I’m taking several weeks off to focus on refining my vision for advancing systems research. This means less casual exploration of ideas, and more focused theoretical work, technology development, and coordination with collaborators.

You can see my recent working paper: A Deep Systems Analysis of Bitcoin for a small glimpse of the direction I’m headed in.

For my own reflection — and to make my journey through the world of systems up until this point a bit more accessible — I’ve created a catalog of every post I've written. It’s organized into six general categories:

1. Foundations & Philosophy of Systems

2. Complexity Science

3. Historical Traditions & Key Figures + Organizations

4. Applied Systems Research

5. Methods, Tools & Learning

6. Personal Content and Reflections

Thank you to all of my readers and subscribers for your support. I’ll return to publishing when I have something important to say.

The System Explorers Catalog

1. Foundations & Philosophy of Systems

Core concepts and philosophical aspects of systems science

• What is Systems Science?
  Overview of systems science as a meta-discipline examining patterns across systems of all types, with discussion of its current state and potential.

• Systemness
  Exploration of the first principle of systems science: what makes something a system versus a mere collection of parts.

• Process and Hierarchy
  Introducing two fundamental concepts in systems science: processes and hierarchical organization.

• Networks
  Introduction to key network principles like nodes, edges, hubs, and centrality as tools for understanding systems.

• Dynamics
  Introducing the concept of dynamics in systems.

• Complexity
  Defining complexity in systems, distinguishing complex from merely complicated, discussing agent-based modeling as a key analytical tool.

• Evolution
  Introducing evolutionary principles and processes as they apply to complex systems.

• Information and Knowledge
  Introducing the key systems concepts of information theory and knowledge creation.

• Governance
  Introducing the key systems concept of governance in systems.

• External Models
  Introducing the key systems concept of external modeling.

• Self-models
  Introducing the key systems concept of self-modeling.

• Understanding
  Introducing the notion that systems can be understood.

• Improvement
  Exploring the notion that systems can be improved.

• A Mathematical Definition of System
  Formal mathematical approach to defining what constitutes a system.

• What Can We Know About Systems
  Exploration of observation limits in systems research, examining how individual perception affects our understanding of systems.

• Systems: Science and Sciences
  Examining the lack of clarity around systems science’s boundaries and basic terminology.

• Boundaries in Systems Science
  A reflection on the concept of boundary after an ISSS meeting.

• Categories?
  Early impressions of category theory and its potential relevance for systems research.

• Organization
  The role of organization in systems.

• Formalizing System Ontologies
  Introducing a prototype formal ontology of systems.

• Thoughts on General Systemology
  Reflections on the field of general systems theory and its potential development.

• Early Adopters?
  Exploring the idea that we are in the early adoption stage of systems science and its implications.

• Systemism as Ideology
  Introducing “systemism” as a meaningful intellectual movement and ideological framework.

• Spreading Systemism with an Idea Machine
  Strategies for promoting systemism more widely in society.

2. Complexity Science

Posts on complexity theory, emergence, and complex adaptive systems

• Complexity (2024)
  A personal poem reflection on change and adaptation as fundamental constants in complex systems.

• A Quick Note on Complex Adaptive Systems
  Clarifying a key point about complex adaptive systems.

• Exploring Emergence in Artificial Life
  Reflections on a paper that studies emergence in the context of artificial life research.

• The History of Complexity
  Thoughts on the importance of the Sante Fe Institute's collection of foundational papers in complexity science, highlighting key historical works by Friedrich Hayek, Warren Weaver, and Herbert Simon.

• The History of Complexity P2
  Additional brief thoughts on complexity science history.

• Why "Complexity" Science is Poorly Named
  Evaluating a Sante Fe Institute co-founder’s critiques of Complexity Science.

• The Complexity-Systems Divide
  Introducing a few conceptual and practical differences between the fields of complexity and systems science.

• Reflections on Science and Complexity
  Reflecting on a foundational paper in the study of complex systems.

• Exploring Wonderland
  Highlighting a series that creatively explores complex systems concepts through metaphor.

3. Historical Traditions & Key Figures + Organizations

The intellectual history and evolution of systems research

• On Hayek and Bertalanffy
  Analysis of the intellectual connection between Friedrich Hayek and systems theorist Ludwig von Bertalanffy.

• Hayek's System-Theoretic Social Theory
  Examination of the direct influence of Bertalanffy’s systems concepts on Hayek's economic and social theories.

• Looking Backwards to Revitalize Systems Science
  The importance of understanding the history of systems research to meaningfully influence its present and future.

• The Systems Sages
  Profiling seven influential systems thinkers spanning multiple generations, from Ludwig von Bertalanffy and NorbertWiener to contemporary figures like Elon Musk and Vitalik Buterin.

• Notes on Bell Labs
  Historical analysis of Bell Labs' systems-oriented research approaches.

• Notes on Xerox PARC
  Historical analysis of Xerox PARC's systems innovation approach and impact.

• Notes on Protocol Labs
  Identifying useful lessons modern systems researchers can learn from this research organization's structure and approach.

• Notes on Von Foerster's Notes
  Reflection on Von Foerster's epistemological approach to living systems, exploring his ideas about observation and knowledge creation.

• Notes on General System Theory
  Personal notes on Bertalanffy's foundational work on general system theory.

4. Applied Systems Research

Real-world applications across domains, including socio-technical systems and cryptoeconomic applications

• What is a Cryptoeconomic System?
  Defining and characterizing cryptoeconomic systems.

• The Value of Cryptoeconomic Systems
  Arguing that cryptoeconomic systems are uniquely valuable for systems science research because of their permissionless, transparent, open-source, and programmable nature.

• Bitcoin's Emergent Properties
  Framing Bitcoin as a complex adaptive system with emergent properties.

• Measuring Complexity in Blockchain Systems
  A research framework for quantifying complexity in blockchain systems.

• Cryptoeconomics in the Second Era of Trump
  Discussion of how the Trump administration’s policies are likely to influence the development and adoption of cryptoeconomic systems.

• Complex Webs of Power
  Network science applied to study geopolitical dynamics and hegemony in the 21st century.

• The Power of Prediction Market Systems
  Introducing the concept of prediction markets and their information aggregation capabilities.

• Economic Systems as Processes
  Introducing a process-oriented and algorithmic perspective on economic systems and their dynamics.

• Markets & Society
  Highlighting a new academic journal in political economy.

• Seeing Cities as Systems
  Introducing a powerful tool for applied systems work in local government.

