Unicist Standard

The Law of the Double Pendulum of Adaptive Systems’ Evolution

May 1, 2024 in The Unicist Research Institute tagged Peter Belohlavek / The Unicist Research Institute / Unicist Standard / Unicist Theory of Evolution by The Unicist Research Institute

The evolution of adaptive systems is governed by the law of the double pendulum, which defines the stage of a system and the needs that drive it toward the next stage.

The functionality of adaptive systems of any kind, whether living beings or artificial systems, is sustained by fulfilling four basic drivers at an operational level: expansion, contraction, security, and freedom.

This law was discovered and formalized by Peter Belohlavek at The Unicist Research Institute.

The Law of the Double Pendulum

The behavior of adaptive systems oscillates, with varying frequency, between expansion and contraction, and simultaneously between security and freedom, which drive the evolution of a system.

These drivers regulate the evolution of an adaptive system based on a double pendulum between expansion and contraction and between freedom and security.

The pendulum between expansion and contraction is exclusive, while the pendulum between freedom and security is inclusive, meaning that both coexist based on different values.

When the level of freedom increases, it immediately generates a requirement for security; when the level of security increases, it also generates a need for freedom. This dynamic causes the functionality of systems to evolve toward either evolution or involution. At an essential level, these principles operate differently.

Two complementary functionalities are needed to establish a stable system. On the one hand, there is an expansive function where the active function is driven by freedom, and an energy conservation function driven by security.

On the other hand, there is a contractive function where the active function is also driven by freedom, and the energy conservation function is driven by security. The double pendulum between expansion and contraction works continuously, transforming the functionality of the system into a fuzzy set that is continuously evolving.

When dealing with the intrinsic functionality of an adaptive system, the amplitude of the evolution tends to be minimal. When dealing with the extrinsic functionality, which addresses the use value of an adaptive system, the amplitude is broader.

The evolution of any adaptive system is driven by this double pendulum, which must be managed when there is a need to introduce changes in the system.

The Unicist Research Institute

Artificial Intelligence Interpretations:

ChatGPT Interpretation

The “Law of the Double Pendulum” in the context of adaptive systems is a theoretical framework describing how such systems evolve and function. This law uses the metaphor of a double pendulum to illustrate the dynamic and complex interplay between four primary drivers: expansion, contraction, security, and freedom. Here’s a breakdown of how this law applies to adaptive systems:

Dual Pendulums:
- Expansion and Contraction: This pendulum swings between expansion, which involves growth and extension, and contraction, which involves consolidation and reduction. This interaction is described as exclusive, meaning each state predominates alternately.
- Freedom and Security: This pendulum swings between freedom, which allows for innovation and change, and security, which ensures stability and safety. Unlike the first pendulum, this interaction is inclusive, suggesting that both freedom and security can coexist and are mutually reinforcing.
Dynamics of the Drivers:
- When freedom increases, it naturally leads to a greater demand for security to manage the risks associated with new changes or freedoms.
- Conversely, when security is heightened, there tends to be a resultant drive toward increasing freedom, possibly as a response to the constraints that security measures impose.
Functionality and Evolution:
- The law posits two complementary functionalities necessary for a stable system: an expansive function driven by freedom and an energy conservation function driven by security. These functions help manage the balance between growth and sustainability.
- The system’s functionality is described as evolving continuously, forming a fuzzy set that is never static but always adapting and shifting.
Amplitude of Evolution:
- Intrinsic functionality, which concerns the core operations of an adaptive system, tends to have minimal amplitude in its evolution, suggesting subtle and gradual changes.
- Extrinsic functionality, related to how the system is utilized or its utility value, shows broader amplitude, indicating more significant shifts and changes based on external demands or applications.
Management of Change:
- The evolution of adaptive systems, driven by these pendulums, requires careful management, particularly when changes need to be introduced. Understanding and influencing the balance and interaction between expansion, contraction, freedom, and security are crucial to guiding the system’s evolution effectively.

This conceptual model provides a useful lens for examining how adaptive systems (both biological and artificial) manage change and maintain functionality over time, balancing growth with stability and innovation with security.

