Integration as scientific method
The title of this page could also be: A scientific method for philosophy, psychology, sociology and other Alpha Sciences or Plausible Science
It has become a tradition to consider the exact physical experimental scientific method, including experiments, mathematical deductions and falsification as some of its core ingredients, as the only reliable paradigm to check the validity of scientific hypotheses. The fact that some very important sciences, indispensable to take decisions in daily life, i.e. the so called Alpha Sciences (philosophy, psychology, sociology, art, economics, religion, politics, etc.), were inaccessible for this method, didn't bother too much science philosophers. "Just wait till we discover some exact tools to measure those phenomena. It's just a question of time." This promise is now repeated since nearly three centuries, and still the alpha sciences remain in the realm of unscientific, uncontrollable, irrational, mythical, obscure, implausible thinking, where the number of theories equals the number of theorists.
This article proposes a novel approach, considering that classical science is only one of at least two methods to control the plausibility and reliability of scientific hypotheses. The second method is described. Some historical research yields evidence that this method in fact exists since a long time, from the Renaissance scientists to Kant and Whitehead, and that it even "invented" the modern scientific method. And, most probably, the brain itself functions that way.
Confronted with life's experiences, man constantly tries to "understand" things happening around him: which factors contribute to it, to which extend they do so, and how these factors could be influenced to achieve our goals? Even at brain level, spontaneous abstractions and analogies are elaborated, and hypotheses are induced, at different levels of abstraction.
The fundamental question is: how can we be sure that these hypotheses, on which we trust to take action, are reliable, plausible, "exact"? This is the purpose of science: to control the spontaneously induced hypotheses.
The thinking process consists of four steps, often cyclically repeated:
- making concrete observations;
- induction, formulating general hypotheses out of a number of concrete observations;
- deduction, formulating concrete applications starting with hypotheses and combining them with data;
- checking if the conclusions / predictions of the deduction(s) comply with observed reality.
Science is an intellectual method to control, i.e. to prove or to falsify, hypotheses that where spontaneously formulated by a subconscious and spontaneous process, called induction.
It is important to note that, up to now, all the hypotheses, including these that made exact science so useful and impressive, were spontaneous and subconscious, i.e. highly uncontrollable and largely unpredictable. Even Einstein could not explain how he "discovered" his creative theories.
Exact science is a scientific method where the validity of a hypothesis is controlled or falsified (i.e. proved or refuted) by making the controllable steps of thinking as reliable as possible:
- observations are to be made with exact measurements, repeated to make sure general tendencies are measured, by controlling the fluctuating margins of the measurements;
- inductions -- were and still are uncontrollable and unpredictable
- deductions are to be made with correct mathematical computations, and using reliable former theories
- checking by controlling the predictions, and making experiments, i.e. varying each aknowledged factor ("variable").
By its nature, exact science is limited to fields of reality that are directly observable, exactly measurable, and open for manipulation and experiment. Practically speaking, exact science is limited to a range of reality, going from level 3 (elements of atoms) to level 8 (animals and plants) (see Evolution Scheme). Levels 1, 2 and 9 (the latter including the so called Alpha Sciences) stay out of the application field of exact science.
One could say that exact science controls the deduction.
Plausible science is a scientific method where the validity (plausibility) of a hypothesis is controlled by making induction more plausible and controllable, i.e. as reliable as possible
- observations -- are most often limited to incidental activities;
- inductions are enhanced by inspiration techniques and rendered plausible by combination of the maximal number of spontaneous hypotheses;
- deductions have to be logic, i.e. checked against a growing corpus of coherent hypotheses. Opportunities for mathematical deductions are rare.
- checking occurs by applying the new insights. The opportunities for experiments and experimental variations are limited.
One could say that plausible science controls the induction.
Some fundamental concepts underpin the practical procedure:
- The brain helps us, at a preconscious and intuitive level, to spontaneously formulate hypotheses, that aim at explaining the influencing factors that led to the reality we experience. This process of induction is not yet fully explained. So, computers can't make inductions, and are not creative.
- Due to the fact that each experience is subjective, i.e. a confrontation limited to certain aspects and some states of the influencing factors, the intuitive hypothesis will have a limited relevance: missing some nuances, and making some overgeneralizations. We call these exaggerations: eductions. Newton observed the movements of objects that were relatively big, at a relatively low speed. Hence his mechanics were exact at those speeds and sizes, but inexact at higher speeds and other sizes. Einstein, with the observations of higher speeds and little size at his disposal, was able to correct Newton's formulas, adding factors which were reduced to nil at 'Newtonian' circumstances.