• Thoughts on the U.S. Political System
  A systems perspective on American political structures and dynamics.

• Systems Science and Artificial Intelligence
  Exploring the relationship between systems science and AI development.

• Why Conduct a Systems Analysis of Bitcoin?
  Rationale for applying systems science methodology to Bitcoin.

• Collecting Data on Cryptoeconomic Systems
  Exploring methodological approaches for gathering and analyzing cryptoeconomic data.

• Will Blockchains Dream of Electric Sheep?
  Speculative exploration of emergent intelligence in blockchain systems.

• Transcending the von Neumann Architecture?
  Engaging with von Neumann’s thoughts on the study of self-reproduction and their relevance for modern computing.

5. Methods, Tools & Learning

Research approaches combined with teaching and communication

• Distinguishing Basic and Applied Science
  Discussing why systems science needs to distinguish between balance curiosity-driven basic research and problem-solving applied research.

• Comparing Methods in Systems Research
  Comparative and contrasting the empirical and deductive approaches to theoretical systems science.

• Humanistic Systems Research
  Highlighting Bertalanffy's vision of systems research that centers human individuals rather than technological efficiency.

• Humanistic Systems Research (Part II)
  Further exploration of Bertalanffy’s human-centered systems approach.

• Reflections on The Role of Models in Science
  Reflecting on the role of modeling and modelers in scientific inquiry.

• On Agents and Architectures
  Exploring the question of where to place our focus when studying systems — the agents, or the structure they are embedded in.

• Exploring New Systems for Scientific Research
  Introducing ResearchHub as an innovative system for facilitating scientific research.

• Documentation
  The importance of documentation in systems work and research.

• How I'm Using ChatGPT
  Personal experience using ChatGPT as a research assistant, content editor, and software engineering tool.

• Systematic Self-Exploration
  Exploring how a systems approach might be applied to personal development.

• From Principles to Analysis
  Introducing a framework for conducting rigorous systems analysis from foundational principles.

• Pathways to Mathematical Systems Theory
  A syllabus for applying mathematical approaches like set theory and graph theory to systems analysis.

• Decoding A Black Box
  Exploring techniques for understanding opaque or complex systems without internal access.

• Exploring Networked Leadership
  Exploring different styles of leadership within networked organizations.

• A Research Update
  Video presentation on measuring complexity in cryptoeconomic systems research methods

6. Personal Content and Reflections

Personal reflections and community updates

• Welcome to Systems Explorers
  Introduction to the newsletter's purpose, focus on systems science, and community-building goals.

• The State of System Explorers
  Reflection on the newsletter's first ten weeks, lessons learned about writing on systems topics, and plans for future content and community building.

• My Hopes for the ISSS
  My desire to see greater collaboration, unification, and positive vision within the International Society for the Systems Sciences.

• Back to School
  Personal update on pursuing academic studies in systems science.

• (What I remember from) Xerox PARC
  A poem reflecting on my childhood experiences at the influential research center.

• Towards Truly Open AI Systems
  Ditching Open AI and ChatGPT out of a desire to move towards truly open artificial intelligence systems.

• Living with Broken Systems
  Personal reflections on the reality of living within failing systems.

• Systems Fail
  Reflecting on my personal experience post Hurricane Helene.

• Grappling with Chaos
  Reflecting on my experience in North Carolina post Hurricane Helene.

• A Pause
  Announcement of a brief hiatus in newsletter publication.

• Hope
  Reflections on feeling inspired and hopeful after a conference.

• Delegation
  Reflecting on the importance of delegation when working with groups.

• RFK Jr. and Systemic Change
  Reflections on the role of RFK. Jr. in U.S. political discourse.

• My Reading List for 2024
  Curated selection of recommended books and resources for systems research.

• An Abstract
  Research paper abstract on studying Bitcoin with systems scientific methods.

• Best of System Explorers: Part One
  Collection of highlighted posts from the newsletter's first period.

• Best of System Explorers: Part Two
  Continuation of curated highlights from the newsletter archive.

• Welcoming New Subscribers
  Introduction and orientation for new community members.

• Computational Tools and Modern Complexity Theory
  Overview of a school course focused on digital tools and methodologies for complexity research.

• Surveying System Explorers
  Surveying user interests and preferences.

• Join the system explorers chat
  Information about participating in the community discussion platform.

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Bell Labs mastered the art of turning curiosity driven achievements in basic science into profitable commercial products. Xerox PARC successfully created an environment where scientific innovation flourished, but failed to emulate Bell’s success in commercialization.

Today I’m sharing my notes on Protocol Labs, a modern organization whose story is still being written.

Open Source Innovation

Protocol Labs was founded in 2014 by Juan Benet in order to improve the internet and computing. More specifically, Benet, who studied computer science at Stanford, wanted to develop new protocols that would decentralize the web and cloud storage. He received $120,000 from Y Combinator to execute on that vision.1

The first component was IPFS (InterPlanetary File System), launched in 2015. IPFS is a free and open-source software project that allows users and applications to directly share information without needing a central server.2

The second component was Filecoin, launched in 2020. Filecoin allows users to pay others, in peer-to-peer fashion, to store data they find valuable.3

Protocol Labs research was formed in 2018 in order to work on “peer-to-peer transports as an effort to safeguard freedom of speech and assembly in a digital age.” Their work spanned a broad array of categories with researchers bound by a general belief that the internet is humanity’s most important technology. 4 5

Over the span of the lab’s six year history, they published papers and code in areas ranging from abstract category theory to applied decentralized identity before closing down in 2024. 6 7

Directly inspired by the success of Bell Labs, Benet steered the organization towards actively bridging the gap between basic curiosity-driven research and applied science.8 9

While Protocol Lab’s broad vision of maintaining an open and decentralized internet might sound fuzzy and idealistic, their technology has been put to the test. When the Turkish and Spanish governments attempted to censor their citizens by blocking access to Wikipedia, IPFS was effectively used to ensure citizens maintained open access to information.10

Like Xerox PARC, Protocol Labs appeared to have had some success in using a broad shared vision of the future to drive fundamental real world innovation.

The Token-Powered Laboratory

While I admire Protocol Labs’ work on decentralized digital systems, I suspect their innovation around business models offers the most valuable lessons for modern systems scientists. With the desperate need for greater funding towards basic systems research, those passionate about this work would benefit from greater awareness of novel funding mechanisms.