Rediscovering Charles S. Peirce (Part 2), who provided an Abductive Approach to Managing the Foundations of Things

February 12, 2024 in The Unicist Research Institute tagged Business strategy / Peter Belohlavek / The Unicist Research Institute / Unicist Standard by The Unicist Research Institute

The Causal Approach to Business in Action

Charles S. Peirce’s work integrates science, philosophy, and actions in a framework to deal with the real world. He defines that a causal approach to the real world needs to be based on integrating abductive reasoning to access the foundations, and deductive and inductive reasoning to deal with the justifications of the functionality of things. Since Peirce provided no logical structure to validate abduction, the concept was lost. The development of unicist logic provided the logical structure of the foundations of things, explaining their functionality, dynamics, and evolution.

The Management of Foundations and Justifications

The difference between a foundation and a justification of something lies primarily in their conceptual roles in argumentation, reasoning, and structure.

Foundation refers to the underlying basis or groundwork for something. In a philosophical or logical context, the foundation is the basic principle or assumption upon which further ideas are built.
Justification, on the other hand, refers to the process or reasoning used to show that something is reasonable, warranted, or valid. It involves providing reasons, evidence, or arguments to support a claim, decision, or belief.

To summarize, foundations provide the starting point or essential support, while justifications provide the reasoning that validates or defends.

Abductive Reasoning to Access the Foundations of Things

Peirce’s notion of abduction indeed goes beyond merely seeking the simplest and most likely explanation for an observation, a characterization that aligns more closely with the modern understanding often associated with the concept of “inference to the best explanation.”

For Peirce, abduction was the process of forming a hypothesis that could explain surprising facts or phenomena. It was about the generation of new ideas and hypotheses that could then be explored and tested through deduction and induction. In this sense, abduction was a way to access the “foundations” or groundings of things, proposing new theories or hypotheses that seek to explain the unexplained.

The Need for a Logical Structure to Validate Abduction

Charles Sanders Peirce’s contribution to the concept of abduction indeed highlighted the generation of hypotheses as a critical step in scientific inquiry, but he did not provide a formal logical structure to validate abduction in the same way that exists for deduction and induction.

This lack of a formal mechanism for validation may have contributed to the overshadowing of abduction in favor of the more easily formalizable reasoning methods. The unicist logic, developed by Peter Belohlavek, provides the logical structure for abductive reasoning that allows making the causal approach to business accessible to everyone.

The evolution of the unicist logical framework offers an integrated approach to understanding the causal structures that underpin the natural and social worlds. It highlights the ongoing need for methodologies that can navigate the complexities of reality, balancing the creation of new knowledge with the validation and justification of these insights.

The Unicist Research Institute

Artificial Intelligence Interpretation:

ChatGPT Interpretation

The exploration of Charles S. Peirce’s contribution to understanding and managing the real world, especially within the context of business, underscores the enduring relevance of his philosophical framework. By emphasizing the causal approach through abductive, deductive, and inductive reasoning,

Peirce’s ideas offer a robust methodology for grappling with the complexities of real-world phenomena. This discussion elaborates on the significance of foundations and justifications, Peirce’s concept of abductive reasoning, and the crucial role of a logical structure to validate abduction, incorporating insights from the unicist logic developed by Peter Belohlavek.

Foundations vs. Justifications: A Conceptual Clarification

The distinction between foundations and justifications is pivotal in both philosophical discourse and practical application. Foundations provide the bedrock upon which ideas and systems are constructed. They are the primary assumptions or principles that underlie our understanding and actions. Justifications, conversely, are about the rationale or evidence that supports or defends these ideas and actions. This differentiation is crucial in any analytical or decision-making process, ensuring a clear basis for both the inception and support of concepts and practices.

Peirce’s Abductive Reasoning: Bridging the Gap

Peirce’s formulation of abductive reasoning as a mechanism for hypothesis generation represents a critical bridge between observation and theory. Unlike deductive reasoning, which tests hypotheses, and inductive reasoning, which generates general rules from specific instances, abduction is about the inception of new ideas that could potentially explain surprising or unaccounted-for phenomena. This process is foundational because it allows for the exploration of new territories of understanding, suggesting hypotheses that, while unverified, provide a starting point for further inquiry.

