It might seem not worthwhile or even be superfluous to wonder if there exists a need to model the evolutionary behavior of social organizations. Nevertheless, when one observes the world we live in, the conflicts that confront humanity—terrorism, repeated wars, environmental problems, inequalities, and increasing poverty—one cannot but think about the convenience and need for dealing better with social organizations.
On the other hand, humanity has reached such a degree of technological and scientific development that it can be asserted that there is no problem of a technological nature—if it is solvable and humans concentrate on solving it—that could not be solved within some timeframe. Think of the program to send a man to Mars. The approximate amount of resources that such a colossal task will consume and the time required have already been calculated, so it is a reasonable assumption that nobody doubts that at some time in the future, man will land on Mars. Nor does anyone doubt that sooner or later humans will find a cure for cancer and so on; if humanity doesn’t suffer a catastrophe before then, that is. However, at the same time how many people really believe that the problems of poverty, degradation of the environment, terrorism, or underdevelopment will be solved within the foreseeable future?
I am not making any political exhortations, I only want to emphasize the growing need for more understanding of the evolution of social organizations, a term that includes companies, political parties, governments and their ministries, factories, schools, religious organizations, nongovernmental organizations, and innumerable others.
At the same time, when organizational management methods and theories currently in vogue are analyzed (Morgan, 1998), one can detect that they are based on two paradigms, which I have called the paradigm of good practices and the paradigm of talent (or the lack thereof). The first established that all management theories are really practices that have given good results in one context or another, and that in many cases are generalized without bearing their context in mind. For example, management by objectives, project management, and others (Mintzberg, 1994). The second paradigm refers to a management method based on the capacity and knowledge (or the lack thereof) of the managers. Actual management processes are often a combination of some proportion of these two paradigms, and are in fact a process of test-and-correct. It is necessary to point out immediately that errors in management are very expensive and, given their markedly irreversible character, very difficult to correct.
To illustrate the above situation it is useful to quote Jeffrey Goldstein (1997), who concluded:
1(a) When organizations succeed, it is mostly in spite of, not because of the way they are organized. 1(b) When organizations succeed, it is mostly in spite of, not because of, the way leadership is exercised. 1(c) The manner in which most organizational working units are organized, set up and managed serves more to stifle than to encourage the creativity and productivity of its members.
These conclusions might sound dramatic, but there is doubtless an unsolved problem here.
Why does this unsatisfactory situation exist? In my judgment it is because we lack an understanding of the evolutionary dynamics of social organizations. I think that complexity theory can contribute to attaining that knowledge. What makes me think that modeling the evolutionary behavior of social organizations is not only necessary but also possible? Below I will try to give an answer to this question, based on the one hand on the relationship between the sequence of appearance of different kinds of systems’ laws of motion (mechanical, thermodynamical, chemical, and biological) and on the other hand on the degree and qualitative difference of systems that obey those laws.
SOCIAL ORGANIZATIONS (SO) ARE THE MOST COMPLEX SYSTEMS
Without any doubt, the current world is characterized by the complexity of the problems it must face and solve on a daily basis. The term “complexity,” however, has become popular in the most diverse branches of science and even outside of them. By way of illustration, we refer to studies on the science of complexity by the Santa Fe Institute (Zurek, 1990) and to a newsletter of the International Council of Scientific Unions (ICSU, 1994) devoted to ideas on the relationship between complexity, chaotic behavior, and systems dynamics presented at the seminar Confronting Complexity in Science by numerous eminent scientists in fields as varied as mathematics, physics, biology, neurosciences, and philosophy. Another recent reference regarding complexity, however, ends by declaring, after a page and a half of analysis:
In fact, complexity (together with the related terms of order and structure) appears as essentially undefinable in any way that allows objective measurements. (Ayres, 1994: 13-14)
One can conclude, therefore, that there does not appear to exist much consensus in connection with complexity, as also evidenced by the fact that this lack of consensus was the topic of a recent conference (NECSI, 2001). So I will try to strike out along a new road, not so much to attempt a definition of complexity but to capture the relationship between the types of motion of a system and the degree or level of complexity that they display. This road will be based on the ideas expressed by Engels in his Dialectics of Nature (Engels, 1982) concerning the complexity of different types of motion, understanding as motion any class of change in the system. Under this understanding, it is essential not to forget that there always exists a material carrier of the executed motion.
