The development of the UPI became possible due to the rules of the Unicist Double Dialectical Logic that allow emulating the organization of nature.
This allowed inferring the evolution of all those aspects where the fundamentals of behavior have been discovered and modeled.
Fundamentals define and describe the drivers of the actions of a specific entity in nature.
Predictive interfaces allow developing reliable confirmations of diagnoses and predictions of the evolution of the environment where the fundamentals are known.
The building of predictive interfaces is upgraded based on new discoveries of fundamentals that allow expanding the application field using the same generic inference rules.
The UPI uses predictors that represent on the surface the behavior of the fundamentals of a given aspect of reality. The existence of predictors makes the management of fundamentals possible.
The Unicist Predictive Interface is an upgrade of the preexisting Blue Eagle X-pert system whose first version “STRAT” was developed in 1985.
Since then it has been used to develop predictions of validity of diagnoses, future scenarios and strategies in order to be able to build adequate adaptive processes to produce results.
The UPI is developed as part of R&D Labs in organizations to provide the necessary adaptiveness to deal with human complex adaptive processes. Each specific application is patented in order to protect its intellectual property.
The Use of Predictors
Predictors are signs that can be read to anticipate the future.
Predictors are observable events that make the fundamentals of specific aspects of reality observable.
The fundamentals of a specific reality are able to define a concept if there is a catalyst or a gravitational force that is influencing it.
Everyone uses predictors to interpret actions.
For example, a smile is a predictor of what can be expected. Non-verbal communication necessarily includes the observation of “predicting signs” in order to act or react.
The rational use of predictors requires being aware of the structure of fundamentals that rule a given reality and the external forces of the restricted and wide contexts that influence it.
It is necessary to use predictors to deal with complex adaptive aspects of reality.
The unicist algorithms and the unicist ontogenetic maps provide the structure of predictors to be observed and measured to anticipate the future in order to react or exert influence to make things happen.
The Unicist Logical Approach to Adaptive Business Processes
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.
The development of this technology became possible because of the discovery of the unicist laws of evolution, the object driven organization that emulates the organization of nature and the drivers of human behavior that allow designing the necessary peopleware.
Before the existence of adaptive systems the solution to adaptiveness was fully focused on the efficacy of individuals which increased their responsibility. This forced them to consider all the details of the feedback from the environment which increased the probability of errors.
The catalyst of an adaptive IT system is its capacity to learn from the feedback to improve its structural adaptive behavior. The entropy inhibitor of the system is given by its capacity to learn to ensure conjunctural adaptiveness.
The Unicist Logical Inference Rules
The rules are based on the Unicist Theory of Evolution and the Unicist Double Dialectical Logic and allow emulating the structure of nature in a particular reality and forecasting its evolution when the ontogenetic maps and fundamentals are known.
- Law of the Unified Field – Stable Oneness
- Law of Value Adding – Stable Expansion
- Law of Value Earning – Stable Contraction
- Law of Actions – Stable Freedom Growth
- Law of Energy Conservation – Stable Security Growth
- Law of Dominance – Gravitational Effects
- Law of Influence – Catalytical Effects
- Law of Energy Saving – Entropy Inhibiting Effects
- Law of Complementation – Complementary Integration
- Law of Supplementation – Supplementary Integration
- Law of Cooperation – Expansive Cooperation
- Law of Competition – Stable Competition
- Law of Mutation – Structural Mutation
- Law of Inertia – Structural Conservation
- Law of the Functionality Zone – Possibilities to Influence
- Law of Probability to Influence – Probabilities to Influence
The Architecture and Design of Adaptive Systems for Work
Businesses are typical adaptive systems. We use the word “business” as a synonym of “work”. Businesses need to adapt to the environment in order to achieve the permanence of their transcendent goals and the personal goals of their participants.
Work is an environment that generates the need of adaptive systems in order to produce results and administrative systems to use and control the methods used.
The maximal strategy implies achieving or overcoming the planned results using procedures with the necessary flexibility and controlling them based on the feedback of the environment.
The minimum strategy is based on using strict methods that use rigid procedures and intrinsic control systems based on accepted standards.
Adaptive IT solutions are systems that have been designed to interact with the external and internal environment. Nowadays, there are multiple programming solutions available to sustain the unicist adaptive architecture. The solutions include aspect objects architecture with unicist logical inferences to manage adaptiveness.
Adaptive solutions are the next step in business management
The integration of the unicist logical approach and the resulting business objects included in the business processes within an adaptive IT environment allows driving business processes to the next level.
To be able to organize by objects it is necessary to use both adaptive and administrative systems to organize the work processes. This widened the possibilities of companies to expand the boundaries of their activities within an environment of assured quality.
The level of adaptiveness varies according to the needs of the system. There are four levels of adaptiveness that can be managed:
1) Analogical drivers
This level is based on the recognition of the significant behavioral patterns of the segments of an environment.
2) Rules based drivers
This level is based on the use of analogical patterns and the logical rules defined by the fundamentals that influence the business processes.
3) Empirical drivers
This level is based on the previous level and the integration of mathematical models to infer behavior based on the observable aspects of the fundamentals that participate in the business processes.
4) Objects based drivers
This level is based on the previous level plus the use of objects that work as interdependent drivers to influence the attitude of the participants of the business processes. The feedback of the environment is defined by the results produced by these objects.
Implementation adaptive systems requires following a step by step approach. Most of the people need to learn to interact with the business environment instead of trying to push it.
The Building of a Unicist Predictive Interface
The building of a UPI to introduce adaptiveness into information systems is based on having the necessary knowledge objects base that contains the specific knowledge of a domain and the Unicist Double Dialectical Logic to relate the different aspects of the objects to predict the behavior of such domain. The building of a UPI requires an R&D process using the unicist methodology to deal with complex adaptive systems.
Predictive interfaces are always focused on predicting specific problems in order to confirm diagnoses that have been made or anticipate changes in the environment.
They need to be built with a team of experts that have the knowledge and the authoritative role to sustain the validity of the results provided by the system. Teamwork is based on an R&D process that shortens significantly the confirmation process of the validity of the functionality of the predictive system.
This R&D process is based on the use of a prototyper that contains the generic rules that need to be adapted to the different aspects of the problem that is being dealt with.
The prototyper works, from the first day on, as a predictive inference engine after the hypothetical knowledge objects of the domain have been introduced.
The next step implies the development of the destructive tests to confirm the limits of validity of the knowledge introduced.
After the destructive tests have been done, it is necessary to develop the non-destructive tests which imply the use of the predictions as parallel information, comparing them with the actual functionality.
The prototype has been finished when the non-destructive tests have shown the reliability of the predictions.
The next step is to transform the prototype into an optimized predictive inference engine that uses the information of the specific knowledge base to provide the information necessary to feed the adaptive system’s interface.
Pilot testing is a core aspect of an R&D Lab. It provides the information of the validity of the solutions and the results that can be expected when using the solutions. There are two different type of tests:
- Destructive tests are the first tests that need to be developed to find the limits of the validity of a hypothesis. They require expanding the use of the hypothesis until it fails. It requires dealing with the structure of the fundamentals of the problems.
- Non-destructive tests imply using the information of the destructive tests and applying it to the segments that have been defined, measuring the feedback and making the final changes to confirm the effectiveness of the actions.
From then on the system is expanded by the learning process which is based on the feedback from the users.