Polymath as Complexity

I have been enamored with Complexity Science since I learned about it several years ago. Complexity science stands out from other sciences due to its focus on understanding the behavior of complex systems, which are made up of many interconnected parts that interact in non-linear and unpredictable ways. This resonates so much with how I perceive the world!

The reason is simple: Complexity is at the core of my polymathic nature. As I contemplated this idea recently, along with my other metaphors like "Polymathy as Gesamtkunstwerk" and "Polymathy as Permaculture," I thought of sharing my perspective on polymathy through the lens of Complexity Science.

I became curious: what if I set a polymath on a journey through various fundamental concepts that underpin Complexity Science? It revolves around key concepts such as emergence, self-organization, adaptation, nonlinear dynamics, feedback loops, networks, criticality, robustness, resilience, scaling laws, and information and complexity measures.

Now, let's explore how diverse multidisciplinary professionals can manifest these concepts in their respective fields.

• Emergence: As a polymath delves into different fields and integrates knowledge from various domains, they may discover novel patterns or ideas that arise from the interactions between their diverse expertise.

Example: A polymath with a background in biology and computer science might uncover new insights into bioinformatics, leading to the emergence of computational tools for analyzing biological data.

• Self-Organization: A polymath's pursuit of knowledge is often driven by curiosity and intrinsic interest. Their learning process might be self-organizing, as they explore different topics without strict external guidance, leading to a coherent and integrated understanding of various subjects.

Example: A polymath interested in art, history, and philosophy might find themselves naturally drawn to studying Islamic Golden Age or Renaissance period, where artistic, scientific, historical, and philosophical elements intertwine.

• Adaptation: Polymaths are adaptable learners, capable of navigating diverse challenges and dynamically adjusting their approach to solving problems in different disciplines.

Example: An environmental scientist who is also well-versed in economics might adapt their research methods and policy recommendations to address complex environmental issues in a way that considers economic constraints and incentives.

• Nonlinear Dynamics: The application of knowledge from one field to another by a polymath can lead to non-linear effects, where small insights or innovations in one domain can have significant impacts on a seemingly unrelated field.

Example: A mathematician who also specializes in linguistics might develop novel algorithms for natural language processing, resulting in a breakthrough in machine translation and revolutionizing the field of artificial intelligence.

• Feedback Loops: Polymaths often engage in feedback loops of learning and knowledge acquisition. Insights gained from one domain can influence their approach and understanding in another domain, leading to continuous improvement and refinement of their expertise.

Example: A psychologist with expertise in neuroscience might apply insights from brain imaging studies to gain a deeper understanding of cognitive processes, which, in turn, informs their approach to therapy techniques.

• Networks: A polymath's knowledge can be represented as a network of interconnected nodes (disciplines) and edges (relationships between disciplines), illustrating how different domains influence and complement each other.

Example: An architect with a background in engineering, psychology, and environmental science may create a network of knowledge that guides them in designing sustainable and human-centered buildings.

• Robustness and Resilience: A polymath's diverse skill set enhances their ability to withstand challenges and adapt to changing circumstances, making them more robust and resilient in their pursuit of knowledge and problem-solving.

Example: An economist with expertise in public health and policy analysis can offer robust recommendations to governments during times of crisis, such as a pandemic, by considering the intricate interactions between economic and public health factors.

• Scaling Laws: Polymaths' capabilities can scale with the complexity of the problems they tackle, enabling them to tackle larger and more intricate challenges.

Example: A polymath in computer science and cybersecurity might design scalable algorithms to analyze massive datasets, addressing the challenges posed by the growing complexity of cybersecurity threats.

• Information and Complexity Measures: Polymaths may use various complexity measures to assess the depth and breadth of their knowledge, identify knowledge gaps, and prioritize areas for further exploration.

Example: A data scientist with knowledge of information theory might use measures like entropy and algorithmic complexity to analyze patterns and structures in complex datasets, leading to more insightful data-driven decisions.

And you know what is most interesting? Similar to Systems Theory that was developed by polymaths, Complexity Science is a child of probably the most polymathic academic institution in the world, the Santa Fe Institute (SFI) in the United States. The SFI's unique interdisciplinary environment and research focus have made it an ideal hub for exploring the complexities of the natural and social world.

It's president and William H. Miller Professor of Complex Systems, an Oxford-trained polymath David Krakauer happen to be one of my favourite modern intellectuals. His research explores the evolution of intelligence on earth. This includes studying the evolution of genetic, neural, linguistic, social and cultural mechanisms supporting memory and information processing, and exploring their generalities. At each level Krakauer asks how information is acquired, stored, transmitted, robustly encoded, and processed. This work is undertaken through the use of empirically supported computational and mathematical models. Fascinating, isn't it?..

Keep discovering polymathic aspects of our complex life in WHY POLYMATHS?