- As a consequence it is more correct to state that (nearly) every hypothesis, formulated by (experienced) humans, has somewhere a kernel (essence) of truth, than to state, as Aristotle and Descartes did, that each hypothesis is either true or false. A common misconception is to confuse reality with the model of reality. Within the model, each statement is exactly true or false, because the model is a conscious reduction of reality to measurable factors. Outside the model nearly all hypotheses are an approximation of reality, so they are nearly never completely false or completely true.
- Conflicts between hypotheses referring to the same phenomena are, most probably, not proofs of incorrect observations or false reasoning, but consequences of educed interpretations based upon observations in a limited context.
- The more intuitive hypotheses you combine from different sources, i.e. different experiential backgrounds, the more plausibility such a hypothesis will gather, on the condition that the essence of every contributing hypothesis is kept.
- The plausibility of a hypothesis increases with the number of complementary hypotheses it shares, but also with the degree of compliance with the existing and ever expanding coherent corpus of already existing hypotheses. This intellectual process is called integration.
- We agree that exact science should be preferable to plausible science. But it is senseless and, definitely, dangerous to wait another century or two to start applying a reliable scientific method to such important fields of human thinking.
The procedure of Plausible Science
- Gather all available hypotheses or theories about a studied phenomenon. Also exact hypotheses, i.e. hypotheses accepted and proved by traditional exact science, are to be taken into consideration, if available. But already with 2 apparently divergent approaches useful integrative work can be performed.
- Elaborate a logical scheme or classification of all available material. Use therefore one of the logical schemes, available in a list of logical schemes (coming soon). If no available scheme seems to be appropriate, propose a new one, often starting from an existing one. The elaboration of logical schemes is an important part of the development of plausible science.
- Often at this point, of at other moments, splitting up the material into several coherent entities will seem indicated. The ideal logical entity or page consists of a definition, referrals to more general processes of systems, a description of possible states or variants and their influencing factors and possible actions to cope with it, and a list of applications
- Refining definitions will often be necessary. It will be the first step in the process of retroduction, i.e. retrieving the essence from educed, inexact theories.
- Longer lists (more than 3 items) of topics, applications, aspects, instances, etc., have to be restructured into more exact subdivisions. The intuition that a new subdivision seems desirable, enables us to discover new factors and distinguish hidden nuances
- Over-generalization occurs by a lack of contrasting experience. As no "exception" for the observation is available, the factor which is sensible for a possible variation is not observable, and in this vacuum an over-generalization of the hypothesis easily occurs. Differentiation of observation, and contrasting experience, is perhaps the most valuable factor in creativity.
- Likewise, over-concretization occurs by lack of inspiration, or lack of contrasting experience. The person honestly thinks his needs or intentions can only be realized by this precise activity.
- Very often several "variants" are developmental stages of one central process
- Often analogies are discovered, and point to more general processes, interactions and systems, conducting us ultimately to the General Systems Theory.
- The integrative theory has to be congruent with the continuously growing corpus of Integrative Theories in that field. Very often, this corpus has to be refined with distinctions not earlier made.
- The plausibility of the new, integrative theory is supported by (1) its congruence with the yet existing corpus of integrative theories, and (2) by the fact that the contributing, non-integrative theories can be deduced from the integrative, by reducing or fixing certain nuances and variables. The (3) intuitive (dis)approval by persons, earlier convinced of the exactness of the contributions, can be inspiring.
- This integration cycle probably can be repeated several times, also later at distant moments, when new insights become available.
Differences between exact and plausible science
- All the Principles of Logic, from Socrates to Descartes and Leibniz, in fact are Principles of Deductive Logic. An Inductive Logic is still to be elaborated. This integrative process can be seen as the kernel of such a new paradigm.
- Exact science seems to aim at exact truth, plausible science only at the most probable approximation. But considering the number of times "exact" theories had to be reformulated in the course of time (with its flagship, Newtons's mechanics, as the most dramatic instance), plausibility seems perhaps a more realistic epithet than the presumptuous "exact".
- In both cases, the original formulation of a proposed hypothesis is made by subconscious, intuitive and largely uncontrollable induction.
- In fact, both scientific approaches try to justify theories, by referring them to a maximum of available data. At some levels of reality exact measurements and repeated experiments are possible, enabling the control to be limited to a restricted number of observations, on the condition that this measurements are very precise and exact. If they are less exact, a broader view on applicability becomes necessary, including a referral to a corpus of insights.
