Simply Complexity | Neil Johnson

Summary of: Simply Complexity: A Clear Guide to Complexity Theory
By: Neil Johnson


Embark on a fascinating journey through the world of complexity theory with Neil Johnson’s ‘Simply Complexity: A Clear Guide to Complexity Theory’. This book summary explores the intricacies of complex systems and how seemingly unrelated phenomena, such as traffic jams and market crashes, are interconnected. Delve into the realms of chaos, why individual differences tend to cancel each other out in group dynamics, and the pivotal role networks play in feeding back information across the population. Complexity science not only empowers us to unravel the mysterious patterns in wars and human relationships, but it also holds the key to resolving problems across various disciplines.

Unraveling the Complexity of Our World

Complexity science is the study of emerging phenomena from interacting objects. As competition between objects increases, complexity arises, giving way to conflicts and problems. Complexity science draws on other sciences such as biology, sociology, and ecology to solve these problems by forming hidden connections between complex systems. It has widespread potential applications in different fields and can expedite our understanding of complex systems in other disciplines. As a result, it may be extremely important in our daily lives. Examples of complexity can be seen in traffic jams, financial markets, and even cancer tumors, demonstrating the pervasive nature of complexity in our lives.

The Magic of Self-Organizing Complex Systems

Complex systems are fascinating as they can cause unpredictable phenomena such as traffic jams or market crashes without any central regulation or coordination. Instead, these systems are self-organized, making events seem almost magical. These systems are able to bring about changes in their own behavior, leading to a range of outcomes from random to extreme. Feedback is a critical aspect of complex systems; it refers to a past event affecting something in the present, which leads to order or disorder. For instance, information flow in financial markets or traffic on a road affects the behavior of traders or drivers, leading to unpredictable outcomes. Complex systems appear “alive” due to the complicated interactions among the agents. Understanding these systems can help us deal with several issues in our society more effectively.

Complexity Vs. Chaos

Complexity and chaos are often used interchangeably, but they are not the same. Chaos is a specific example of the output of a complex system. Complex systems might display chaotic behavior, but they may also show periodic or even static behavior. In other words, complexity does not necessarily imply chaos. Chaos is complicated, not complex, because even rules can create chaos. A complex system is more complicated than the behavior produced by applying a mathematical rule over and over. What makes a complex system truly complex is the interaction that takes place within it and the way it switches between different behaviors caused by feedback. Therefore, chaos doesn’t necessarily imply complexity and complexity doesn’t necessarily imply chaos.

Unpacking the Complexities of Group Behavior

A group is a complex yet robust phenomenon that cancels out individual differences and exhibits similar behavior regardless of location, language, and culture. In group situations, opposing personality types tend to cancel each other out. This concept is particularly evident in reality TV shows like Big Brother and Celebrity Big Brother, where typical human group dynamics play out regardless of the celebrity’s individuality. Similarly, in financial markets, the behavior of traders in one market shows remarkable similarities to groups of traders in another market. In essence, group behavior is a collective phenomenon that follows predictable patterns, regardless of the individual differences it comprises.

The Power of Networks

Networks are all around us; we create them as social animals, forming alliances and coalitions that reflect who interacts with whom and how they do so. Networks rely on nodes and connections, providing feedback that creates complexity. Studying these networks can be lifesaving, as they play a role in the distribution of life-giving nutrients, the transmission of viruses, and the development of complex systems in biology. Medical practitioners can use this understanding to diagnose and treat conditions like cancer tumors, AVM, or stop the spread of viruses. Understanding social networks is crucial as we are in a constant cycle of creating and contextualizing our relationships. Networks tell a story of information being passed from one point to another, and it shows how feedback in any system is the key component for complexity. By studying networks, we can improve our understanding of how complex systems work and affect the world around them.

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