Winning in the Age of Bohonomics

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Big Science and Big Innovations: An Adventure between Holism and Reductionism

As we know reductionism is the view that the behavior of a system can be explained by understanding its components. Reductionism is the basis of how any researchers starts to solve a complex problem. S/he divides the problem into sub-problems, analyzes them, and finds their solutions and may divide them to sub-sub problems for more simplification. And so progress of science continues. Mostly, this is the basis of the western science. When reductionist model is used as an explanation it depends on an analogy between the components of the model and the components of the system. The analogy is between the components of the model and the parts of the model. For example, Descartes proposed an idea that non-human animals could be reductively explained as automata.

Reductionism can mean either (1) an approach to understanding the nature of complex things by reducing them to the interactions of their parts, or to simpler or more fundamental things. Reductionism can also mean (2) a philosophical position that a complex system is nothing but the sum of its parts, and that an account of it can be reduced to accounts of individual constituents. Methodological reductionism is the strong position that the best scientific strategy is to attempt to reduce explanations to the smallest possible entities. Thus, according to methodological reductionism, all scientific theories either can or should be reduced to a single super-theory through the process of theoretical reduction.

Reductionists do not view that systems somehow function as wholes and that their functioning cannot be fully understood solely in terms of their component parts. They believe that sub-systems of the whole system do not have any problematic functioning. Reductionism in science means that a complex system can be explained by reduction to its fundamental parts.

Holism is another view to understand systems. The idea of holism was broadly presented by Jan Smuts, the famous military leader and a philosopher, but the principle of holism was concisely summarized by Aristotle in the Metaphysics: “The whole is more than the sum of its parts”. Holism is the view that some phenomena that appear at the system level do not exist at the level of components, and cannot be explained in component-level term. Thus, holistic models are more focused on similarities between systems and less interested in analogous parts. A holistic modeling approach to modeling often consists of two steps (not necessarily in this order): (1) Identify a kind of behavior that appears in variety of systems and (2) find the simplest model that demonstrates that behavior.

Holism is based on a basic idea that the whole has some properties that is parts lack. Holism has traditionally appeared as a model of thinking in the philosophy of biology, psychology and in the human sciences. Holism is the big modeling idea that natural systems (social, economic, physical, mental, biological, chemical, linguistic, etc.) and their properties should be viewed as wholes, not as collections of parts.

In the latter half of the 20th century, holism led to systems thinking and its derivatives, like the sciences of chaos and complexity analysis. There are hard systems theory and soft systems theory. Systems are frequently so complex that their behavior is, or appears, “emergent”: it cannot be deduced from the properties of the elements alone. Thus it is also “new”. Emergent, self-organizing systems are a part of the whole system in many scientific analyses of psychology, sociology and biology. Scientific holism holds the idea that the behavior of a system cannot be perfectly predicted, no matter how much data is available. Even “big data” does not solve this fundamental scientific problem.

We cannot explain the existence of synergy without holistic thinking. According to scientific interpretation of holism, there are good ontological reasons that prevent reductive models in principle from providing efficient algorithms for prediction of system behavior in certain classes of systems. This is a very serious question for many fields of new inventions and innovations. Why to accept limits for ideas?

With roots in Joseph Schumpeter, the evolutionary approach might be considered the holist theory in economics. Evolutionary economics deals with the study of processes that transform economy for organizations, companies, institutions, corporations, industries, employment, production, trade and growth within, through the actions of diverse agents from experience and interactions, using evolutionary methodology. Evolutionary economics share certain language game elements from the biological evolutionary approach. Thomas Kuhn, the author of “The Structure of Scientific Revolutions” (1962), accepted this kind of evolutionary approach to scientific (r)evolutions.

Innovation processes are not easily explained by reductionist models. Evolutionary economics is typically used when innovation processes are explained. Evolutionary economics analyses the unleashing of a process of technological and institutional innovation by generating and testing a diversity of ideas which discover and accumulate more survival value for the costs incurred than competing alternatives.

One can note that holism and reductionism are different models with different purposes. For reductionist models, realism is the primary value, and simplicity is secondary. For holistic models, it is the other way around.

Thus, the choice of modeling is a normative choice. We should be open for both perspectives of the big science. Reductionism helps us to focus. Holism helps us to open our eyes.




Weinberg, S. (1992) Dreams of a Final Theory: The Scientist’s Search for the Ultimate Laws of Nature. New York. Pantheon Books.

Jones, R.H. (2000) Reductionism: Analysis and the Fullness of Reality. Bucknell University Press.

Thomas Kuhn


Kuhn, T.S. (1962) The Sructure of Scientific Revolutions. Chicago: University of Chicago Press.




Dennett, D. (1995) Darwin’s Dangerous Idea. New York: Simon & Schuster.

Downey, A.B. (2012) Think Complexity. Sebastopol, CA: O´Reilly.

Jan Smuts


Audi, R. (1999) The Cambridge Dictionary of Philosophy. Second Edition. Cambridge: Cambridge University Press.

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