Engels proposed a classification of motion along an increasing scale of complexity; that is, complexity exists to a greater or lesser degree according to the type of complex motion that the system is able to perform. Accordingly, Engels identified mechanical motion as the simplest, followed by physical motion (understood as thermodynamics and electrodynamics, to which today one must add relativity theory and quantum mechanics). A more complex motion than physical motion would be chemical motion, that of the combination and transformation of some substances into others. Biological movement would come next, as linked to its main distinctive characteristic, reproduction. Following this logic, social motion would occupy the highest level of complexity, and would be the one characteristic of human organizations of whichever type.
Although very intuitive, this classification has its problems, as does any other. One can say that mechanical motion is the motion of the whole as such. For example, when one studies the mechanical motion of the solar system one is not interested in—or it is not important to know— what occurs on the Sun itself. The laws of mechanical motion can say nothing about nuclear reactions on the Sun. If one wants to know about these one needs to use the laws of nuclear physics. In this sense, chemical motion is characteristic of interactions among different kinds of substances formed by millions and millions of entities that give rise to millions and millions of entities of another kind of substance. Naturally, these interactions can take place only in the presence of the mechanical motion of atoms and molecules, but you cannot reduce the process of chemical reactions to a mechanical displacement of any kind. In this sense chemical motion is richer, more complex than mechanical motion.
It is important to note that within each of these types of motion (mechanical, physical, chemical, biological, and social) one can find systems with simpler and systems with more complex motions. For example, the mechanical motion of a falling object is simpler than the mechanical motion of a planetary clock; and the biological motion that an amoeba can carry out is simpler than one that mammals can perform. Thus one can maintain that complexity in general has at least two very defined dimensions, one that corresponds to a generic type of motion and another one within each generic type.
To complete this introduction to complexity, I recall a factor that some authors seem to forget when they try to model the dynamics of “the living,” including social organizations (Mack, 1994). These authors often use the same conceptual scaffolding (formalism) as they use to model simpler types of motion, without keeping in mind a very important qualitative aspect, also noted by Engels, that is summarized by the fact that the more complex generic type of motion includes, but cannot be reduced to, an aggregation of the simpler types.
An interesting example of these qualitative and irreducible differences can be found in the difference between thermodynamics and mechanics, that is, between the description or modeling of the motion of millions and millions of particles—say, a gas—and the description of the motion of only a few particles according to Newtonian laws. As is well known, the laws of mechanics are time reversible, with no differences in the direction in which time flows (toward the past or toward the future). In contrast, the laws of thermodynamics have an irreversible character and time flows only in direction of the future or, as some like to put it, in the direction of the increase in entropy.
Another very different example can be found in the study of language: The development of language and its dynamics could not be understood only from studying man as a biological being, because language is essentially a social phenomenon that, although developed on a biological substratum, cannot be reduced to it.
In my opinion, this methodological error prevents many authors from attending to the qualitatively different aspects of a particular type of motion with respect to other simpler ones. For modeling human social organizations, however, this aspect will turn out to be essential.
If we study the appearance of scientific discoveries and truly novel scientific ideas in light of these conceptions about complexity, we can see that they have appeared in a sequence that coincides with the sequence of increasing complexity of motion. The first scientific results in modern times, which appeared approximately in the seventeenth century, were related to mechanics and are associated with the well-known names of Newton and Galileo, among others. The nineteenth century, together with the beginning of the twentieth, can be characterized as the acme of physics. Formulated during that period were thermodynamics and its famous Second Law (entropy in an isolated system can only grow or remain constant), Maxwell’s electrodynamics, Einstein’s relativity theory, and quantum mechanics. Chemistry, which developed in parallel with physics, finally found a solid foundation in the first two decades of the twentieth century with the discovery of atomic structure. The 1940s saw the rise, as scientific disciplines, of information theory and cybernetics, which created the paradigmatic basis on which, to a large extent, all computer science is based. These two sciences went on to contribute in an important way to the understanding of the enigmas of the genetic code, on the basis of which decisive advances occurred in biology as it took over as the avant garde of scientific progress by the middle of the twentieth century. A turning point in this changeover was the discovery of the structure of the DNA molecule in the 1950s, based on X-ray analysis discovered in the 1920s.