By 2016, the success of IPFS and the vision for Filecoin had attracted traditional funding from prominent investors like Union Square Ventures.11 This set the stage for a Filecoin token sale in 2017 in which Protocol Labs raised over $205 million from over 2,100 investors in 50 different countries across the world. This effort required innovation on multiple fronts, from pioneering a new type of legal instrument (the SAFT) to building a new platform (Coinlist) for companies to do legally compliant token sales.12

The story of Filecoin’s token sale provides a fascinating glimpse into how cryptoeconomic tools can be used to raise money and facilitate new types of investing and business models.

In short, Protocol Labs accomplished:

1. Writing a whitepaper advancing an ambitious technical vision and developing prototypes to prove competence

2. Selling cryptotokens in a public fundraising process to raise money for that vision

3. Building and deploying the Filecoin protocol, allowing the token to trade freely on markets

4. Maintaining a treasury of tokens (or “market protocol assets”) with market-determined value derived from the protocol’s utility to fund broader organizational efforts.

But what does Filecoin actually do? Filecoin is what Protocol Labs describes as a “market protocol” — a system that mediates economic activity, and settles value exchanges using a cryptographic asset like bitcoin or ether. The Filecoin protocol lets people pay for storage or retrieval with the FIL token and make transactions in peer-to-peer fashion, without handing over control of their data or sacrificing privacy.

This represents a clear and compelling example of how we might develop new business models capable of advancing Internet protocols in more fair and privacy respecting ways. The current dominant business model involves users getting free services from corporations like Google and Facebook in exchange for their personal data, which companies then sell to advertisers — making users the product. Models like Filecoin could help fulfill the original vision for the web: a decentralized network respecting user rights and freedom.

A breakdown of how raised funds were allocated is publicly available.13

Filecoin isn’t just a theoretical toy — it’s being used for meaningful applications across various domains:

• The Victor Chang Cardiac Research Institute in Australia is storing 137.439 TB of cardiac research data on the network 14

• UC Berkeley’s Orebi Gann experimental physics group is using Filecoin to store neutrino data captured from black holes and stars with their novel detector 15

• USC and Stanford have used Filecoin to cryptographically preserve evidence of war crimes, creating tamper-proof records16

• The City of New York is piloting Filecoin to create a more reliable, more resilient, more secure, and cost-effective archive of the city’s public records17

Perhaps most impressive is that IPFS is now truly "interplanetary"—Lockheed Martin has integrated IPFS technology into satellite operations, allowing for mission updates to be uploaded to spacecraft in orbit. 18

The Innovation Network

While Protocol Labs started as a research, development, and deployment lab for network protocols, it has evolved into something much more ambitious — an innovation network.19 Today Protocol Labs “connects over 600 organizations developing breakthrough technologies across computing frontiers including web3, AI, AR, VR, BCI, and hardware.”

This transformation reflects Benet's vision, directly inspired by Bell Labs. In talks about Protocol Labs' evolution, Benet distinguishes between traditional companies like Google, Intel, and Apple and what he calls "innovation networks" like Silicon Valley, Stanford University, and Y Combinator. He identifies an "innovation chasm" between early academic work and technology development — a middle ground typically poorly funded where promising innovations often remain trapped in papers or incomplete implementations.

Benet argues that centralized companies struggle to sustain significant R&D across multiple domains. In contrast, innovation networks create resource-sharing structures that aren’t dependent on a single business model. If one venture fails, the network continues thriving. This approach allows teams to move across different efforts without catastrophic consequences, distributes risk, and offers capital efficiency through shared infrastructure.

Today Protocol Labs operates as a fully distributed organization with no physical offices. Its international staff spans over 36 countries, with employees working from locations as diverse as Siberia, New Zealand, Iceland, Nigeria, and Argentina—a truly global collaboration network united by shared purpose rather than physical proximity.

Lessons for Systems Science

Protocol Labs offers several valuable lessons for anyone working to advance systems science:

1. Open source approaches can drive meaningful innovation when paired with sustainable funding models

2. Novel economic mechanisms like token sales can provide substantial resources for long-term research initiatives

3. Distributed, networked organizations can effectively coordinate complex work across geographic and disciplinary boundaries

4. A shared vision and values can align diverse contributors despite the absence of traditional organizational structures

While Protocol Labs Research officially closed in 2024, the broader innovation network continues to evolve. This transition from centralized lab to distributed network will provide valuable insights about the future of scientific collaboration and technological development for observers.

Unlike Bell Labs' government-sponsored monopoly or Xerox PARC's corporate backing, Protocol Labs pioneered a new model for funding ambitious technical work—one that directly engages a global community of contributors and stakeholders. For systems scientists concerned with fostering innovation across traditional boundaries, this approach deserves serious consideration.

The continued development of systems science will likely require similar creativity in how we organize, fund, and coordinate complex research efforts spanning multiple domains of knowledge. Protocol Labs shows us one potential path forward.

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“Xerox could have owned the entire computer industry, could have been the IBM of the nineties, could have been the Microsoft of the nineties." - Steve Jobs

I'm doing research into organizations that successfully drove breakthroughs in both basic and applied science in order to determine if there are any useful lessons to be learned for modern systems scientists.

Last week I shared my notes on Bell Labs and today I'm sharing my insights from reading about Xerox PARC.

Freedom of Research

PARC was founded in 1970 as a specialized research division of Xerox, which had established itself as a dominant force in the photocopying market.

Xerox’s CEO C. Peter McColough issued a call to turn the organization into the “architect of information” for the business office, and PARC’s founding research goal was to build that architecture. Throughout the 70s, researchers at PARC went on to invent many of the technologies that propelled humanity into the information age.

From developing Ethernet and LAN for standardized communication between computers, to introducing viable personal desktop computers, PARC’s researchers accomplished incredible feats guided by their high-level shared vision of “creating the office of the future.”

PARC’s founding research director, George Pake, held a set of core principles about managing research that were clearly conducive to the process of supporting creative invention.1

A few key ones included:

• Recruit the best and most creative researchers

• Don't command researchers to invent desired outcomes — describe general goals and solicit proposals

• Don’t be tight with spending — give researchers the most supportive environment including plenty of the most advanced technical instruments

Research scientists enjoyed tremendous freedom and there were no standard rigid hours. Projects weren’t assigned from above — researchers generated their own ideas and formed groups based on shared interest.

Lynn Conway, who helped facilitate a revolution in the process of computer chip design while at PARC, felt that the state of art computer networking technology researchers enjoyed endowed them with "powerful invisible weapons." Their environment empowered them “to outfox and outmaneuver people who would think [they] were crazy.”

