Herbert Simon’s The Sciences of the Artificial – 3rd Edition provided an alternative model for thinking about society. We can think of social institutions as partially designed and selected for their organizational properties; so they are different from proteins and planetary systems. Simon is also an important contributor to the study of complexity. So his new chapter in the 1996 edition of the book, “Alternative Views of Complexity,” is worth reading carefully. Here is how he motivates this new chapter in SA:
The preceding chapters of this book have discussed several kinds of artificial systems. The examples we have examined — in particular, economic systems, the business firm, the human mind, sophisticated engineering designs, and social plans — range from the moderately to the exceedingly complex (not necessarily in the order in which I have just listed them). These final two chapters address the topic of complexity more generally, to see what light it casts on the structure and operation of these and other large systems that are prominent in our world today. (169)
It turns out that there isn’t much new in the 1996 chapter, however. In fact, most of its content is taken from his pathbreaking 1962 article, “The Architecture of Complexity” (link). The new chapter 7 and renumbered chapter 8 largely incorporate the content and sometimes the language of the 1962 article. And this is interesting, because it implies that Simon’s primary ideas about reduction, composition, and inter-level interactions were largely already formed in 1962.
There are a few ideas and themes that are new to the 1996 version. One is a more specific periodization of thinking about complexity theory in the twentieth century. The 1996 version identifies three phases of theorizing about complexity and “whole systems”.
- Biological emergence theory (post World War I)
- Cybernetics and systems theory (post World War II)
- Contemporary complexity theory (post 1960s)
Simon is skeptical about the tendency towards irreducible holism that was associated with the earlier two phases of thinking in both versions; in the 1996 chapter he favors a “weak” interpretation of emergence: a commitment to …
… reductionism in principle even though it is not easy (often not even computationally feasible) to infer rigorously the properties of the whole from knowledge of the properties of the parts. In this pragmatic way, we can build nearly independent theories for each successive level of complexity, but at the same time, build bridging theories that show how each higher level can be accounted for in terms of the elements and relations of the next level down. (172)
This “pragmatic holism” is already contained in the 1962 version (link). So this doesn’t represent new ground in 1996. But Simon’s use of this idea to criticize several false starts in the field of complexity research is valuable.
Simon finds some of the central concepts of the third phase to be more promising for the study of social phenomena. The mathematics and physics of chaotic behavior (where simple low-level processes can aggregate to wildly variant higher-level outcomes), simulations of evolution through computational models (genetic algorithms), and the exploration of cellular autonoma (the game of life) all come in for favorable comments. (The Lorenz attractor illustrated here is a common example of chaotic behavior.)
One idea that is not contained in the 1962 version is that of causal non-linearity. Non-linearity is a problem for the “near decomposability” view that Simon wanted to take of complexity in the 1962 version, because it casts doubt on the ability to disentangle causal influences deriving from inter-connected subsystems. Small differences in initial conditions can lead to large differences in outcome. This is a key aspect of chaos theory and the varieties of turbulent phenomena that provide the best examples of chaotic systems. And this casts some doubt on one of the central conclusions of the 1962 paper:
The fact, then, that many complex systems have a nearly decomposable, hierarchic structure is a major facilitating factor enabling us to understand, to describe, and event to “see” such systems and their parts. Or perhaps the proposition should be put the other way round. If there are important systems in the world that are complex without being hierarchic, they may to a considerable extent escape our observation and our understanding. (477)
This is a decidedly pre-chaos understanding of the nature of complex systems. I have the impression that many contemporary complexity theorists would reject the idea that social processes are commonly the result of “nearly decomposable, hierarchic structures”. So it is a genuine change for the mathematics of chaos theory to be included in the 1996 version. Complexity research has moved forward since 1962, and Simon recognizes this in the 1996 chapter.
What we don’t find here is any discussion of whether actual social processes and systems display chaotic behavior in this well defined sense. And we don’t see Simon shifting his position on “nearly decomposable” systems.
Are there examples of social processes and phenomena that display chaotic characteristics over time? Take the occurrence of massive street demonstrations as an example; are there aspects of chaos in the technical sense involved in the outbreak of street mobilization? Do small, apparently random events have large effects on the eventual outcome?
It would appear that this is the case when we look at the cases of uprising and passivity in different cities during the Arab Spring of 2011. Some social scientists have tried to understand the likelihood of uprising as an increasing function of economic crisis, regime weakness, and regime brutality. This implies a linear assumption about the causal role of these three forces. But it seems plausible to speculate that random events like a broken phone chain, an Internet outage, or the defection of a key leader could push the process of mobilization into a different direction. Moreover, it seems that contemporary research on social complexity pays a lot of attention to non-linearity, path-dependency, and sequential processes of social mobilization — leaving a lot of room for the kinds of turbulent effects that are observed in traffic flow, storm generation, and water dripping from a leaking tap. This is the kind of work that is described in Scott Page and John Miller, Complex Adaptive Systems: An Introduction to Computational Models of Social Life.
So oddly enough, it seems that one could fairly say that Simon’s views of social complexity — as expressed in the 1996 third edition of The Sciences of the Artificial as well as in his groundbreaking “Architecture of Complexity” in 1962 — are significantly incomplete, given the way that complexity theorists are now thinking about social processes. Simon did not incorporate the guiding assumptions of “complex adaptive systems theory” into his own thinking, and remained convinced of the adequacy of the ideas of hierarchical systems and nearly decomposable systems as late at 1996. His own approach to social complexity remains a phase two approach, not a phase three approach.
(The graph at the top of this post is offered as an interpretation of a highly path-dependent social process. The reader is asked to consider each path as a hypothetical development from a common origin, with small stochastic variations in the situation occurring over time. Imagine the starting position is “large city, economic hardship, weak state, lots of repression”, time is the x axis, and the y axis measures civil unrest. Some of those variations push the path towards a high outcome (blue), and some towards a low outcome (magenta). The great majority of outcomes fall within a short distance of the starting position. So the most likely outcome is “not much change”, but there are unlikely but diametrically different outcomes possible as well.)