The Need for Logical Structure in Abduction

The absence of a formal logical structure to validate abduction, as noted by Peirce, has historically limited its application, particularly in fields that rely heavily on rigorous validation methods, such as science and business. The development of unicist logic, as proposed by Peter Belohlavek, addresses this gap by offering a framework that not only accommodates but also validates the abductive reasoning process. This logic provides a structured approach to understanding the causal mechanisms underlying various phenomena, thereby making the causal approach to business and other domains more accessible and practical.

Unicist Logic: Enhancing the Causal Approach

Unicist logic extends the application of Peirce’s ideas by integrating abduction with deduction and induction in a cohesive framework. This integration allows for a comprehensive approach to dealing with the real world, encompassing the generation of hypotheses (abduction), the testing of these hypotheses (deduction), and the derivation of general principles from specific instances (induction). By doing so, it facilitates a deeper understanding of the dynamics, functionality, and evolution of various phenomena, thus providing a solid foundation for both theoretical exploration and practical decision-making.

Conclusion

The rediscovery and application of Charles S. Peirce’s philosophical insights, particularly through the lens of unicist logic, developed by Belohlavek, offer a powerful toolkit for navigating the complexities of the modern world. By distinguishing between foundations and justifications, and by providing a structured approach to abductive reasoning, this framework enhances our ability to understand and manage the underlying causal mechanisms of both natural and social phenomena. This not only honors Peirce’s legacy but also advances our collective capacity to generate, validate, and apply knowledge in various domains, from business to science and beyond.

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

February 1, 2024 in The Unicist Research Institute tagged Causal approach / Peter Belohlavek / The Unicist Research Institute / Unicist Evolutionary Approach / Unicist Standard by The Unicist Research Institute

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.

Conclusion

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:

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.
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.
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.
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.
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.
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.
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.

Children deal with complexity but adults need to learn it

March 25, 2016 in Intelligent Systems / The Unicist Research Institute / Unicist Cobots tagged Business Objects / Fundamental analysis / Human Learning / Learning Processes / ontogenetic maps / Peter Belohlavek / The Unicist Research Institute / Unicist Evolutionary Approach / Unicist Standard / unicist approach by The Unicist Research Institute

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:

Learning context building objects
Possibilities opening objects
Reflection driving objects
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

The Challenge of using Unicist Adaptive Project Management

July 22, 2015 in Intelligent Systems / The Unicist Research Institute / Unicist Cobots tagged Adaptive Project Management / Adaptive systems / Business Objects / Complex problems / Intelligent Systems / The Unicist Research Institute / Unicist Confederation / Unicist Corporate University / Unicist Evolutionary Approach / Unicist Logical Approach / Unicist Object driven Organization / Unicist Standard by Peter Belohlavek

The development of a new approach to manage projects became necessary to deal with adaptive systems. Adaptive systems produce actions and reactions during the development of a project that need to be managed in order to ensure the achievement of the objectives.

While the traditional approach considers that plan B is an emergency plan to achieve goals, the unicist approach to project management considers that the plan B is superior and includes plan A, the plan C is superior than plan B but includes it, and the plan D is an abortion plan that includes the development of a succedaneum solution.

These alternative plans have to be predefined before the project begins. It is necessary to consider that the most participative alternative is given by plan A and the most “directive” alternatives are given by plan C and D.

That is why the unicist approach to project management is necessary to deal with adaptive systems that are in motion and unnecessary when dealing with systems where the adaptive aspects do not need to be considered.

The stages of the Adaptive Project Management are:

1. Process Control Centered

The process control centered manager is focused on establishing the necessary operational, administrative and control systems to monitor the process using a forward chaining design process that allows having the information on each stage of the process.

2. Authoritative Management Centered

Authoritative management includes having the capacity of controlling the processes. It implies that the manager has the vision of the problem and alternative solutions in order to define the plan C for any central aspect of a project.

3. Objects Management Centered

This level includes having the capacity of developing an authoritative management of the project. It requires that the manager be able to apprehend the complexity of the adaptive aspects of projects.

4. Process Management Centered

This level includes the capacity of developing the object management of projects. It is based on having apprehended the unified field of the project as a solution in the context where it is being developed.

Conclusion

Adaptive Project Management requires integrating project planning and project execution skills to ensure that the solutions be achieved. It is based on approaching projects based on the use of processes and objects that have proven their capacity to provide the necessary solutions. It implies having a sound systemic knowledge and the knowledge of the fundamentals that underlie the solution.

