Figure C5e. Present and New Project Paradigm Model
Kuhn Cycle
The Steps of the Kuhn Cycle Understanding incorporates the five main steps:
Pre-science: the field has no workable paradigm to guide its work successfully.
Normal Science: the standard step, where the field has a scientifically based model of understanding (a paradigm) that works.
Model Drift: the model of understanding starts to drift due to the accumulation of anomalies, which the model cannot explain.
Model Crisis: The Model Drift becomes so excessive the model is broken. It can no longer serve as a reliable guide to problem-solving. Attempts to patch the model up to make it work fail. The field is in anguish.
Model Revolution: this begins when severe candidates for a new model emerge. It's a revolution because the new model radically differs from the old one.
Paradigm Change: a single new paradigm emerges, and the field changes from old to new. When this step ends, the new paradigm becomes the new Normal Science, and the Kuhn Cycle is complete.
Normal Science
Normal Science here means a Normal present status of project management operations that are different anywhere a project is under preparation and implementation stages.
The general project management model exists, is operational, and valuable (like Normal Science).
Model drift
Model Drift: the model of understanding starts to drift due to the accumulation of anomalies, which the model cannot explain.
The Model drift clarifies the cycle and allows the reuse of the Model Drift concept in the System Improvement Process.
Kuhn hypothesizes that enormous progress comes from revolutionary breakthroughs typical for the life sciences.
Details:
Model Drift here describes the understanding and the accumulation of anomalies. It brings situations that the model cannot explain. Therefore, a step is incorporated to clarify the cycle and reuse in the improvement processes.
Model Drift (also known as model decay) is the degradation of a model's prediction power due to environmental changes.
Thus, the relationships between variables (model performance degradation due to data differences and input and output variables). For example, one variable in a degradation stage can change the behavior processes in any organization or project. The data drift can break cycles, corrupt their data, and reveal new opportunities for data use.
Model Crises
Model Drift becomes so excessive the model is broken. It can no longer serve as a reliable guide to problem-solving. Attempts to patch the model up to make it work fail. The field is in anguish.
Details:
Model Crisis is the third step of the Kuhn Cycle. In this step, a model of understanding the project management activities has drifted so far that the field is thrown into crisis. The reason is simple.
The number of projects is growing, data around projects lack order, and there is no demand to open a new data dimension for the project's scope standardization, harmonization of the project's timing, and the project cost stabilization.
Project management is in this environment shattered by the inputs of too many anomalies that the traditional central theory cannot explain.
At this point in the cycle, the current project management practices are awaiting a new model that works.
But here lies the key to success. Scholars, environmentalists, politicians, funders, etc., must grasp this opportunity (challenge) and start to solve this problem of the Model Crisis step. It is a complex task. Its impact is the world's "social control model," a collection of rules describing how a unit of society works.
The most common social control model is used by families, the most basic social unit. Once a social control model is perfected and can be used repeatedly.
Therefore, the project management solvers should thus do what scientists do once they realize they're in the Model Crisis step.
They should stop working on problems and shift their attention to fundamental research. They should try to fathom why the model is broken and fix it.
Model Revolution
Model Revolution: this begins when severe candidates for a new model emerge. It's a revolution because the new model radically differs from the old one.
Details:
Model Revolution: in Science, the shift in professional commitments to shared assumptions occurs when an anomaly "subverts the existing tradition of scientific practice."
These shifts are what Kuhn describes as scientific revolutions, when "the tradition-shattering complements the tradition-bound activity of normal science."
Kuhn made several claims concerning the progress of scientific knowledge: that scientific fields undergo periodic "paradigm shifts" rather than solely progressing linearly and continuously.
These paradigm shifts open new approaches to understanding what scientists would never have considered valid.
Perhaps the best example of such a paradigm shift in Science is the Copernican revolution in cosmology: the move from a geocentric to the heliocentric view of our solar system.
The following examples are:
The first and second world wars.
The introduction wheel, later automobile, into society.
Democracy for the future coexistence of people in the Great Triad.
Revolutions are always "good" to the people on the revolting side and "bad" to those who aren't. What does it mean for project management? At the heart of this question is what digital transformation means to us.
How will digitization change project preparation and implementation, and how will supply chains and synergies between projects and organizations change once the digital transformation is complete?
Paradigm Change (Shift)*
Paradigm Change: a single new paradigm emerges, and the field changes from old to new. When this step ends, the new paradigm becomes the new Normal Science, and the Kuhn Cycle is complete.