- In exact sciences, scientific publications can be limited to a few pages, proposing a mathematically sound hypothesis based upon extremely exact observations. In plausible sciences, publications have to be very extended, considering several contributing theories and referring to the Corpus of integrative theories. Short publications are scientifically only acceptable if they exactly fit within a larger frame. Internet is the ideal working domain for integrative thinking, by its possibilities of quick interaction and easy editing, at least at the level of Web 3.0
- For exact scientists, it's an advantage to know more and more about highly specialized fields. For plausible sciences, it's of course an advantage to know much about very different theoretical fields, inspiring to analogies. The Homo Universalis can be considered as the most appropriate plausible scientist.
- During the last century, academic faculties of Alpha Sciences, in a desperate effort to meet the scientific criteria morally imposed by exact sciences, started to evolve towards the same hyperspecialization, considering it as a quality label. We can, most probably, expect much resistance from the academic world against the scientific method of integration and plausibility.
- It seems unacceptable, at this moment in history, that theories in the Aplha Sciences, and decisions based upon these theories are still made without integrative, plausibility control. Without integration, theories from non-exact scientific fields have the same plausibility as uncontrolled, wild phantasies.
One could perhaps consider that those "primitive", "unscientific" forms of thinking, from ancient Egypt and Babylon through Greece and Alexandria, were in fact kinds of intuitive integrative thinking. The most important condition for integrative thinking, i.e. a general knowledge of multiple fields of human experience, was fulfilled. The ancient philosophers, unless nowadays scientists, appeared to be experienced in many diverse "sciences", ranging from mathematics to architecture and the art of war making, from music to medicine, passing through law, politics and astronomy, all in one person. The notion of homo universalis, i.e. someone who knows "everything", was a high ranking qualification until the 17th and even the 18th century. Blaise Pascal (1623-62) is reknown as the "last" homo universalis.
Kant and Hegel formulated their Thesis - Antithesis - Synthesis principle, an unmistakable intuitive hint towards integrative science.
Pierre Teilhard de Chardin and Alfred North Whitehead strongly advocated diversified backgrounds and inspiration fields. Teilhard, the theologist who became scientific paleontologist and philosopher, and Whitehead, the mathematician who became philosopher (the first lesson of philosophy he attended was the one given by himself). They can be seen as modern homines universales.
Also Ken Wilber, with his concept of Integral Thinking, comes close to integrative science, although he just creates an "integral context", enabling integrative thinking by approaching reality from 4 viewpoints (interior/exterior, individual/social) without introducing the integration procedure.
One of the most frustrating and nevertheless exciting facts about the brain, is that it doesn't work as we logically suppose. Already the "phrenological theory" which supposed that the brain cortex had feelings and idea types. Next naive image became famous
But even a century later, the most intellectual functions were unable to be located. Even functions, considered important and essential, including memory, will, calculator, imagination, escaped to every tentative location. It is obvious that the brain uses different functional and logical categories than we can imagine, and that we have described in logic and computer science.
Some biological necessities make a computer-like brain useless:
- every day, some hundreds to thousands brain cells die, on the most unpredictable locations. Nevertheless, normal functioning nearly never seems suffering from that
- from the first days of life on, decisions have to be made in real time, without any certainty about the rightness and value of available hypotheses, and missing many useful data
- active memories that aren't rehearsed, tend to disappear in days. Dreaming and daydreaming are the most useful rehearsals. Passive memories (the faculty of recognizing) remain months to years.
- exact observations are nearly impossible to make.
- thinking is very inaccurate. Errors are routine.
- the brain has even difficulties to make the distinction between real observations and mental images
The riches of the brain is its ability to make associations, i.e. putting concepts together that aren't identical. Strictly speaking, associations and abstract categories holding non-identical but similar images and concepts, are forms of erroneous thinking. So is the spontaneous generation of hypotheses: a vague analogy with causal links we already know.
To compensate these inaccuracies, the brain constantly uses a it firmly possesses: making still more associations, as vague as the former. In fact, every observation and problem to resolve induces a number of possible interpretations. These are then compared, and the most plausible common aspects are considered as the most probable. All this in a fraction of a second.
As this analogy suggests:
Test your brain: try to calculate by heart 678 * 789. I suppose this is nearly impossible for your brain. But if I say you that the answer is 0.1 or 567.890.123 you will immediately reply that my suggestions are silly. May I ask you, why are you so presumptuous to refute my suggestions, if you even don't know the right answer yourself? The explanation is simple: your brain, totally unable to make relatively simple calculations (therefore you had to memorize as a little child the results of calculations with numbers from 1 to 10), is very good in making, in fractions of seconds, a multitude of associations, and compare them, culminating in a rather impressive approximation of the good result.
These considerations are very suggestive that the brain doesn't function with exact scientific data, but with rather vague associations pointing together towards the most plausible answer.