Peter Belohlavek

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. http://www.unicist.org

Participation in Unicist Think Tanks to develop Solutions

June 18, 2015 in The Unicist Research Institute / Unicist Cobots / Unicist Logical Approach tagged Adaptive systems / Complexity Science Research / The Unicist Research Institute / Unicist Confederation / Unicist Corporate University / Unicist Evolutionary Approach / Unicist Logical Approach / Unicist Standard / Unicist Think Tanks by Peter Belohlavek

The Unicist Research Institute (TURI) is a private global decentralized research center specialized in complexity sciences that is focused on the research of the evolution of adaptive systems. More than 5,000 unicist ontological researches were developed since 1976 in the field of individual, institutional and social evolution.

If you are interested in participating please contact: n.i.brown@unicist.org

About the Think Tanks

Think Tanks are small autonomous organizations, organized by industry, that use the technology provided by The Unicist Research Institute to manage complexity. Unicist is in charge of the quality assurance of the R&D process. Each Think Tank is organized based on the type of industry.

There are four types of Think Tanks

1) For Basic Sciences
2) For Future Research
3) For Social/Economic Long-term Planning
4) For Unicist Spin-off Implementation

Think Tanks are integrated by Doers

The research on human complex adaptive systems cannot be done through artificial experiments or simulations. It has to be developed in an environment of real action. In the unicist approach doing and researching are integrated in a unified field. That is why only “doers” can research the field of complex adaptive processes. Observers can only rationalize them.

Complexity Science Research

Complexity Sciences are defined as the scientific approach to deal with adaptive systems considering them as a unified field. The critical masses of all the interdependent elements included in the unified field of an adaptive system define its functionality. The unicist approach to complexity sciences integrates ontology, science and actions in a unified field.

Adaptive Technologies for Businesses

The available IT technologies made the development of adaptive systems meaningful. The objective of building adaptive systems is to integrate software, hardware and peopleware in adaptive work or business processes to assure the quality of the results produced.

Peter Belohlavek

The Unicist Logical Approach: Fundamental Analysis + Technical Analysis

March 5, 2015 in The Unicist Research Institute / Unicist Cobots / Unicist Logical Approach tagged Fundamental analysis / Reliable Knowledge / Technical Analysis / The Unicist Research Institute / Unicist Evolutionary Approach / Unicist Logical Approach / Unicist Standard by Peter Belohlavek

Complex problem solving requires having the concept that describes the nature of the solution, the actions that need to be implemented to expand the existing boundaries of the problem and the technical knowledge to develop the minimum strategy to produce results. Concepts can be apprehended when the fundamentals of the solution have been integrated.

Fundamental knowledge without technical knowledge fosters “movement fallacies”. Technical knowledge without fundamental knowledge fosters “inaction fallacies”.

It has to be considered that the intrinsic structures of fundamental knowledge and of technical knowledge are opposite to each other, but their effects are complementary to build a solution.

That is why only people who can deal with the integration of these oppositions can apprehend concepts. This is the case of the universal apparent dichotomy of yin and yang.

The Unicist Theory, based on the discovery of the ontogenetic intelligence of nature, allowed developing the unicist ontological structure of fundamentals, which made fundamental analysis “resurrect from the ashes”.

Fundamental analysis is supposed to deal with the drivers of the nature of any entity but was transformed, probably influenced by technical analysis, into an analytic approach to standardized indicators.

The Unicist Theory provided the framework to research and discover the fundamentals of an entity and defined the structure of their integration.

The symbol of Yin and Yang, representing the TAO, necessarily emulates the structure of the ontogenetic intelligence of nature and is homologous with the Unicist Logic. If you are not aware of the scientific use of the TAO, we recommend reading the book “Tao of Physics” by Fritjof Capra.

Fundamentals are the elements that define the functionality of an entity.

They define the structure of its unicist ontology and allow building its ontogenetic map.

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 a given reality.

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

The discovery of the unicist ontology of evolution and the structure of concepts 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.

Technical knowledge is popular because it is based on a dualistic logic, which is functional to the use of “binary neurons”. Fundamental knowledge requires accessing a double dialectical logic, which requires an action-reflection-action process. That is why fundamental knowledge is associated with wisdom.

