Understanding the Simplicity of Complexity: the Chaos Theory
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Article 3, Chapter 1: Complexity Management
In previous articles we analyzed the scenario posed after the Industrial Revolution 3.0 (IR3.0). We currently live in a hyper-connected and liquefied society where the environment is volatile, uncertain, complex and ambiguous. A VULCAN world. Innovative and productive societies face two main challenges, which may well become advantageous opportunities: planning in the face of uncertainty and the sustainability of change. Human and business organizations can be considered what in Physics are called SYSTEMS. That is, an association of connected elements to achieve a collective goal. In this article we will take a third step forward on the WAKER path and we will focus on the existing scientific knowledge (K, Knowledge) in relation to this type of system.
“I think the next (21st) century will be the Century of Complexity”
Stephen Hawking
Several decades before the Society experienced the IR3.0 that occurred in the last quarter of the last century, Science had already went through its great revolution. In the first quarter of the 20th century, the foundations of the Theory of Relativity and Quantum Physics were established, which had been preceded by Maxwell and his unified theory of electrical and magnetic phenomena. The Cartesian, deterministic and Newtonian scientific paradigm prevailing until then is questioned and falters. It means the transition from a mechanistic conception to the indeterminism. From certainty to uncertainty. From simplicity to complexity.
| Pre-revolution: Traditional Modern Science | Post-revolution: Relativistic and Quantum Science | |
|---|---|---|
| Determinism/Mechanicism | Indeterminism/Relativism | |
| Universal Gravitation, Electromagnetism | Theory of Relativity, Uncertainty Principle, Quantum Theory | |
| Newton, Faraday, Maxwell | Einstein, Heisenberg, Schrödinger | |
| Finite and Static | Infinite and Expanding | |
| · : Dissection · : systems can be explained by the the individual study of each of their components · Precise predictive models | · : Unification · : systems and their properties must be analysed at a whole and not only through the elements that compose them · Probabilistic predictive models | |
| Simple | Complex |
The relativistic and quantum scientific revolution understands that reality is volatile (space-time relativity), uncertain (uncertainty principle, quantum theory), complex (complexity science, chaos theory), ambiguous (corpuscle wave duality), stratified and interconnected (network theory), precisely the VULCAN attributes of today’s Society. Science and Humanities perfectly aligned.
From all aforementioned, it is justified that IR3.0 demands the transition in the organization of companies and human groups from SIMPLE to COMPLEX systems. Which are the characteristics of both?
| SIMPLE Systems | COMPLEX Systems |
|---|---|
| Closed | Open |
| Close to equilibrium | Far away from equilibrium |
| Itemisable elements | Infinite elements |
| Strict principle of causality · Superposition (additivity) · Linear and unidirectional cause-effect relationships | Denial of the principle of causality · Synergy and Interference · Multifactorial and feed-backed cause-effect relationships |
| Limited connections (independence) | Hyperconnectivity (interdependence) |
| Focus on individual components (nodes) | Focus on connections (edges) |
| Order created from centralised coordination: · Homogeneity · Pyramidal structure · Top-down, vertically centralised coordination · Rigidity | Emergence of patterns of order based on autonomy: · Diversity · Self-organisation · Bottom-up or horizontally distributed coordination · Adaptability |
In the same way that “Traditional Science” and the Newtonian paradigm are not adequate for studying the complexity of reality, traditional organizational systems have become obsolete for the management of human teams and innovative organizations of the 21st century. We must assume that complexity cannot be studied or handled by dissecting out the systems into their parts, instead they must be approached in a holistic way, assimilating the system as a whole, understanding not only the entities that constitute it (nodes), but also, and even better, the connections and interactions that are established between them (edges). It would be better to give up building hypotheses and predictive models of behavior based on reductionist empiricism. Its inaccuracy and unreliability only leads to error and frustration. Past behaviors do not guarantee future repetitions. We would better be humbler and accept the uncertainty and probabilistic prediction of behavior patterns, some of them unexpected and surprising. Being alert to identify their appearance is a smarter and more advantageous attitude. We tend to think that the accuracy of mathematics lies in that it can explain reality by working with numbers. However, today’s mathematicians understand their work as the study of patterns, whether real or imaginary, visual or mental, natural or human. To do this, nothing better than learning from the so-called Science of Complexity and the theories encompassed in it, such as Network Theory and Chaos Theory developed only recently at the end of the last century.
“Where Chaos Begins, Classical Science Stops”
James Gleick
We need to set references that allow us to develop habits and checkpoints. From our traditional vision, we tend to assume that this is achieved only thanks to a centralized, vertical, top-down and mandatory system of control and coordination. If not, we believe that the collective and its members will behave in a totally inefficient chaotic way. We associate order with static equilibrium, a complacent and almost dying form of order. On the contrary, the self-organization of complex systems represents a paradigm shift in which apparently chaotic behavior is understood as a pattern of order that emerges from the bottom-up or in a flat network, thanks to the interactions between the elements of the system. Chaos should not be confused with randomness. The first is a pattern of dynamic order in evolution subject to rules, while the second is capricious, untamed, without a definite pattern. Likewise, we must not confuse the complex with the complicated. A complex system with ordered chaotic patterns of behavior is governed by simple and adaptable rules. It is the simplicity of the complex. What are the requirements that must be met in a system for it to self-organize and chaotic order patterns to emerge? We can summarize them in six:
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Talking about complexity and Chaos Theory is talking about fractals, geometric objects whose basic structure, fragmented or apparently irregular, is repeated at different scales and sizes. Many natural structures are fractal-like: trees, coastlines, a snowflake, blood and lung vessels, etc. The Mandelbrot set is possibly the most studied fractal. It is generated from a simple recursive sequence of complex numbers
But beyond geometry, nature, through extremely simple rules of association, also allows the wealth of behavioral complexity to be generated, such as ecosystems or animal colonies. Let’s think of a flock of birds. The flight of the flock is a pattern of dynamic and chaotic order. What are the rules that govern it? Ethologists reduce them to just three: don’t bump into anything, keep up and stay in close proximity. It is enough for the group to organize itself in this way with a common supra-individual objective, such as migration, defense against predators or foraging. The self-organizing systems of living beings emerge thanks to the learning of behaviors by imitation within a culturally or biologically linked group. If in these systems at lower levels there is order within disorder and effective work overcoming the fluctuations of the environment, why in human organizations are not we able to do it so easily? The human mind tends to turn its back on nature and we think that our inventions, due to their rationality and perfectionism, will be superior. Have you observed that in creations conceived by mankind, straight lines or perfect curves (circles, spheres) predominate, while that in nature is not so? Now that there is so much talk about artificial intelligence, it would be wise to be less arrogant as a species and learn more about natural intelligence.
