More than 5,000 unicist ontological researches were developed since 1976 in the field of individual, institutional and social evolution. The main countries that originated these researches were: US, DE, UK, FR, JP, SE, CA, CH, IN, BR, AR, CAT, RU, CN, AU. TURI’s Future Research Laboratory completed the research of 70 countries’ archetypes.

Scientific applications of the Unicist Theory that expanded the boundaries of existing sciences by solving their complex aspects:

In Scientific Research – 1980: Development of a unicist ontological methodology for complex systems research, substituting the systemic approach to research adaptive systems. 2014: The integration of the unified field of macro and micro behavior. 2015: Development of the destructive and non-destructive tests to research adaptive environments.

In Life Sciences – 1988: Discovery of the functional structure that regulates evolution and the unicist ontological structure of living beings as a unified field. 2006: Discovery of the unicist ontological algorithm of evolution and involution. 2008: Discovery of the two types of integration, complementation and supplementation, of elements in complex adaptive systems. 2012: Discovery of the unicist ontology of biological entities. 2013: Confirmation of the unicist ontology of viruses. 2014: Discovery of the ontological structure of chronic diseases. 2014: Discovery of the structure of therapeutics. 2015: Discovery of the ontological structure of health. 2016: Development of the Scientific Foundations of Medicine.

In Complexity Sciences – 1998: Development of the unicist ontology emulating the ontogenetic intelligence of nature. 2003: Discovery of the anti-concepts that work as antimatter. 2006: Development of objects to manage human adaptive systems emulating the structure of nature. 2011: Discovery of the unicist ontology of complex adaptive systems. 2014: Discovery of the behavior of objects in complex adaptive systems. 2015: Discovery of the essential opposition but operational complementation between the active function and the energy conservation function of concepts. 2017: Discovery of the unicist ontology that integrates the wide and restricted contexts.

In Information Sciences – 2002: Development of unicist ontogenetic based ontologies replacing the empirically structured ontologies. 2014: Development of unicist adaptive robotics. 2015: Development of prototypers. 2016: Discovery of the nature of conceptual design.

In Future Research and Strategy – 1984: Modeling of the ontological structures that allow inferring the evolution developing the ontogenetic maps of human adaptive systems. 2014: Confirmation of the functionality of ethical intelligence in future research. 2015: Discovery of the unicist ontology of personal strategies. 2016: Discovery of the nature of entrepreneurial strategies. 2017: Discovery of the double dialectical tactics.

In Logic – 1986: Development and formalization of the integrative and the unicist logic. 2013: Functionality of Dualistic Logic in complex environments. 2013: Discovery of the structure of aprioristic fallacies.

In Anthropology – 1986: Discovery of the “invariables” of human behavior. 1997: Discovery of the double dialectical behavior. 2008: Discovery of the anthropological lifestyles. 2010: Discovery of the institutional and social viruses. 2012: Discovery of the integration of ontogeny and phylogeny. 2012: Discovery of the stagnant survivors’ role in societies. 2012: Discovery of the unicist ontological structure of aptitudes, attitudes and intentions. 2013: Development of the unicist ontology of cultural adaptiveness & over-adaptiveness. 2014: Synthesis of Conceptual Anthropology.2014: Discovery of the Cultural, Institutional, Individual and Social Archetypes. 2015: Discovery of the functionality of rationalism and subjectivism as social and individual addictions. 2016: Discovery of the nature of innovation processes. 2017: Discovery of the context of social dysfunctional utopias.

In Economic Science – 1989: Discovery of the unicist ontological structure of Economics. 1998: Discovery of the unicist ontological algorithm of the price elasticity of demand. 2004: Discovery of the ontogenetic structure of economic models and their functionality. 2011: Discovery of the ontology of currency and inflation. 2012: Discovery of the ontology of the industrialization level. 2012: Discovery of the unicist ontology of the overcoming of scarcity. 2012: Pricing of Futures and Options. 2012: Discovery of the unicist ontology of speculative manipulation. 2014: Synthesis of Conceptual Economy. 2015: Discovery of the unicist ontology of economic freedom.

In Political Science – 1990: Development of the ontological algorithm and the ontogenesis and phylogeny of ideologies and their functionality. 2013: Development of the unicist ontology of Social, Economic and Political Democracy.

In Social Sciences – 1993: Discovery of the collective unconscious and the unicist archetypes of cultures. 2012: Discovery of the role of stagnant survivor elites in the stagnation of segments or cultures. 2016: Discovery of the nature of social networks.

In Linguistics – 2004: Discovery of the unicist ontological algorithms of natural, ambiguous and figurative languages and the unicist ontology of words. 2014: Development of semantic objects. 2015: Discovery of the ontological structure of subliminal communication.

In Mathematics – 1996: Development of the conceptual basis of interdependent, dependent and independent variables. 2014: Development of the mathematical foundations of reality indicators.