Worth keeping in mind is the fact that in spite of the considerable resources used in physical investigations in the last 50 years, relatively few truly new principles have recently been discovered. The considerable expense dedicated to the as yet unfinished construction of a superconducting particle accelerator in the US is illustrative of this. The study of biological systems has advanced in a spectacular way in the last decades. Today we have reached the point where we can begin, in a systematic way, to turn biological knowledge into goods and services, as illustrated to some extent by the appearance of words such as biotechnology and genetic engineering.
To summarize, it can be maintained that results in science have been closely linked to the degree of complexity of the type of motion studied, and that the sequence has proceeded from the simpler to the more complex. At each stage, the shift has been marked by the formulation or discovery of laws that describe a particular type of motion, thereby giving predictive strength to the sciences related to its study. Today, it seems that the stage is being set for the transition to a dynamic modeling of social organizations. This modeling will bring with it remarkable and significant results, as has already happened as a consequence of the study of other simpler types of motion.
SOME CHARACTERISTICS OF SOCIAL ORGANIZATIONS STUDIED AS COMPLEX DYNAMIC SYSTEMS
The conclusion of the previous paragraph that we are most interested in emphasizing is that a social organization (SO)—that is, any human organization, such as a laboratory, a factory, an institute, and so on—is a dynamic system; that is, a system that changes and will evolve over time. The complexity in the systems studied by human beings has thus grown, gradually, continually, and in parallel with the advancements of science. Today there are innumerable systems studied by the natural sciences and biology in particular that are classified as complex dynamic systems (CDS), among them lasers, the climate, the human brain, autocatalytic chemical reactions, ecological systems, huge electrical systems, systems of electronic communication, and many others.
The last few decades have seen considerable progress in the understanding and modeling of the dynamics of such systems. This research has shown that in these systems the transition from orderly (predictable) behavior to disorderly, unpredictable, or chaotic behavior goes through the same sequence of phases. I refer in particular to Lorentz’s early work on climate prediction during the 1960s (Lorentz, 1963), Rene Thom’s catastrophe theory (Thom, 1975), Feigenbaum’s universalisms (Feigenbaum, 1978), and a multitude of other works, beginning in the 1970s, on the chaotic behavior of such systems. The same decade saw the start of the ideas on synergetics advanced by the German physicist Hermann Haken (Haken, 1987), which contributed to understanding the self-organized orderly (coherent) behavior of systems consisting of a multitude of elements (subsystems) that in general behave in an aleatory or disordered way but that, under certain conditions, also called restrictions, behave in a coherent and orderly manner.
To all this it is necessary to add the development of the thermodynamics of irreversible processes, especially the work of the Brussels school with I. Prigogine at its head (Nicolis & Prigogine, 1977, 1989). Prigogine proposed the term “dissipative structures” for those dynamic systems with stable or stationary structures located far from thermodynamic equilibrium and maintained by the constant dissipation of energy. Today all living systems are thought to be dissipative structures; in other words, these systems can only exist on the basis of a continuous flow of energy, information, and substances. These works helped significantly in the understanding of phenomena associated with the behavior of complex dynamic systems.
It is therefore convenient to enumerate some characteristics that social organizations possess as complex dynamical systems. I divide them into two different categories: first, those characteristics that they have in common with other complex systems belonging to other types of motion (in accordance with the classification that we have proposed); second, those characteristics that I consider to belong only to the social system’s type of motion and that are, therefore, not found in any other type of system, whether biological or otherwise.
THE POSSIBLE ROLE OF THE ENVIRONMENT IN THE DYNAMICS OF SOCIAL ORGANIZATIONS
It is indubitable that social organizations are open systems, but to what are they open? With what do they interact? That with which social organizations interact can be called their “environment.” Evidently this is not only a biological or ecological environment, it is mainly a social environment; or, more precisely, a socio-political-economic-natural environment, all together forming a certain totality.