There is no strict principle of superposition (additivity) or proportionality. “So much I give” no longer equals “so much I get”. Efficiency and productivity are not guaranteed by individuals doing more or faster. The elements or nodes of the system are connected in a network where feedback mechanisms are established that result in interferences or synergies. Relationships are not one-way. Each bird in the flock, to comply with the rules, must be able to turn, brake, accelerate. Traditionally we tend to create simple, linear and closed systems and models, since we can understand, design and control them more easily. But by doing so, we are just excluding the weakest components and connections, focusing only on the ones that seem stronger and more influential. However, when the relationships are not linear and the network is hyperconnected, any of the connections, irrespectively how weak it may seem, can have a strong influence on the system as a whole. This is known as the “butterfly effect”: small changes, big effects; the apparently insignificant becomes the protagonist; Any entity in the network can exert significant influence.
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Individuals must establish sensory connections that allow them to be transmitters and receivers of information exchanged with the rest of the elements of the system and the environment, as well as channels to transmit their actions.
Complex systems far from equilibrium have a high amount of energy to dissipate. The supply of physical, psychic, emotional and spiritual energy in the individuals of an organization is essential for reaching a dynamic, adaptable and effective order.
As established by Chaos Theory, the “attractor” is a pattern that complex systems tend to reach after being subjected to a perturbation. They are the basis that sustains the emerging hidden order behind the chaos. In geometric systems, they are classified into four types: punctual, cyclic, toroidal, and strange. The first three follow the three spatial dimensions (line, plane, volume), while in the strange attractor the fourth spatio-temporal dimension governs. Therefore, an evolutionary and adaptive dynamic order, that is, self-organization, requires a strange attractor. The attractors in geometry are a set of numerical values, but what would be their translation in complex systems of human organizations? The point attractor would correspond to the Feeling: we anchor ourselves to what we like. The cyclical attractor, with Thinking and Intuition: after rational or heuristic analysis, we seek the synthesis of more than one perspective. The toroidal attractor can be similar to a myriad of sensations or perceptions (Sensing): episodes to which we are subjected periodically. Although they may harbor a certain degree of complexity, these three types of attractors create predictable order patterns and are said to be stable attractors. On the contrary, the strange attractors allow us to escape from determinism and enter the unpredictable. Its correspondence could well be the Will: the power to decide and order our own conduct. Organizations must create and sustain a collective imagination that allows aligning wills in favor of a common good superior to the individual. We can talk about the vision, the mission, the strategy. But in a scenario of unpredictability and volatility, visions turn into illusions, the long-term strategic plan becomes impossible, and forward-looking analysis loses its primacy. More important than all this are the culture and the values in which the rules of behavior aforementioned are framed. (“Culture eats strategy for breakfast”, Peter Drucker). However, in order not to become a stable attractor, the organizational culture must not be unique, homogeneous, solid, or prescriptive, but rather loose, heterogeneous, liquid and leaving room for dissent and amendment. Attractors are cohesive elements that order and shape individual behaviors in favor of a higher collective function of the system. Nevertheless, systems, in order to evolve and adapt, must adopt mechanisms of change within their attractors scheme. This is what is known as bifurcations: a transition from one state to another qualitatively different that occurs when small and subtle changes in some of its parameters or characteristics occur in the system.
The elements (nodes) of a system and the connections established between them structure a network. The properties of a system are not only determined by the characteristics of each of its nodes and connections, but also by the architecture of the network they conform. Thus, a centrally designed and controlled network structure results in a hierarchical system in which the elements are highly specialized in specific functions. This can lead to more effective systems from the productive point of view, but also more fragile and vulnerable to environmental turbulence, since the control mechanisms are concentrated in a few elements. On the contrary, the structure of self-organizing systems is decentralized and more evenly distributed through its nodes and connections, promoting the appearance of clusters that together give rise to more robust and adaptable systems, which can cope more efficiently to external shocks and unforeseen events.
“Order arises from Chaos (…) We grow in direct proportion to the amount of chaos we can sustain and dissipate”
Ilya Prigogine
Summing up,
In next article, step 4 in the WAKER Path, we will propose some guidelines for action (experimentation) that may help us navigate this transition towards a self-organizing model in which order emerges thanks to pushing the system towards the edge of chaos.
UPCOMING ARTICLE OF CHAPTER 1: COMPLEXITY MANAGEMENT
“A navigation guide for organisations sailing on the edge of chaos”