System Thinking

Bertalanffy argues that Aristotle’s holistic notion forms a defi nition of the basic system, which is still valid. According to him, order or aggregation of a whole or a system, exceed-ing its parts when these are considered in isolation, is nothexceed-ing metaphysical, not a super-stition or a philosophical speculation. It is a fact of observation encountered whenever we look at a living organization, a social group, or even an atom. Articulates of Ludwig Von Bertalanffy are

1. The fact that the study of living systems is a study of commonalties shared by systems of differing physical structures

2. That physics cannot encompass organic phenomenon without fundamental modi-fi cation and extension

3. That the commonalties’ characteristics of biological systems are exemplary of other kinds of commonalties, which could form the basis for the general systems theory

4. That biological systems (developmental systems) are open systems, able to exchange matter, energy, and information with their environments, and conse-quently the second law of thermodynamics (that material systems should proceed from ordered to disordered states) is not even directly applicable to them

5. That apparently purposive telic behavior of developing systems is entirely plausible Therefore, we see that there is a sense in which we can learn something about a particular sys-tem S, such as a developing organism, by studying some other purely reductionistic or physi-cal sense, but nonetheless manifesting the same behavior is shared by systems of the utmost physical diversity. That is, physically disparate systems can nevertheless be models or analogs of each other. Thus the two distinct systems, S₁ and S₂ , can behave similarly only to the extent that they comprise alternate realizations of a common mathematical or formal structure.

The crucial difference between magic and science resides in the manner in which mod-els are generated and through which specifi c properties of the system are captured. The hypothesis of Kabala, that makes it mysticism rather than science, is that the name of a system are manifested in the corresponding numerical properties of the associated num-ber and can be studied thereby. A Mathematical object with system captures some aspects of the reality of the system. We seek to learn about the system by studying the properties of the associated model.

It is perhaps ironic that few achievements of scientifi c world should be nonreductionist character typical of system theoretic arguments. Hamilton showed that two distinct and independent branches of physics, namely, optics and mechanics could be conceptually uni-fi ed not through reduction of optics to mechanics or vice versa but rather through the fact that both obeyed the same formal law, that is, realized the same formal system. Hamilton stopped there and 100 years later, Schrödinger exploited the commonalties between the two and developed wave mechanics.

Even mathematicians often construct mathematical models of other mathematical systems. The relation between a topological space (a differential and abstract subject) and an associated group (an algebraic system much simpler than topology) is a mod-eling relation and it enabled us to learn about the homomorphism topological spaces by studying entirely different isomorphic groups of algebra system. This was further exploited to develop “Theory of Categories” which cuts across all other.

It was clearly demonstrated that where science fails to explain, system approach of exploiting commonalties applies. A.D. Hall and R.E. Fagan have thoughtfully defi ned sys-tems thinking as below:

Systems thinking is the art and science of linking structure to performance, and per-formance to structure—often for purposes of changing relationships so as to improve performance.

But, how should we look at a complex world? One approach is to break the complex world into smaller, more manageable pieces. The argument goes that if we can under-stand the separate pieces, then we can put our separate underunder-standings together to understand the whole. This is reductionism, or Cartesian thinking. It works for simple things. Cartesian thinking fails to address complex problems because, in the process of breaking up the overall system into parts, the connections and interactions between those parts get lost. Consider a comparison:

1. If you break a brick wall into parts, you end up with a pile of bricks, with which you can rebuild the brick wall—nothing lost, and perhaps even something gained in an improved wall, as shown in Figure 1.13.

2. Now if you break a human being into parts, you end up with a pile of organs, bones, muscles, sinews … but you can never reconstitute the human being.

Where does the difference lie?

The whole human being depends on the continued interaction between all its parts—

in fact, the parts are all mutually dependent. So it turns out to be with complex systems.

They are made up from many interacting, mutually dependent parts. Because of this it is often impractical to conduct experiments on them.

So, we need to be able to think about complex issues, partly because we cannot use Cartesian methods to “reduce” them, and partly because we cannot conduct controlled experiments upon them. The implications of that is simply this: System thinking must be rigorous if it is to be both credible and useful. But, is all systems thinking rigorous?

When learning new concepts and ways of thinking, a picture can be worth a thousand words. When we look at how people learn new things, the graphical aspect of systems thinking helps us visually see how systems work and how we might be able to work through them in better ways. The term “systems thinking” was fi rst associated with Jay Forrester from MIT in the 1940s to refer to a different way of looking at problems and goals not as isolated events, but as parts of interrelated structures.

When we look at business and human endeavors as systems, we need to understand the complete picture, the interrelated variables, and the effects they have on each other.

We cannot understand the whole of any system by studying its parts. For example, if you want to understand how a car works and you take it apart and study each of its parts (engine, tires, a drive shaft, a carburetor, transmission, etc.), you would have no clue as to how a car works. To understand an automobile, you must study the relationship of the parts and how they work together. The holistic approach for explaining the system working is important.

System thinking is a conceptual framework, a body of knowledge and tools, developed over the past many years, to explicitly underline the effects of emergent properties and visualize this implicit and explicit effect.

System thinking can be used to understand highly complex systems; it also can help us understand day-to-day issues. As an example, many of us would like to lose weight.

What is the nature of the system in which we fi nd ourselves? Let us identify the vari-ables in this system: unhappiness with weight, amount of food consumed, and degree of hunger. What is the relationship between these variables?

From Figure 1.14, one may understand the nature of systems thinking. How could this help in an everyday problem solving like dieting by seeing the structure of the underlying system.

Another example: Any company which fi nds labor costs are too high and wants to reduce them. So, they simply lay off 10% of their work force. Costs might go down right away but the workload on the remaining work force increases manyfold. Those workers feel stressed and cannot get all the work done. As a result, the company hires outside people to help them and reduce the workload.

FIGURE 1.13

Example of a system and its component.

(a)

(b) (c)

Step 1. The following variables could be identifi ed.

Product manufacturing cost Lay off workers

Hire outside people Workload

Step 2. Find relationships between these variables shown in Figure 1.15.

Systems thinking is a wonderful tool for understanding the environment. It provides a visual tool for learning. It was not until I saw the picture that I began to understand some of the critical elements of thinking systemically. A systems picture can indeed be worth a thousand words!