Systems Builders in Action

"Systems research requires building systems" - Jim Mitchell (PARC computer science lab)

PARC started out with three separate labs: computer science, general science, and — to my incredible surprise — systems science. I had never heard of an organization, let alone a giant like Xerox having a dedicated systems science division.

Reflecting on his experience at PARC, Pake notes the extreme rarity of systems science departments in universities and even asserts "there really is not a discipline named systems science."

And yet, PARC’s systems science laboratory, in the absence of any solid theoretical foundations for the science, managed to play a pivotal role in bringing a futuristic vision of office computing to life.

Key inventions facilitated by the Systems Science Lab included: laser printers, the object-oriented programming (OOP) paradigm, ethernet, and LAN.

The lab also helped create the Alto personal workstation, a pioneering system which introduced features like graphical user interfaces (GUIs) with overlapping windows and clickable icons.

Another remarkable example of applied systems science at PARC was the work that Lynn Conway and Carver Mead did in creating a systematic approach to the design of integrated circuits — very large scale integration (VLSI).2

VLSI revolutionized the process of creating complex computer chips by making it more manageable, scalable, and accessible to a broader range of engineers and researchers. The key insight was shifting the focus away from individual components and towards methodologies and tools that enabled designing complete integrated systems on silicon.

While academic systems science struggled (as it still does) to find solid theoretical foundations and unification among its practitioners, PARC was doing applied systems science by using basic systems thinking concepts to solve very practical problems. Their researchers saw how a scattered array of hardware and software inventions could be treated as interoperable coherent wholes and be integrated in order to serve human needs.

Alan Kay, who worked on early portable personal computers, recounts a “blood oath” taken by original PARC founders: “never do a system that wasn't engineered for 100 users."

A Failure to Innovate

When Pake took on his role directing research at PARC, he told his supervisors that five years was too soon to expect research results, but there should be some useful commercial output before ten years.

Pake was remarkably accurate — profits from the laser printer gave Xerox a healthy return on its initial investment in PARC’s first decade of operations. Unfortunately this sort of success was a notable exception, not the rule. Xerox is well known for its failure to appreciate the potential market value of the various other technologies invented by researchers at PARC and commercialize them.

For example, despite support from Pake, Xerox rejected calls to open a personal computer division. Researchers who worked on the early versions of the Alto personal computer wanted to push an Alto III personal computer for mass market consumption, but the proposal was rejected.

Steve Jobs, who had received a $1 million investment in Apple from Xerox pre-IPO and tours of PARC for Apple executives as part of the deal, went on to use the ideas introduced by the Alto in the design of the original Macintosh computer. The Mac successfully, and very profitably, brought the personal computing desktop paradigm to the masses in 1984.

Xerox’s failure to innovate can be attributed to a couple factors worth briefly introducing.

Organizational Structure

In stark contrast with Bell Labs, Xerox was not structured in a manner that facilitated seamless flow from research inventions borne from basic scientific reason to the entrepreneurial innovation required to commercialize and bring products to market.3 4

Bell had specialized research development teams tasked with staying in touch with the cutting edge work being done by scientists and feeding promising commercial technology proposals to systems engineering teams. PARC’s researchers worked in isolation in their ivory towers for five years before a development organization was created in 1976 to take research prototypes and turn them into products.

PARC, located in Palo Alto, CA housed free wheeling scientists creating and working in the office of the future. Xerox corporate headquarters located in Rochester, New York housed corporate businessmen laser focused on ensuring that Xerox’s primary money machine, photocopying, stayed running.

There were high levels of bureaucracy, costly delays in decision making and an overall structure which prevented the easy flow of knowledge across PARC’s borders into the rest of the corporation.

Politics

There were toxic political dynamics at play as PARC researchers feared that the people making critical business decisions knew nothing about technology. Meanwhile many of the executives saw PARC researchers as strange bearded nerds who never left the lab and knew nothing about business.

There was also animosity and distrust between PARC research scientists and Xerox engineers. For example, when there was an attempt to extend knowledge about the GUIs developed by scientists outward to the rest of the company, many of the engineers found the work unintelligible and they couldn’t turn it into a marketable product. Scientists dismissed engineers as “toner heads” obsessed with copying machines while engineers found scientists to be unrealistic and arrogant.

One organizational theorist and civil servant, Myron Tribus, who joined Xerox after serving in the Nixon administration, recounted:

“I was used to the politics at Washington, but at Xerox it was way worse. In Washington, you knew your adversaries and accepted they would work against you. At Xerox, you only found out who was not on your side after you noticed the knife in your back.”

PARC has made for a fascinating case study. While the lab succeeded in inventing the office of the future, its parent company largely failed to innovate and commercialize the technological advances they were privy to.

Instead organizations like Apple, IBM, and Microsoft reaped the benefits and glory of bringing the computing revolution to the masses.

The contrast with Bell Labs who had the basic research to commercialization pipeline down to a science is remarkable. Another useful point of contrast is to note how with PARC, the scientific breakthroughs emerged in the fuzzier realms of computer science, information science, and consumer psychology, not just in the hard sciences as was primarily the case at Bell.

Next week I’ll be exploring Protocol Labs, a name which is probably unfamiliar to readers who aren't engaged with crypto, but one that should certainly command the attention of all systems researchers interested in the intersection of basic and applied science.

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One of the main barriers preventing the development of such a science is the lack of resources being devoted towards developing basic and rigorous theoretical foundations for the field. Without a solid set of shared theoretical foundations, I believe any claims about the potential for systems science to reliably serve society are unrealistic.

So I’ve been wondering — what useful lessons can those who believe in systems science learn from historical organizations that intentionally drove breakthroughs in fundamental science in the past?

I begin my exploration with looking at Bell Labs — the company responsible for connecting the United States via telephone during the 20th century. Bell served as the birthplace of notable scientific achievements including the creation of information theory, the discovery of the cosmic microwave background, and the quantum mechanical principles that enabled the transistor.

Here are a few standout qualities that contributed to Bell’s success.

Freedom of Research

Mervin Kelly started working at Bell as a physicist in 1925 and went on to serve as its president from 1951-1959. During his watch Bell pioneered technologies such as the laser and the solar cell. Reflecting on his career, he described the organization as an “institute of creative technology.”1

Kelly believed that basic research was the foundation which supported all advances in technology. He ensured that researchers had “freedoms equivalent to those of the research man in the university” and thought research should be a non-scheduled area of work with no deadlines, objectives, or progress reports.