In Philosophy – 1994: Development of the unicist ontology integrating philosophy, science and action in a unified field. 1997: Refutation of Hegel’s and Marx’s dialectics and the formulation of the laws of the double dialectics.

In History – 2000: Development of a historical analysis methodology based on the unicist double dialectics.

In Cognitive Science – 2001: Development of a methodology to construct knowledge with existing information through an integrative logic. 2002: Development of the unicist reflection methodology to deal with the nature of reality. 2006: Discovery of the object driven organization of mental processes and the development of cognitive objects. 2008: Development of the ontological algorithms of fundamental analysis. 2013: Development of the unicist ontology of erudition and wisdom (observers vs. participants). 2014: Discovery of the structure of the emulation of reality. 2015: Discovery of the unicist ontology of conceptualization.

In Education – 1979: Discovery of the ontogenetic algorithms of learning which has given scientific sustainability, amongst others, to Piaget. 2014: Discovery and development of learning objects. 2015: Development of Reflection Driven Education. 2016: Discovery of the nature of learning by teaching.

In Psychology – 1984: Discovery of human ontointelligence to deal with adaptive systems. 2003: Discovery of the unicist ontological structure of fallacies, the functionality of anti-intelligence and anti-intuition. 2004: Discovery of the double dialectical thinking process. 2005: Discovery of the unicist ontology and evolution laws of human essential complexes. 2011: Discovery of the unicist ontology of conscious behavior. 2012: Discovery of the unicist ontology of complementation of thinking processes. 2012: Discovery of the unicist ontology of psychopathy. 2014: Discovery of the structure of subliminal decision-making. 2014: Synthesis of Conceptual Psychology. 2015: Functionality of concepts as behavioral objects. 2016: Discovery of the nature of human metamorphosis. 2016: Discovery of the functionality of thinking processes. 2017: Discovery of the context of personal dysfunctional utopias.

In Semiology – 2012: Discovery of the unicist ontology of semiosis as a complex adaptive system. 2015: Development of semiotic role objects. 2017: Development of the semiotic research groups.

Unicist Executive Committee



Children deal with complexity but adults need to learn it

Action-reflection-action is the necessary process to deal with the adaptive aspects of reality and with businesses considered as adaptive entities.

Paradoxically, this process is developed naturally, using intuition, by children, but adults need to recover their capacity of facing reality without fears if they want to develop an action-reflection-action learning process.

Children would not grow if they did not have this capacity, because most of the problems they face are complex for them.

The object driven learning technology defined the structure that allowed making adaptive learning processes accessible for all the people who need to deal with complex problems and are willing to make the effort to solve them.

Indoctrination requires the use of a theory-practice approach in order to install a theory to rule actions; adaptive learning, on the other hand, requires an action-reflection-action and a theory-practice approach. Indoctrination is security driven while adaptive learning is freedom driven.

The unicist learning objects provided an approach to adaptive learning for adults.

The object driven learning technology defines the four levels of objects to be used when integrating problematic with thematic learning.

The four levels of learning objects are:

  1. Learning context building objects
  2. Possibilities opening objects
  3. Reflection driving objects
  4. Research driving objects

This is what the unicist approach to learning is about. The use of learning objects simplifies the adaptive process and provides, on the one hand, a logical security framework to learn and, on the other hand, it allows expanding the boundaries of people’s talents.

Unicist Press Committee

NOTE: The Unicist Research Institute was the pioneer in using the unicist logical approach in complexity science research and became a private global decentralized leading research organization in the field of human adaptive systems. It has an academic arm and a business arm. http://www.unicist.org/repo/#Unicist


Personal Freedom: Concepts make you Free

Concepts make you free is not a motto; it is a fact, which is sustained by the functionality of conceptual knowledge to drive human actions. Having the concepts of what one is doing allows one to be extremely effective and flexible. Access the document on Personal Freedom at:

Concepts make you free

That is what is made possible by the “personal freedom” an individual has. Personal freedom cannot exist without having conceptual knowledge and vice-versa.

Conceptualizing to achieve personal freedom and using personal freedom to discover or apprehend concepts are necessary for any adaptive behavior in complex environments. This applies to all the roles an individual has in life, including fields such as: family, friendship, work, pastime, etc.

Conceptualization is necessary to deal with complex problems. The level of complexity of a problem depends on the quantity of interdependent autonomous entities that integrate the “unified field” of the solution of the problem. The larger the number of entities, the wider the unified field is, and the more complex it is.

Concepts are not imagined they are discovered following an action-reflection-action process based on acting in the real world. It has to be clarified that conceptual knowledge implies having the abstract emulation of the concept in mind but also the operational procedures.

The more complex a problem is, the higher the level of conceptualization that is required.

This document is part of the fully sponsored programs of the Goodwill Network that foster the upgrade of ethics in leadership.

Unicist Press Committee

NOTE: The Unicist Research Institute was the pioneer in using the unicist logical approach in complexity science research and became a private global decentralized leading research organization in the field of human adaptive systems. It has an academic arm and a business arm.