An introduction to Complexity

My first exposure to complexity and complex adaptive systems came from a podcast interview with Bill Gurley, a renowned venture capitalist. During the interview, he mentioned the significant impact that W. Brian Arthur's article on increasing returns and lock-in had on his investment philosophy. He also referred to the work of the Santa Fe Institute on Complex Adaptive Systems and recommended the book "Complexity: The Emerging Science at the Edge of Order and Chaos" by M. Mitchell Waldrop. Intrigued, I decided to explore further, leading to a profound transformation in my investment philosophy and long-term business thinking. Here, I aim to provide a snapshot of what I've learned.

What is a Complex Adaptive System?

Complexity science focuses on the study of complex adaptive systems, which are systems where the parts or agents interact with each other to generate emergent behavior. Such systems are considered "complex" due to the vast number of interactions that can occur, making the behaviours challenging to understand or predict in advance. They are "adaptive" because the agents and their interactions are influenced by the environment through feedback loops. Another characteristic of complex adaptive systems is their nonlinearity, meaning that small changes can lead to significant behavioural shifts.

Examples of complex adaptive systems include biological ecosystems, where various species interact and give rise to emergent behaviours like the profoundly positive impact of the reintroduction of wolves on the Yellowstone National Park ecosystem.

Complexity science draws from multiple disciplines such as mathematics, physics, biology, computer science, and economics. While still a relatively young field, it offers new perspectives on complex problems like climate change, economic crises, and urban development.

Application of Complexity Science to Real-World Systems

Complexity science has been applied to various real-world systems, including social systems, economic systems, ecological systems, biological systems, and even artificial intelligence and machine learning. Examples of its application include:

• Social Systems: Complexity science has contributed to the study of social phenomena such as riots, financial crises, and disease spread. It has also developed models of social network dynamics and collective decision-making.

• Economic Systems: Complexity science has been used to study economic phenomena like market crashes, bubbles, and financial crises. It has also contributed to models of economic growth and market competition.

• Ecological Systems: Complexity science has enhanced our understanding of ecological phenomena such as food webs, predator-prey interactions, and population dynamics. It has also helped develop models for ecosystem management and conservation.

• Biological Systems: Complexity science has shed light on biological phenomena like cell communication, gene regulation, and the immune system. It has also aided in modeling drug development and disease treatment.

• Artificial Intelligence: Complexity science has been instrumental in studying artificial intelligence, machine learning, and cognitive architectures. It has contributed to the development of neural networks and understanding their behaviour.

Introduction to W. Brian Arthur and Increasing Returns

The Santa Fe Institute is a renowned nonprofit research and education center located in Santa Fe, New Mexico, USA. Established in 1984, the institute is home to leading researchers in the field of complexity science. Through their research, they have provided valuable insights into the workings of complex systems and effective management strategies.

Introduction to W. Brian Arthur and Increasing Returns

W. Brian Arthur is a key figure in complexity science, and his work on increasing returns to scale is crucial for understanding the success of certain technologies (e.g., QWERTY keyboard, Microsoft Windows and iPhones) and online platform business models (e.g., Google, Meta, and Amazon).

Increasing returns refers to the idea that a technology or business can become the dominant player in an industry or area due to positive feedback loops and negative externalities for competitors. For instance, the QWERTY keyboard became the standard configuration for PCs and laptops due to economies of scale, user adoption, and education. Arthur's work reveals that small changes can have significant effects, and feedback loops can amplify these effects, leading to rapid evolution towards a particular state.

Arthur's insights highlight that technologies evolve and emerge by building upon earlier ones rather than spontaneously appearing. He also emphasises that technological systems are fractal, meaning each new layer or subsystem is a scaled version of a previous one. This suggests that breakthroughs in multiple technological layers or subsystems are often necessary for new emergent or evolved technology classifications.

To learn more about W. Brian Arthur and his work, I recommend starting with his classic paper on increasing returns titled "Increasing Returns and the New World of Business," published in the Harvard Business Review in 1996. Additionally, you can listen to this recent podcast where he discusses the nature of technology.

