7 Key Concepts You Need to Know From Herbert Simon’s Paper on the Architecture of Complexity
Introduction
In 1962, Herbert A. Simon, a distinguished figure in the fields of economics, psychology, and artificial intelligence, published a beautiful paper entitled “The Architecture of Complexity.” This seminal work has since become a cornerstone in studying complex systems, transcending disciplinary boundaries and impacting various research fields.
Simon’s paper thoroughly examines the nature of complexity. By exploring the interconnectedness of diverse disciplines, such as biology, physics, sociology, and computer science, Simon illuminates the inherent complexity present in systems at both the molecular and societal levels.
This article will break down Simon’s original paper into seven key concepts with commentary on each.
Concept 1: Why We Need Systems Theory
A number of proposals have been advanced in recent years for the development of “general systems theory”, which, abstracting from properties peculiar to physical, biological, or social systems, would be applicable to all of them. We might well feel that, while the goal is laudable, systems of such diverse kinds could hardly be expected to have any nontrivial properties in common. Metaphor and analogy can be helpful, or they can be misleading. All depends on whether the similarities the metaphor captures are significant or superficial.
— H. A. Simon, The Architecture of Complexity
Two fundamental issues in describing complex systems have been encountered so far:
The significance of General Systems Theory lies in its ability to explain phenomena of complex systems in sufficiently abstract and rigorous methods despite the former’s spread into various fields and disciplines such as physics, chemistry, biology, and social sciences.
General Systems Theory (GST) is a conceptual framework and interdisciplinary field that originated in the mid-20th century. It was developed to address complex phenomena and problems involving interaction between system components or elements. Here are the key facts about General Systems Theory:
General Systems Theory provides a systematic way to analyze and understand complex systems, emphasizing their interconnectedness and the emergence of properties at different levels of organization. It has influenced multiple disciplines and continues to be relevant in addressing complex problems across various fields.
Concept 2: What Is a Complex System?
Roughly, by a complex system, I mean one made up of a large number of parts that interact in a nonsimple way. In such systems, the whole is more than the sum of the parts, not in an ultimate, metaphysical sense, but in the important pragmatic sense that, given the properties of the parts and the laws of their interaction, it is not a trivial matter to infer the properties of the whole.
— H. A. Simon, The Architecture of Complexity
In Complexity in Natural and Human Systems — Why and When We Should Care, we provided four definitions of complexity, which, despite some overlapping, presented precise notions of what a complex system should look like. Herbert Simon originally adopted one of those definitions revolving around the following concepts:
Concept 3: Why Hierarchical Complex Systems Are More Frequent in Nature
The lesson for biological evolution is quite clear and direct. The time required for the evolution of a complex form from simple elements depends critically on the numbers and distribution of potential intermediate stable forms.
— H. A. Simon, The Architecture of Complexity
In Dr. Simon’s paper, hierarchies play a pivotal role in the rise and evolution of complex systems:
This last concept explaining why nature favours hierarchical systems is crucial in an age where flat organisational structures are seen as the natural progression towards a more empowered and egalitarian form of running businesses. The entire team is affected in a flat hierarchy when the boss is absent or overloaded. A hierarchical structure is easier to scale by breaking and recomposing existing subsystems. On the contrary, more direct reports make the system more fragile in a flat hierarchy by continuously overloading the line manager.
Another recent concept, that of self-organisation, has emerged with Agile, in which no formal hierarchies are imposed, but the teams are encouraged to self-organise, preferably without hierarchies. Studies have shown that hierarchies will inevitably emerge in such cases, although for slightly other reasons than the ones described above (speed of evolution). It seems that hierarchies are inevitable.
Concept 4: Problem-Solving Through Selective Trial and Error
Nature is the most capable problem solver. By producing life through millions of years of constant tinkering and refinements, nature has solved what appears to be an insoluble puzzle. Simon extends this analogy further to cover the problem-solving activities we normally engage in.
A considerable amount has been learned in the past five years about the nature of the mazes that represent common human problem-solving tasks, proving theorems, solving puzzles, playing chess, making investments, and balancing assembly lines, to mention a few. All that we have learned about these mazes points to the same conclusion: that human problem-solving, from the most blundering to the most insightful, involves nothing more than varying mixtures of trial and error and selectivity.
