Written by Robin Gu, a researcher at X-Order, an innovative research institute that attempts to combine cross-disciplinary fields such as distributed computing, computational game theory, artificial intelligence and cryptography to discover future extended orders. It was founded by Tony Tao, who is also a partner at NGC Ventures.
“If we try hard enough to reduce anything to pure logic — for example if we try to pin down a final meaning of such concepts as Truth, or Being, or Life, or if we try to reduce some field such as philosophy or mathematics (or economics for that matter) to a narrow set of axioms — such attempts founder.” — Brian Arthur
Prologue
In the previous articles “Ponzi Structure and the Law of Expansion”, “Capital Order and the Structure of the Extended Layers” and “Historical Instances of Ponzi Structures and their Impact”, we analyzed the characteristics of extended order in the economics field in detail. However, it does not only exist in economics, but similar phenomena have also been discovered in various disciplines such as physics, biology, and ethology. The evolution of human fits well with such continuous extended order. We believe that these findings imply some more generic patterns. In our continuous explorations, we found that there is an area of academic research that is very close to what we aim to understand — complex system theory, more accurately, Complex Adaptive System theory.
In order to understand the concepts of the complex system, we have to first introduce the theory of Reductionism. In modern science, all branches of study are based on Newtonian Mechanics, using reductionism to study their respective subjects, and axiomatic theory (at least pursuing the usage of such theory) to explain nature.
Reductionism holds that “if you understand the various parts of the whole and the mechanism that integrate these parts, then you would understand the whole”.
However, analysing and deducing complex behaviour, in reality, is likely to fail; especially the analysis of a system composed of many possible interactive units such as the application of reduction-analysis. This is because the characteristics of micro-units do not necessarily represent the patterns of the whole system, such as cells in the organization, individual shareholders in the stock market, and drivers in the urban traffic system.
On the other hand, the study of complex systems regards collective (or system as a whole) behaviour as the basic subject of study. It is a scientific method to study how the relationship between components of a system causes its collective behaviour and how the system interacts with the environment, forming a relationship.
As an interdisciplinary field, complex system has received contributions from many different fields. For example, the research of self-organization in physics, spontaneous order in social sciences, the chaos theory in mathematics, the adaptation theories in biology and so on. Therefore, complex systems are often used as a broad terminology, covering many research methods of different disciplines.
Complex system theory originated from the system theory in the 1930s. However, its development period has only been maybe forty or fifty years to date.
Many Phenomena, in Reality, have some Characteristics of Complex Systems
The ant colony is a well-known complex system in biology. Every individual ant interacts with other ants and the environment in a simple way. Yet, individuals are unaware of the behaviour of the whole ant colony. If we look at it from a holistic perspective, we will find that ant colony, as a collective, can accomplish many very complex behaviours, such as jointly lifting objects many times larger than a single ant, or crossing ditches with their bodies used as bridges. The ant colony, an intelligent organism, has been given an interesting title, AuNT.
The Matthew effect in economics is a result of equilibrium in complex systems. In the system, resources are centralized in a specific direction, and continuous iteration enhances the centralization of resources, resulting in an effect that the strong will stay strong. This is described by Brian Arthur as the Law of Increasing Returns in economics.
The well-known butterfly effect is also a manifestation of complex system theory. An initial small change can lead to a huge difference in the final result.
These instances tell us that since the society we live in is a complex environment in itself, only a more networked and systematic approach to the study of this environment will enable us to better fully uncover the law of society as a whole.
From Complex System to Complex Adaptive System
There are two layers to the meaning of the word “complex” in the complex system.
The first layer is the subject of interaction. By following some simple rules, as long as there are enough subjects, the order will inevitably emerge in the system. However, due to a large number of subjects, small probabilistic events such as some changes in the number of subjects or the contingency caused by the interaction process will inevitably occur.
Hence, the whole of a complex system is larger than the sum of its individual units. It makes the macroscopic order of the system unpredictable at the microscopic level by any subject.
The second layer is that the simple rules set up initially will change the system’s environment as the participation of subjects increases. In such a process, due to the continuous interaction with the environment, simple rules will spontaneously form “survival of the fittest” behaviour according to the endogenously increasing complexity.
This is the Complex Adaptive System we want to emphasize.
Rules are constantly changing themselves in order to better adapt to the impact of changes in the historical environment.
To put in simpler terms, in general, every subject in a complex system follows rigid rules. They are more like small logical modules, which do not contain “memory”. While in a more developed Complex Adaptive System (CAS), the subject is adaptive. They are intelligent units with “memories” that modify their response logic based on past experiences. Sand dunes, thunderstorms and atmospheric molecules in nature are generally complex systems, but not CAS.
In part 2, we will discuss the link between this set of Complex Adaptive System theory and the actual economy, and what we can learn from it.
References:
1. Complex System — Wikipedia
2. Complexity and the Economy — Brian Arthur
3. Complexity: The Emerging Science at the Edge of Order and Chaos — Mitchell Waldrop
4. The Engine of Complexity — John E. Mayfield
5. The Fatal Conceit — Hayek