Introduction to Geoffrey West and Scale

Geoffrey West, a theoretical physicist and faculty member at the Santa Fe Institute, focuses on understanding how complex systems work. He has studied the growth of animals, plants, cities, and ecosystems, finding that they all follow similar patterns described by mathematical equations known as power laws.

In West's research on cities, he discovered that many aspects of city life exhibit power law relationships. For example, the amount of money generated by a city relates to its size through a power law equation. Additionally, certain elements like a city's infrastructure (e.g., road networks) grow at a lower rate relative to population growth due to efficiencies of scale.

Conversely, the number of patents or average salary scale non-linearly with population growth, increasing rather than decreasing. Negative elements such as crime and disease also tend to increase non-linearly as a city's population grows.

These insights suggest that a city's prospects are influenced by its population growth, and as a city becomes more important in terms of overall population share, the opportunities for business increase. Additionally, there may be increasing returns-type power laws at play for desirable suburbs within a city.

West's work also highlights the need for innovation or technology to sustain growth in any entity, be it a city, economy, or company. To extend the growth trajectory, a breakthrough innovation or product line is required to "stack" another S-shaped curve onto the existing one. Without breakthroughs, growth eventually slows down, and entropy takes hold. The acceleration of technological breakthroughs in the digital age shortens the useful shelf life of companies, driving the need for more frequent breakthroughs.

How Complexity has Influenced My Thinking on Investments and Business

Complexity has profoundly influenced my thinking as an investor and entrepreneur, shaping the work I do with owner operators on a daily basis.

It has provided insights into the inherent advantages of certain technology businesses, such as SaaS products, digital platforms, and online marketplaces. The unstoppable flywheel effect generated by effectively harnessing network effects, access to data at scale, and self-reinforcing algorithms offers commercial advantages. It has also prompted me to question the viability of traditional distribution channels and contemplate the evolving nature of the value chain in industries like travel and retail.

From an investment perspective, complexity has driven me to seek businesses with key characteristics such as slow growth resilience in fragmented industries, growing addressable markets due to underlying demographics, digital assets that can scale with zero marginal costs, potential for increasing returns to scale, lowest cost operators, platform business models that can spawn new business lines, emergent technologies built on existing layers, and solutions to real-world problems in sectors like food security, housing, banking access, and healthcare. Additionally, I prioritise companies that create non-zero-sum outcomes for themselves and their broader ecosystem or industry.

Moreover, complexity has inspired me to develop a deeper understanding of how artificial intelligence and machine learning can unlock value across industries like travel, commerce, healthcare, education, and agriculture.

Long-Term Business Strategy: Seeking Non-Zero-Sum Outcomes

In a complex adaptive system like the travel industry, numerous agents (individuals, OTAs, airlines, accommodation providers, governments, ecosystems) interact to produce outcomes.

These agents can be considered nodes in a network, and their connections represent the relationships between them. The connections can take various forms, and agents can have different types of relationships with each other.

The term "non-zero-sum" refers to a situation where the total value of the system exceeds the sum of the values of its individual parts. In other words, the whole is greater than the sum of its parts.

An example of a business actively pursuing non-zero-sum outcomes is Patagonia, an outdoor retailer founded with a mission to build the best products, minimise harm to the environment, and inspire and implement solutions to the environmental crisis. Patagonia's commitment to giving one percent of its sales to environmental groups through organisations like 1% for the Planet, its sustainable practices, and the establishment of the Patagonia Purpose Trust to protect its values and missions all exemplify its pursuit of non-zero-sum outcomes.

The semiconductor industry also exhibits characteristics of a non-zero-sum complex adaptive system. Due to the increasing complexity of semiconductor manufacturing, specialisation has thrived, and key players in the industry depend on each other, resulting in above-average operating margins throughout the value chain. Technological advancements in semiconductors require collaboration among multiple companies years in advance.

To gain a deeper understanding of complexity investing and the semiconductor industry, I highly recommend exploring materials from NZS Capital, including their YouTube videos and white papers.

Complexity has significantly influenced my investment philosophy and long-term business strategy. It has allowed me to identify the advantages of certain technology businesses, consider the dynamics of complex systems, and seek non-zero-sum outcomes. By embracing complexity, I aim to navigate the ever-evolving landscape of investments and business with a deeper understanding of the underlying principles at play.

-Komorebi