Perhaps most importantly, he thought that scientists needed to confine their efforts to their research and not be pressured to worry at all about developing that research into something useful — otherwise they risked “losing contact with the forefront of their field.”

Division of Labor

Operations at Bell Labs fell into three categories.

30% of the staff worked in research and fundamental development. This is the department where reservoirs of basic scientific knowledge were created. Specialized development teams were responsible for identifying mature research and starting the process of connecting it to technologies that would serve the organization’s goals.

10% of the staff worked in systems engineering. Systems engineers played a crucial role in technical planning geared towards integrating the disparate knowledge from research, development, and operations. They would create studies appraising the potential of development of projects and drafting detailed plans for their implementation.

60% of the staff worked in specific development and designs. These workers carried out the designs crafted in systems engineering and were responsible for building labs, creating designs, and producing specifications.

Clearly, this setup worked well for Bell during their heyday. I wonder what modern systems science might learn about developing healthy feedback loops between basic and applied research.

Facilities

Kelly was very particular about how the physical environment was designed. For him, it was a key factor in creating conditions conducive for creativity and productivity. He embraced a functional building design that used flexible and modular rooms with a distinction between two types of functional spaces: labs/shops and offices. This allowed for granular control over room size and for the physical space to be rearranged quickly and cheaply. The flexibility of lab space made significant contributions to operations.

Takeaways

While there is a lot we can learn from the success of Bell labs and it’s a fun trip down memory lane, the authors of Bringing Back the Golden Days of Bell Labs, wisely emphasize the challenges any modern organization would face trying to recreate their success.2

Bell was the benefactor of a historically unique government sponsored monopoly held by their parent company AT&T. They had a very substantial money machine at their disposal which gave them freedom to make long term strategic commitments and have top notch scientists freely exploring their interests.

In the modern age where big tech companies have divisions spread across continents and people are accustomed to remote work, it’s a lot harder to bring everyone under one roof.

I would also note that Bell’s scientific researchers were heavily focused on pushing the boundaries of the physical sciences (physics, chemistry) and mathematics relevant to the hard engineering problem of electronic communications. Systems science aspires to find relevant laws and principles that extend into the very fuzzy realms of biology, psychology, economics, and political science. This certainly limits the extent to which practices from Bell are directly relevant.

Still, I think there’s a lot to be learned from studying Bell Labs. Next week I’ll share some notes on Xerox PARC.

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Note: This was originally written in response to an assignment for my course on the Collective Dynamics of Complex Systems at Binghamton University.

Warren Weaver's Science and Complexity is one of my favorite papers that I've read during my time in Binghamton's Systems Science program.1 Written in 1948, it feels as if Weaver is speaking directly to one of the core reasons I've become so enamored with systems — we need new scientific paradigms in order to effectively study social phenomena.

Weaver’s Framework

His core insight is that we can usefully distinguish between three eras of modern science, each concerned with separate classes of phenomena.

In the 17th century humanity started developing methods for dealing with problems of simplicity. These are problems where identifying the causal relationship between two or a few variables allows us to make meaningful and reliable predictions about physical phenomena. The physics and classical dynamics of the 19th century laid the foundations for technological developments from the telephone to the airplane.

During the 20th century we developed methods for dealing with problems of disorganized complexity. These are problems where we are dealing with a large number of variables, each of which has individually random behavior. However, using statistical techniques we can identify certain orderly and analyzable average properties of the system as a whole.

These methods allowed large telephone exchanges to make predictions about the average frequency of calls, and life insurance companies to maintain financial stability with their knowledge of the average frequency with which deaths occur.

However, in the mid-20th century scientists became increasingly aware of a third class of problem that doesn't neatly fall into either of the previous two categories. The second World War made it clear that we needed to develop reliable methods for dealing with problems of organized complexity. These are problems in which there are many variables involved, but they are interrelated in complex, non-random fashion. They show the essential feature of organization.

• How is it that simply re-arranging the organization of atoms in a molecule can turn a substance from being harmless into a poison?

• How exactly are the prices of commodities like wheat or gold determined?

• How can countries win wars, or secure global peace?

The methods of physics and statistics have not proven sufficient for answering these types of questions, ones that the modern biological and social sciences are most concerned with addressing.

Applications in Systems Science

Re-reading Weaver's paper for a second time, I'm inspired to reflect on some of the theories and techniques I've been exposed to for making sense of systems which demonstrate the feature of organized complexity.

Information theory, originally developed by Claude Shannon in 1948 to improve the efficiency and reliability of communication systems, such as telephones and telegraphs, laid the theoretical and technical foundations which allowed us to build the modern Internet and World Wide Web.

Information theory has been identified as a useful framework for quantifying levels of self-organization within complex systems.2 While I've explored how these concepts might be usefully applied to the study of blockchain systems, I still feel like I have many more questions than answers.

Set theory and graph theory were identified by Ludwig von Bertalanffy in 1968 as being promising frameworks for, respectively, axiomatizing the general formal properties of systems and elaborating the relational structures within systems so we can deal with their structural properties rather than just quantitative relations.

Today, set theory serves as the foundation for modern database design while network science, grounded firmly in graph theory, is thriving as a field for formally studying the organization of complex systems.3 I've enjoyed exploring the fundamentals of set theory during my independent study in discrete mathematics and exploring the rich academic literature that applies graph theoretical techniques to the analysis of blockchain systems.4

I appreciated the opportunity to re-read this paper and feel excited about exploring some of these concepts through computer modeling and simulation.

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Note: This was originally written in response to an assignment for my course on the Collective Dynamics of Complex Systems at Binghamton University.

John von Neumann's Theory of Self-Reproducing Automata (1966), based on lectures presented in 1949, represents a fascinating glimpse into the mindset of a founding father of modern computing.1 The computers we rely on today are grounded in the von Neumann architecture, whose defining feature is the separation of processing (handled by CPUs) and memory (handled by RAM).

While the paper explores many compelling themes, including von Neumann's systematic comparison of organic nervous systems and artificial computing systems, I'd like to focus on one particularly thought-provoking statement from page 912:

"Appealing to the living world doesn't help us when we don't understand enough about how natural organisms function."

The statement arises in the context of von Neumann's exploration of an apparent contradiction between complication and self-reproduction. In living systems, "simple entities surrounded by an unliving amorphous milieu, produce something more complicated." This suggests that complication is a generative property enabling simple parts to create more complex wholes.