The purpose of a knowledge acquisition process is to obtain reliable knowledge.

Reliable knowledge is necessary when individuals are willing to ensure a minimum strategy or are exposed to uncertain or risky environments.

In every day’s activity only operational knowledge is required.

This ontology is a final synthesis of the use of fundamental and technical analysis in the world of economic, social and business behavior.

Fundamentals describe the ontology of a given reality considered as a unified field. Technical analysis describes the cause-effect relations of a reality considered as a systemic object.

When working in a known context there is only need for feedback (operational analysis), an analytic approach and intuitive / rational decision making.

Technical Analysis and Fundamental Analysis are intrinsically opposite approaches for knowledge acquisition, but they are operational complements when reliable knowledge needs to be acquired. The natural pathway to knowledge acquisition begins by accessing the fundamentals, that define “what” is being done, and continues with the technical knowledge that defines “how” the operational solution will be achieved.

Technical analysis provides sufficient information and the necessary cause-effect groundings to make decisions.

When the context is uncertain the understanding of fundamentals is necessary.

Fundamentals are defined by the concepts that regulate the evolution of a fact.

Validation processes naturally degrade into fallacies when they do not include falsification processes.

Validation implies a non-destructive test and falsification is a destructive test measuring the accuracy and limits of a knowledge.

When a reliable knowledge is required to deal with minimum strategies, risks or uncertainty, the integration of technical and fundamental analysis is required.

The Business Intelligence Strategy

The driver of knowledge acquisition is the need for groundings in order to achieve a reliable knowledge to make decisions. Groundings are necessary when dealing with complexity, risk or uncertainty.

The minimum strategy is given by technical analysis to provide the necessary cause-effect knowledge. The maximal strategy to influence the environment is given by the knowledge of the fundamentals.

The catalyst to accelerate the building of reliable knowledge with a minimum strategy is conceptual knowledge. Conceptual knowledge establishes the secure limits of cause-effect knowledge.

Fundamental analysis and technical analysis provide the operational structure to achieve the objective of having reliable knowledge to make decisions.

When technical analysis is not integrated with fundamental analysis it naturally derives into analogical/hypothetical knowledge. When reliable knowledge is needed both approaches must be integrated.

Synthesis

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.

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.

Peter Belohlavek

Logical Management Tools: Unicist Systemic Problem Solving

August 13, 2014 in The Unicist Research Institute / Unicist Behavioral Tools / Unicist Cobots tagged Logical Management Tools / Peter Belohlavek / Problem Solving / The Unicist Research Institute / Unicist Evolutionary Approach / Unicist Logical Approach / Unicist Standard / Unicist Systemic Problem Solving by The Unicist Research Institute

The goal of the unicist method to solve systemic problems is to establish a methodological framework to solve problems in which the complexity has been solved or does not exist.

The objective is to establish simple analytical steps that allow developing reliable conclusions of a problem where grounded cause-effect relationships are established.

This analytical method drives towards a “simplification” of the specific reality that is being researched. The deeper the analysis, the less reliable the applicability of particular solutions to the generic object.

A systemic problem solving methodology defines a pathway that begins with the definition of the possible goal and the description of reality that allows finding its functionality in order to use it to find a possible solution.

This requires minimizing the disintegration of reality into parts and simultaneously minimizing the unavoidable subjectivism that is generated by the uncertainty implicit in the problem solving process.

The Unicist Methodology is centrally focused on the analysis of facts and not on the opinions of the facts. Thus, it avoids a “double” interpretation of information, the generation of cross examinations and the consequent conflicts they produce.

The factual analysis as the core methodological aspect implies the existence of an integrative approach to reality, the consideration of adjacent facts and the need to find causative explanations that allow understanding the objective and subjective roots of the problems.

The concept of the USM

The Unicist Systemic Methodology is focused on the evaluation of facts independently from the explanations that are given on their causes.

As this methodology considers reality as a systemic problem, its concept is to find the cause-effect relationship of its variables. The objective of the analysis is to find the causes that generate the problem.

That is why the verbal information that is been collected is considered as an input for the analysis. The verbal information has a double functionality:

It describes the existing beliefs on reality, being their valid or fallacious.