Any social organization will evolve within the environment with which it interacts, by somehow influencing it and being influenced by it. In order to understand more precisely the evolution of social organizations, however, we need to examine those characteristics of any environment that are necessary or essential for social evolution.
I believe that two characteristics of the environment are very important for understanding—however incompletely—the evolutionary dynamics of social organizations. Both are interconnected and thus cannot exist independently of each other. I will formulate these properties as a postulate that I will call the postulate of the predictable and the unpredictable:
For social organizations, the behavior of the environment is only partially predictable and, at the same time, it is in part also unpredictable.
There is a certain redundancy in this formulation, but, as we shall see later on, it is a necessary one in order to be able to differentiate clearly between the predictable and the unpredictable. It is also necessary to emphasize that when we speak here about the unpredictable, we refer precisely to what cannot in principle be foreseen by the system. At each moment of the system’s existence there are changes in the environment that will be essentially new for the system and therefore impossible to foresee. In relation to the predictable, what the postulate means is that only an incomplete, partial, or limited forecast of the behavior of the environment is possible.
What consequences does the behavior of its environment have for the motion of a social organization?
A social system, as we have seen, fulfills certain objectives or carries out certain functions. In light of the two caveats concerning the partial predictability and unpredictability of any environment, how should that system be organized in order to respond in such a way as to adapt or evolve successfully despite the essentially contradictory qualities of its environment? As a tendency, if a system does not “move” with success within its environment—that is, if the system does not attain its goals or functions—it will devolve and ultimately could even disappear.
I will try to illustrate this idea with an example from biology. Naturally, no social system, in my view, can be reduced to a biological one, but the social type of motion is possible only if it includes all other simpler types of motion. So we can always learn something from biological systems that we will surely find in social ones.
Think about the life of a lion in the forest. The lion can find the place where he hunts, the place where he drinks water, the place where he sleeps, and so on. He can, in this sense, ordinarily “foresee” where these places are. What would happen if all these places begin to change randomly for the lion; that is, when the lion goes for water he cannot find it, when he goes to sleep he cannot find his lair, and so on? Of course, the lion’s life would be in serious danger. He thus requires some stability in his environment that enables him to find what he needs and, in this sense, he commonly can, as we said, “foresee” where these places are.
However, a lion’s life is not reducible to the above-mentioned. There are other facts that the lion cannot really “foresee.” For example, when he hunts he does not “know” in advance what the results will be and naturally he cannot “know” ultimately if he will be the hunted. So, a lion’s life takes place in an environment that has for him the above two properties: being at the same time partially predictable and unpredictable.
What role does the unpredictable part of the environment play in this case? What does the lion need that part for? It can be said that for an individual lion that is a very bad part. Because he has no options, the only possibility for him is to resist those changes or otherwise he will go hungry or be hunted. Nonetheless, that part is a very important one for the entire species, because it guarantees the capacity of lions to resist the environmental changes that always, sooner or later, will take place; thus in the long run, that part is also very important for each indvidual lion.
In the case of social organizations things are quite different, because for these it is possible to adapt to the random unforeseeable changes of the environment in real time, that is, in creative ways. That is a very important feature that differentiates human systems from animal ones.
A MORE GENERAL MODEL OF A SOCIAL ORGANIZATION
The items outlined in the previous paragraph can have, in principle, a multitude of answers. However, not all answers will deal with equal success with reality, and therefore it is necessary to advance some hypotheses as possible answers and try to check how well they work.
We propose the following: A social system should be organized in such a way that it reproduces in its very functioning and structure as a result of its interactions with the environment in which it moves, evolves, and develops—the two above-mentioned properties of the environment. This means that a social organization’s internal structure should contain at least two parts, one that corresponds to the predictable aspect of the environment and that is subject to planning and control, and another that corresponds to the unpredictable, aleatory, stochastic behavior of that environment. One can speak here of a sort of structural, functional, dynamical coupling between the social system and its environment (see
We label the “control zone” that portion of a social organization that reflects the predictable side of its environment. This part of an organization should be charged with continuously maintaining the possibility of attaining the objectives or executing the functions of the system. This structural portion of a social organization corresponds to that area of any system responsible for guaranteeing the coordination of all the system’s efforts aimed at the attainment of the proposed ends.