However, in artificial systems, he observes the opposite: using the example of machine tools, he argues that any organization creating something new must be more complicated than what it creates. This implies that complication is a degenerative property that limits the creation of more complicated things.

Given our complete understanding of man-made artificial automata versus our limited grasp of biological systems, von Neumann advocates focusing our attention on artificial systems to advance our understanding of self-reproduction. He then demonstrates how to construct formal systems capable of creating objects more complicated than themselves.

My question is - to what extent does von Neumann's statement that "appealing to the living world doesn't help us" still hold today?

We've made remarkable advances in biology and neuroscience since 1949, mapping the human genome and developing sophisticated tools for understanding brain structure and dynamics. As of yesterday we've used AI to (allegedly) "write whole chromosomes and small genomes from scratch."2 Still, no reasonable person would claim we fully understand phenomena like life or consciousness.

Are we ready to design and make practical use of more organismic neuromorphic computers that transcend the von Neumann architecture? 3 4

It’s a fascinating time to be alive as the tools developed by scientists who had to engineer around our limited understanding of organic life are now being wielded to accelerate that very understanding at a breathtaking pace, likely leading to emergent outcomes that none of us can anticipate.

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Note: This was originally written in response to an assignment for my course on the Collective Dynamics of Complex Systems at Binghamton University.

Recent conversations have challenged my thinking about the study of complex adaptive systems (CASs) around a central question—where should we focus our attention when studying them?

Is it more important to understand the behavior of the individual acting agents within systems and the complex dynamics that emerge from their interactions?

Or, should we focus on the architecture of systems and seek detailed understanding of their internal structure which constrains and exerts influence on the agents?

This week’s readings have provided valuable historical context and philosophical perspectives that are helping me develop more useful mental models around this question.

Complexity = Hierarchy?

In The Architecture of Complexity (1962), Herbert Simon argues that one of the main reasons we are able to see, describe, and understand most complex systems in the first place is because of their “nearly decomposable” hierarchic structure.1 He even speculates that humans may be fundamentally incapable of understanding non-hierarchical complex systems due to cognitive limitations that prevent us from calculating all relevant interactions.

The methodology for deep systems analysis which embraces a hierarchical and recursive approach to decomposing systems of interest that I’m applying to the study of blockchain systems is directly inspired by Simon. It has proven invaluable in deepening my understanding of the nature of the interactions between their technical, social, and economic components.

The Limits of Hierarchical Thinking

A decade later, in The Organization of Complex Systems (1973), Simon contrasts two visions of science with differing approaches to explanation.2 Laplacian science “seeks to formulate a single set of equations describing behavior at the most microscopic, the most fundamental level, from which all macro-phenomena are to follow and to be deduced.”

In contrast, Mendelian science “seeks to formulate laws that express the invariant relations between successive levels of hierarchic structures.” Since nature is only nearly, not fully decomposable, if we embrace the second view we are left to accept the fact that “many of the most beautiful regularities of nature will only be approximate regularities and will fall short of exactness.”

For me, this hints at the limits of deep structural understanding and speaks to the need for bottom up agent-oriented approaches that are uniquely enabled by modern computation and allow us to reason about complex behavior which emerges from the micro level.

Pros and Cons of Complexity

Finally, in Complexity and Philosophy (2006) Cilliers, Heylighen and Gershenson highlight how the different historical approaches of systems science and cybernetics are gradually being integrated under the banner of complexity science.3 The central paradigm of complexity science “is the multi-agent system: a collection of autonomous components whose local interactions give rise to a global order."

However, the authors crucially note that there hasn’t been much success in productively applying complexity theory to the social sciences. They speculate this might be a result of the fact that “many social theorists were introduced to complexity via the work done by 'hard' complexity scientists.” This work is strongly informed by chaos theory and contains strong elements of reductionism.

I feel like this hints at the exact issue that I, with my interdisciplinary degree in the social sciences, ran into after I took my first introductory course in agent-based modeling through the Santa Fe Institute in 2019. The toy models of birds flocking, forest fires, and the dynamics of segregation were fun and thought provoking. But I failed to see how they could be usefully applied towards a better understanding of the real world systems that I wanted to study like blockchain networks, or The Federal Reserve. This is what led me to seek out and place my focus on the methods of systems science that aim for deep architectural understanding.

Synthesis?

So, returning to the question of agents vs. architectures, I appreciate how this week’s readings support my growing intuition that there is a need for integration.

Many of the complex adaptive systems we are interested in—like the human body, a smart home, or a branch of a national government, can be simultaneously viewed as agents or as structures. It’s a matter of perspective.

The Handbook of Applied Systems Science identifies three core methods in systems science - bottom-up ABMs, top-down system dynamics, and middle-out network analysis.4

I wonder, how can we develop tools that let us efficiently combine insights from each approach to better understand the interplay between agents and architectures?

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Note: This was originally written in response to an assignment for my course on the Collective Dynamics of Complex Systems at Binghamton University.

Heinz Von Foerster's Notes On An Epistemology For Living Things (1972) was a challenging read for me.1

Von Foerster was an Austrian-American scientist and polymath who made contributions in fields from physics to philosophy and is widely recognized as the creator of second-order cybernetics.

Stylistically, I enjoyed the way that he develops his argument step-by-step in the form of a set of 11 logical propositions. Each piece of the whole argument follows directly from the prior piece in a clear fashion. The fact that the Notes can be "read in a circle" giving the reader the option of starting at any of the propositions rather than being forced to read from the beginning is a nice creative touch.

Unfortunately, I struggled to really grasp the essence of all his points on my first read through. Perhaps this is because he wasn't a native English speaker. Or maybe it's because I felt flooded by such a large amount of novel mathematical, biological, and philosophical concepts, all at once?

Since there were so many details in this paper that I didn't understand, I'm going to focus on a few high level concepts that (I think) I did.

Notes represents an attempt by Von Foerster to address a key challenge faced by scientists in the 20th century. Natural scientists, from Einstein to Heisenberg, had recognized that there is no such thing as an "objective description of the world in which there are no subjects." From this observation, it follows naturally that science must develop a coherent "theory of the observer" or "the description of the describer." Since only living organisms qualify as observers, Von Foerster reasons, biologists must take on the task of developing such a theory.

The notes set out to address the question of how do we (humans) know?

Proposition #1 states "The environment is experienced as the residence of objects, stationary, in motion, or changing."

I was immediately thrown off by considering the environment as something that is experienced rather than something which simply exists. And the use of the word "residence" didn't feel very natural to me. I might have preferred a term like "existence," or "appearance." But, after reading through the rest of the notes and spending some time reflecting, I was able to accept this initial proposition as true and uncontroversial.