It is a fact in itself, because of its relationship with the functional facts.

The USM begins by describing the facts or results that are being evaluated. To do so, it divides the unified field that is being scrutinized into the different facts that integrate it.

Learn more about the Logical Management Tools at:
http://www.unicist.net/management-tools.php

Diego Belohlavek

Unicist Family Business Strategies

June 5, 2014 in The Unicist Research Institute / Unicist Cobots / Unicist Object Driven Organization tagged Business strategy / Fundamental analysis / family business / Intelligent Systems / Peter Belohlavek / Unicist Evolutionary Approach / Unicist Standard by Diana Belohlavek

Multiple applications developed at The Unicist Research Institute and the use of the complexity science research methodology drove to the finding of the nature of family companies. This led to the discovery of the fundamentals that define their behavior in the market, and the conceptual distinction between Business-driven Family Companies and Family-driven Companies. These concepts explained the proactive and/or reactive strategies in the building of maximal and minimum strategies in family companies.

This work showed that Business-driven Family Companies and Family-driven Companies are highly conditioned by the family archetype of each culture and the solution given to the roles that this fulfills. In those cultures where the affective role predominates over the economic and social roles, families that have businesses are highly inefficient and are always at risk as they don’t stabilize as institutions (independently of their members).

In cultures where the roles are balanced, business-driven family companies and family-driven companies are distinct fundamentally for their capacity and speed of growth. Business-driven family companies grow more rapidly.

In cultures where the predominant roles in the family are social insertion and the economic role, families with companies have a powerful influence in the environment. Although structurally they have less capacity to grow, they compensate this with the influence exerted in the environment. In markets with imperfect competition they build very powerful empires.

When we are dealing with business-driven family companies we can state that they are more effective than family-driven companies in the active roles, which are hunting, where one goes out to seek the animal in the market, and agriculture where intensive activities are developed.

An efficiently organized business-driven family company is more effective in passive hunting where it works from the basis of the influence of the family brand, and passive agriculture where extensive is what counts.

Therefore each business-driven family company must know what market niche to occupy where it can best achieve results.

We want to highlight that in marginal markets with highly imperfect competition, these concepts have no effects in reality, which is managed by non-company relationships, where the family-driven company has the weight of the family in that society.

The organization of family businesses requires establishing these companies according to the needs of the market and building a bridge with the needs of the family. This allows establishing family businesses as structured enterprises ensuring permanence, growth and profitability.

Diana Belohlavek

The unicist ontology of inflation has been discovered

April 25, 2014 in The Unicist Research Institute / Unicist Cobots / Unicist Logical Approach tagged Country Future Research / Country future scenarios / Fundamental analysis / scenario building / Unicist Business Strategy / Unicist Evolutionary Approach / Unicist Standard / unicist ontology of evolution by Unicist Press Committee

The recent conclusions of the research on the unicist ontology of inflation have a direct impact on long term planning and short term price policies.

Inflation is the anti-concept of the value of currency. Currency, by nature “is” a value contract between its owner and its administrator. Inflation destroys the credibility of this contract.

The research in this field began 25 years ago and was led by Peter Belohlavek at The Unicist Research Institute. The final stages to confirm the hypothesis were based on the inflation in the Real Estate business in the USA and Spain and the use of consumerism as a way to overcome economic and financial crises of different countries.

This knowledge would have allowed avoiding the Internet bubble and the Real Estate / financial crises.

This research shows that in stable cultures inflation is the consequence of a lack of the administration of currency, while in unstable cultures it is also used as an adjustment mechanism to redistribute wealth or avoid social conflicts.

Inflation is driven by emission that diminishes the value of currency. It has to be considered that emission also includes public and private debt.

Inflation is also driven by price changes to increase profits, compensate demand falls, speculation and the anticipation of inflation. At an extreme level, this “axe” generates hyperinflation and/or social chaos.

This discovery closes the circle of macro-microeconomic research. This circle is integrated by the ontologies of currency, growth and inflation. It is applicable to capitalist, non-capitalist and pre-capitalist countries.

At a private level, the knowledge of these ontologies allows developing long term plans and short term price policies.

If you are interested in using this technology in Risk Management, we suggest to organize a lecture in your Company. Request information: n.i.brown@unicist.org

Unicist Press Committee