* Change has a more significant effect than a simple shift. A shift is still the same subject, just slightly altered. "Change" means that the issue has become a new subject (to a discussion on Wikipedia).
Details:
A paradigm shift, a concept identified by the American physicist and philosopher Thomas Kuhn, is a fundamental change in a scientific discipline's basic concepts and experimental practices and penetrates any applications of real-life situations.
Thomas Kuhn altered the way we looked at the philosophy behind Science and introduced the much-abused phrase 'paradigm shift. His book, The Structure of Scientific Revolutions, has offered a new view on synthetic thinking and structured communication.
The realization that scientific research is data-rather than theory-driven began to gain ground. As such, the advancement of human understanding in the sciences through radical new theories has been coined by Thomas Kuhn as a "paradigm shift."
Examples of such paradigm shifts include the theories of relativity and evolution. Below are some basic already published statements of paradigm:
Thomas Kuhn defines a scientific paradigm as "universally recognized scientific achievements that, for a time, provide model problems and solutions for a community of practitioners, i.e., what is to be observed and scrutinized."
A paradigm is a global organizing model or theory with great explanatory power. An immature science is pre paradigmatic -- that is, it is still in its natural history phase of competing schools. Slowly, Science matures and becomes paradigmatic.
A paradigm shift is defined as an essential change that happens when a new and different way replaces the usual way of thinking about or doing something.
A paradigm shift is a fundamental change in a scientific discipline's basic concepts and experimental practices.
In a personal sense, a paradigm shift defines a fundamental change in how you see the world. Changing a unique paradigm opens a range of new lifelines for your personal and professional growth, presenting you with more opportunities than before.
Shifting a paradigm won't happen overnight.
These are the basic published observations on the meaning of the "paradigm," which changed the scientific discipline's basic concepts and experimental procedures fifty years ago.
Digital transformation and its expected effects are comparable to the paradigms we know or are already beginning to understand (for example, the results of evolution, cognition of the universe, or the theory of relativity on practical life).
This webbook seeks to find the position of projects in the digital transformation that has already begun and has its specific place in low-income territories (provinces).
All new fields begin in Pre-science, (present organization or project management) where they have begun to focus on a problem area but are not yet capable of solving it or making major advances.
Efforts to provide a model of understanding that works eventually bear fruit. The field can, at last, make major progress on its central problems.
It puts a lot in the Normal Science (which tends to stay longer than any other step. Over time the field digs so deep into its area of interest it discovers new questions its current model of understanding cannot answer. As more of these anomalies ("violations of expectations") appear, the model grows weaker.
This is the Model Drift step. If enough unsolved anomalies appear and the model cannot be patched up to explain them, the Model Crisis step is reached. Here the model is obviously no longer capable of solving the field's current problems of interest.
It's a crisis because decisions can no longer be made rationally (e.g., we need a new computing environment). The new input and outputs models must be rebuilt and scaled, and data structures enter new algorithmic operations.
We can't rely on guesswork and intuition. It is not the best solution because such paths tend to fail. Finally, one or more viable candidates emerge from such a struggle with an excellent result.
This begins the Model Revolution step. It's a revolution because the new model is a new paradigm. It's radically different from the old paradigm, so different the two are incommensurate.
Each uses its own rules to judge the other. Thus, believers of each paradigm cannot communicate well. This causes paradigm change resistance.
Once a single new paradigm is settled on by a few influential supporters, the Paradigm Change step begins. Here the field transitions from the old to the new paradigm while improving the new paradigm to maturity.
Eventually, the old paradigm is sufficiently replaced and becomes the field's new Normal Science. The cycle begins all over again because our knowledge about the world is never complete.
It is a strong call for an open discussion on new project management (based on, for example, Blockchain, a smart contract) in a new technical (technology) and social (economy) environment of low-income provinces.
Transferring logical procedures from science to practical and widely used operations of daily life is a tempting task. Thrives on whole segments, on models that only roughly describe what we are looking for in this solution.
The reason is that we still work with small amounts of data. But new technologies indicate the path of further development. For example, current weather forecasting technologies work with data from all territories. So far, there is no such thing for the area of projects.
The problem is not that there is little project data, but how to work with the data (how to capture, analyze, sync, standardize and constantly check their validity and usefulness for day-to-day operations.
Project-related data are generated in many fields, in different socio-cultural environments, with other preconditions for their origin, use, and archiving.
Data has strong relations to historical practices and the impacts of new technologies on current generations in individual localities (in urban, peri-urban, and rural areas).