— H. A. Simon, The Architecture of Complexity
Simon’s theory of how people solve problems is based on a “mixture of trial and error and selectivity”. Confronted with problems we encounter for the first time, the following process occurs in our minds:
Concept 5: Selectivity and Feedback Loops
When we examine the sources from which the problem-solving system, or the evolving system, as the case may be, derives its selectivity, we discover that selectivity can always be equated with some kind of feedback of information from the environment.
— H. A. Simon, The Architecture of Complexity
This approach to problem-solving presents the following features:
Concept 6: Nearly Decomposable Systems
(a) in a nearly decomposable system, the short-run behaviour of each of the component subsystems is approximately independent of the short-run behaviour of the other components; (b) in the long run, the behaviour of any one of the components depends in only an aggregate way on the behaviour of the other components.
— H. A. Simon, The Architecture of Complexity
The notion of near decomposability is intuitive, elusive, and powerful at the same time. It lies at the heart of our ability to describe complex systems without having to track every single one of its parts, which, for example, in the case of a volume of gas, can be intractable. Near decomposability can be explained as follows:
Because of the weak links between the different subgroups, the short-term evolution of a group will be independent of the rest. In the long run, the behaviour of one group will depend on the others only in an aggregate way; short-term, high-frequency changes will cancel out in the long run.
Consider the structure of a society as an example. Strong familial ties hold families firmly together, while appreciable but less strong ones hold tribes, communities, and neighbourhoods in looser but fairly distinct structures. On the nation-state level, similar bonds allow citizens to form identities and cultures and perceive themselves as one whole.
Concept 7: Hierarchies in Social Systems
In social as in physical systems, there are generally limits on the simultaneous interaction of large numbers of subsystems. In the social case, these limits are related to [a] human being more nearly a serial than a parallel information-processing system. He can carry on only one conversation at a time, and although this does not limit the size of the audience to which mass communication can be addressed, it does limit the number of people simultaneously involved in most other forms of social interaction.
— H. A. Simon, The Architecture of Complexity
The necessity and inevitability of hierarchies in complex systems have been amply described in the previous sections. In the present one, we will focus a bit more on the specific examples of social hierarchies and why they emerge.
Simon provides a compelling theory well-established in the scientific community (see Sapiens – A Brief History of Humankind). The theory posits constraints on relationship building among individuals, with those limits generally applying to the size of the group being formed. The type of relationship sought influences the limit.
For example:
Conclusion
Philip assembled his Macedonian empire and gave it to his son, to be later combined with the Persian subassembly and others into Alexander’s greater system. On Alexander’s death, his empire did not crumble to dust but fragmented into some of the major subsystems that had composed it. The watchmaker argument implies that if one were Alexander, one should be born into a world where large, stable political systems already exist. Where this condition was not fulfilled, as on the Scythian and Indian frontiers, Alexander found empire building a slippery business.
— H. A. Simon, The Architecture of Complexity
The general reader might ask Why we (software developers) need to care about complexity? The answer is twofold. First, “The Architecture of Complexity” is a masterful paper by a first-caliber intellectual, Herbert A. Simon, a classic for complexity enthusiasts. Second, complexity consists of a set of concepts that are invaluable to understanding social systems, such as software teams.
The study of the very small (elementary particles, quantum systems, superstrings) and the very large (solar systems, galaxies, clusters and superclusters of galaxies, and the universe itself) relies heavily on concepts of Newtonian dynamics. Everything in between, such as crystals, proteins, DNA, living organisms, social groups of any size, and many other physical, chemical, and biological systems, are complex, and their study through the Newtonian framework is likely to frustrate even the most patient.
Applying Newtonian dynamics, i.e. a framework where the future evolution of a system is deterministic and depends solely on its laws of dynamics and initial conditions, to complex systems such as a team of software developers is doomed to fail. Therefore, software managers and developers must expand their knowledge in the areas of anthropology, philosophy, and complexity theory. This article and the many others we authored on these topics are there to help the author and reader understand what makes complex systems tick and how to act in them.
This elaborate study of complexity, especially in social sciences, lies in the context of achieving Operational Excellence in Software Development teams, helping developers succeed in creating better and more affordable software. We hope this article has been insightful for the reader, helping them make sense of the complex (and sometimes hostile) ecosystem in which they operate.