From this initial proposition, Von Foerster goes on to make several assertions. These are a few that stood out for me:

1. "Objects" and "events" are not primary experiences, and they have no absolute objective status. Instead, they are representations of relations that emerge from two forms of computation, abstraction and memorization.

2. There is no external objective reality, and the notion of environment is subjective and personal. Reality is determined by processes of internal computation.

3. Concepts like “change” or “invariance” are properties of descriptions, not of objects themselves.

4. Living organisms are "relators" that compute the relations which maintain the integrity of organisms.

5. “Information” is a relative concept that only has meaning when related to the cognitive structure of the recipient of a message. Information associated with a description depends on the ability of an observer to draw inferences from the description.

The final proposition, #11, states "The environment contains no information; the environment is as it is" and the reader is prompted to return back to proposition #1.

From the perspective of epistemology, the philosophical study of how we come to know things, I'm left feeling that I can't disagree with anything that I've read. I appreciate being encouraged to think rigorously about the role of subjective internal computations in determining how we obtain knowledge.

But I'm left with several questions.

1. What does Von Foerster have to say about ontology, or what exists (in contrast to how we come to know what exists?)

2. What happens when robots imbued with advanced AI start taking on the role of observer? How will this impact our understanding of intelligence, science, and reality?

3. What exactly does it mean for a living organism to "compute the relations which maintain the integrity of the organism"?

4. How does Von Foerster's mathematical definition of information compare and contrast with Shannon's? The formalism seems identical, but I wonder if it should be interpreted differently based on context?

This is a piece that I'll need to return to several times in order to really grok. But, even having only understood a portion of the content, I feel like I've been enriched with a valuable new perspective as I grapple with developing my own understanding of the relationship between the fields of systems, cybernetics, and complexity as scientific paradigms.

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Note: This was originally written in response to an assignment for my course on the Collective Dynamics of Complex Systems at Binghamton University.

The Role of Models in Science (1945) by Arturo Rosenblueth and Norbert Wiener takes a wonderfully logical and systematic approach to reasoning about the role of models, and modelers, in science.1 For me, the assignment of this paper comes at the perfect time as I’ve been wrestling with fundamental questions about what types of models I’d like to create and why.

Up until this point, my research on blockchain systems (Bitcoin, Ethereum, etc.) has been heavily focused on the application of a methodology that demands a very deep and structured “engineering-style” analysis of systems as a hard prerequisite to the process of building models and then simulations.

It is an approach that might initially seem counter-intuitive in the face of the authors’ observation which reflects the common wisdom that “If a model has a more elaborate structure and is less readily amenable to experiment than the original system, it does not represent progress.”

On the one hand, I have found it rewarding to take such a rigorous approach to building an incredibly detailed and elaborate formal description of Bitcoin. Coming from a background in the social sciences, it has proved invaluable in deepening my technical understanding of a system that I previously understood primarily from an economic, political, and psychological perspective.

On the other hand, I’m finding that the process of analysis has not produced much clarity about what sort of models I want to build, what kinds of research questions I’m most interested in asking, or what types of experiments I want to run.

I’ve been working to zoom back out, step away from my “recreation” of Bitcoin, and reconnect with why I believe it is important for society to develop a deeper and more accessible scientific understanding of blockchain systems in the first place.

From this place of reflection, I found the work of Wiener and Rosenblueth to be inspiring, and thought-provoking. I’m asking myself various questions such as:

• Am I more inclined towards the work of a theorist, focused on “moving up or down the scale of abstraction” or that of the experimenter, focused on “translating abstraction into experiment or vice-versa”?

• Do the computational models I aim to build seem more like material models which represent simplified versions of complex systems with assumed similar properties, or formal models which represent symbolic/logical assertions of idealized simple situations. Are they a synthesis of the two?

• In what specific ways do I aim to improve on existing approaches to modeling blockchain systems?

One of the most thought-provoking lines came at the very end of the paper, with the statement that “the main tool of science is the human mind and that the human mind is finite.” It is true that human intelligence still outpaces machine intelligence in many of the most important dimensions such as creativity. However, LLMs possess a tremendous and rapidly accelerating capacity to synthesize and summarize massive amounts of information in ways that greatly surpass the human mind.

I wonder if this development may require some re-assessment of the utility of building incredibly rich and detailed models in light of the powerful capacities of machine intelligence.

The only thing that I found myself missing from this paper was some insight into how the authors thought about the role of modeling in the social sciences. It’s an understandable omission given the time of its publication, but I know that Wiener went on to develop strong opinions on the utility of cybernetics in guiding society. I would have loved to have seen some of the authors’ thinking about how economists or sociologists at the time were using models.

This would be especially relevant for me given the nature of public blockchain systems as socio-technical ones whose emergent properties can only be properly understood by accounting for various interactions between unpredictable humans, rules enforced by rigid code, and the surrounding complex environment which includes the institutions and actors embedded in the global political economy.

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Things are moving far too quickly for me to attempt a comprehensive analysis, but I would like to share some high-level thoughts on how a Trump administration might influence the evolution of cryptoeconomic systems like Bitcoin and Ethereum. For all of their issues, I believe they represent an important social and technological innovation that is here to stay.

Right now I’m tracking three distinct yet intertwined forms of influence — cultural, political, and economic.

Cultural

While many of the most influential figures in crypto — from entrepreneurs to policymakers — were busy celebrating Trump’s inauguration at a lavish Crypto Ball, Team Trump launched a brand new “memecoin” called TRUMP in the middle of the night. Memecoins are cryptoassets inspired by internet memes which are characterized by their highly volatile nature. The original and most famous example is Dogecoin.

The price of TRUMP skyrocketed from $4 to $30 overnight and reached a peak of $72.00 the following day. Several large fortunes were made by savvy speculators who managed to buy low and sell high. The next day, an official MELANIA coin was launched and eager traders who felt they missed the boat on TRUMP piled in. Demand for both tokens was so high that it overwhelmed the Solana blockchain network that was used to launch both projects.

Trump’s foray into the world of memecoins will contribute to their continued use as a popular tool for content creators and celebrities to capitalize on their name recognition and brands. However, these tokens are a distraction from the bigger picture.

Donald, Donald Jr., Eric, and Barron Trump have launched a new organization called World Liberty Financial along with a slew of technically competent crypto veterans. They’ve raised hundreds of millions of dollars to build a polished, user-friendly interface that will introduce decentralized borrowing and lending to a broader mainstream audience. This is one of crypto’s most powerful and popular use cases beyond speculation.