We have named the “creativity zone” that portion of the system that reflects the unpredictable behavior of its environment. This organizational area is charged with foreseeing the unpredictable—which is of course impossible. How should this organizational area be conceptualized, and how should it work? We submit that this organizational area should be charged with continuously creating and designing tests of the system’s possible adaptive responses to unpredictable changes in its environment. However, since those changes are truly unpredictable, the creativity zone should conduct those tests in a non-planned or organized manner. To put it another way, in this creativity zone an aleatory, random, or chaotic motion takes place that continuously searches all possible adaptive actions. This creativity zone is thus the organizational area where unforeseen responses to unpredictable problems should be continuously elaborated.
Perhaps the best way to convey this idea is with an example, that of recent research in the field of superconductivity. Until 1986 research on superconductivity was carried out in the area we have been calling the creativity zone. That is, until then research on superconductivity was carried out in an unplanned and unorganized manner by those with resources who believed that superconductivity was achievable, that it was an interesting scientific problem, and so on. That research effort faced a formidable difficulty: the fact that the superconductivity effect was at the time thought to be achievable only at temperatures of liquid helium, that is, very near absolute zero (-273°K). Besides being extremely expensive, liquid helium also filters and escapes through any interstice or microscopic pore, which makes its industrial use practically impossible. As an instructive aside, it also is necessary to add the fact that the physical theories for explaining superconductivity in vogue at the time predicted a maximum temperature limit that rendered the phenomenon impossible.
During 1986, however, a small group of researchers discovered that, in spite of everything that was believed at the time, superconductivity was in fact possible at the temperature of liquid nitrogen, a liquid produced on an industrial scale and widely used. From that moment on, and in only three months, more than 100 laboratories with planned objectives, assigned resources, and future research plans on superconductivity were created by government programs throughout the world.
In this example, we clearly see a transition from an area where research is carried out by diverse interests—that is, with an aleatory character—to an area where the results are planned, foreseen, and to which resources are assigned in order to attain some specific results.
The relationship between these two areas and their relative proportions depends at least:
The consequences that maximizing either one of these zones could have for a social system provide a good example. Traditional socialist planning is well known: It pretended to foresee everything, plan everything, control everything on the basis of defined social objectives. Why did it fail? Naturally it was not for one single reason, but its failure can in part be explained by its inability to adapt to the more or less quick changes of its environment. It had reduced the area of creativity to 0 bits.
At the other extreme stands neoliberalism. Under conditions of neoliberalism everything is economically possible: free trade, free flow of capital, and so on. But to what does it lead? Today, the Argentinean case makes evident that this economic pattern leads to serious social conflicts and ultimately also fails. Why? In this case the system loses its social objectives and in the long run leads to well-known conflicts. Here the zone of free will is maximized.
In summary, successful social operations require both zones distributed in varying proportions; in other words, a certain amount of planning combined with a certain freedom of action.
SOME CONSEQUENCES FOR THE MANAGEMENT OF SOCIAL ORGANIZATIONS
From the foregoing some considerations should be kept in mind for the efficient management of social organizations considered as complex dynamical systems:
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- Engels, F (1982) Dialectics of Nature, Havana, Cuba: Social Sciences Editorial.
- Feigenbaum, M. (1978) “Quantitative universality for the class of nonlinear transformation,” Journal of Statistical Physics, 19: 25-52.
- Goldstein, J. (1997) “Psychology and corporations: A complex systems perspective,” Proceedings of the International Conference on Complex Systems, Nashua, NH, 21-26 September; also online in the InterJournal.
- Haken, H. (1987) Advanced Synergetics, Berlin, Germany: Springer Verlag.
- Lorentz, E. (1963) “Deterministic nonperiodic flow,” Journal of Atmospheric Science, 20: 41-130.
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