The organization is gradually accumulating a massive crypto treasury (currently sitting at ~$382,000,000) worth of Ether, Bitcoin, and assorted other cryptoassets. All of their financial activity on the blockchain can be easily tracked in real-time.

Projects like this focused on genuine use-cases beyond pure speculation are what will allow Trumpism to help accelerate the normalization of crypto as a cultural phenomenon.

Political

The Trump administration is being stacked with pro-crypto figures.

• The new acting chairman of the Securities and Exchange Commission (SEC), Mark Uyeda, has declared in the past that the War on Crypto must end.

• Uyeda has appointed a fierce crypto advocate, SEC commissioner Hester Peirce to lead a new crypto task force that will be focused on “developing a comprehensive and clear regulatory framework for crypto assets.”

• Caroline Pham, the new acting chair of the Commodities and Futures Trading Commission (CFTC), has argued that the U.S. should develop a regulatory sandbox to facilitate innovation and sensible regulations for crypto.

• Former Paypal Chief Operating Officer David Sacks has been appointed AI and Crypto Czar.

This new lineup of public officials will lead a complete reversal in approach from the one embraced by outgoing SEC chairman Gary Gensler, whose reign was characterized by a policy of arbitrary “regulation by enforcement” and a stubborn refusal to articulate clear rules for crypto investors and entrepreneurs to follow. Just last week a Federal Judge ordered Gensler’s SEC to explain its decision to not provide clear rules for crypto and warned them against providing “yet another poor explanation in an already-long line of them.”

The administration’s efforts will be supported by a Congress filled with crypto advocates on both sides of the aisle. Most notably is Senator Cynthia Lummis (R) who now chairs the brand new Senate Banking Subcommittee on Digital Assets, and has introduced legislation calling for the United States to create a Strategic Bitcoin Reserve.

She will be joined by the likes of senators Kirsten Gillibrand (D), and Ted Cruz (R) along with representatives Ro Khanna (D), Tom Emmer (R) and Ritchie Torres (D) who have all fought hard and in bi-partisan fashion to support the creation of clear and fair rules for the crypto industry.

Yesterday Trump proclaimed that the U.S. will become the “world capital for AI and crypto.” He then signed an executive order outlining an ambitious and comprehensive plan that will, in the words of Electric Capital co-founder Avichal Garg, “protect individual freedoms, ensure financial sovereignty, and reassert U.S. dominance in the global digital economy.”

Some of the major remaining open questions include:

1. Do the rumors of complete elimination of capital gains taxes on all or certain classes of cryptoassets have any merit, and are they politically achievable?

2. Will the U.S. actually adopt a strategic Bitcoin reserve and start directly purchasing bitcoin? The “stockpile” mentioned in Trump’s executive order is a small step in that direction.

3. Will any of the individual states pass legislation that has been introduced to adopt strategic bitcoin reserves?

A yes to any of these would lead to major changes in the political structure which exerts significant influence on the supply and demand dynamics of the cryptoeconomy.

Economic

For many people, the main question around crypto is still “can/how will this stuff get me rich?”

I’m not going to give any financial advice, but I will make general comments on how I expect market structure and dynamics to change.

A brief glance at headlines from the past week illustrates how major financial executives are changing their tune on crypto with the arrival of a new administration.

• A few days ago Bank of America’s CEO said that “the banking system will come in hard on the transactional side of [crypto],” if regulators create clear rules.

• BlackRock CEO Larry Fink is calling for the SEC to allow for the “tokenization” (ie: blockchainification) of bonds and stocks, proclaiming that "if we can tokenize bonds and stocks...it will democratize investing in ways we can't imagine."

• Morgan Stanley’s CEO, Ted Pick, has said that “the bank will work with U.S. regulators on offering crypto.”

As institutional support for various cryptoassets accelerates, it will create more demand to the tune of hundreds of millions if not billions of dollars pouring into the burgeoning cryptoeconomy over the next few years. We will see unprecedented levels of chaotic and casino-like behavior along with outright scams as bubble-fueled mania sets in.

There will be new all-time highs that create fortunes, and there will be massive crashes that ruin people’s lives.

But most importantly, the environment will become much more favorable for legitimate crypto entrepreneurs building products that leverage the unique aspects of public blockchains to deliver real value to consumers.

End Game?

Crypto, like the world wide web, was brought to life by dreamers and visionaries who have been forced to face some harsh truths about reality.

Crypto-lawyer Gabriel Shapiro put it perfectly.

'“Ironically just at the point of finally being 'legalized' and going mainstream, the industry feels like it's succumbed almost completely to unsustainable zero-sum financial nihilism and bloodthirsty competition instead of taking the opportunity to build toward cypherpunk ideals or really any other ideals than Moloch/Mammon/getting rich.

In other words--Wake the fuck up and *really* believe in something, some mission to try to make the world really better, before it's too late, or crypto will die or turn into something even more sinister than what it was trying to replace.

I'm still gonna be here trying to break through with something.”

As an early adopter, I was forced to abandon my idealistic hopes long ago. Yet I still remain optimistic that creative individuals and groups with good hearts and wisdom gained through experience will find opportunities to use cryptoeconomic systems in ways that benefit humanity.

Note: None of this is financial advice, and I do hold cryptoassets.

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As a child my Father would bring me along

To his place of work, where his lab coat he’d don

There were labs filled with lasers, silicon wafers galore

But it’s the simple things that I always adored

The taste of hot chocolate in styrofoam cups

Smelling dry-erase pens, smeared on white boards

Spinning in chairs until I got dizzy

No interest in systems, just being silly

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I’m very familiar with this term in the context of technology. I’ve been an early adopter of various technologies since childhood, from pre-iPod Rio Mp3 players in 2000 to Bitcoin in early 2012.

After discovering Geoffrey Moore’s model for explaining how cultures adopt (or fail to adopt) novel technologies around 2015, I’ve consistently considered the nature of early adoption as it applies to various tech systems.

But I had never considered how it might apply in the context of systems research. What exactly does it mean to be an early adopter of systems science?

Are we vanguards, riding the crest of a Kuhnian scientific revolution still in its formative stages?

If we are early adopters, does it mean that we need to approach education, communication, and fundraising in a way that is fundamentally different from scientists who aren’t operating in a systems paradigm?

Just some food for thought.

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But after sharing the piece, I was invited to co-chair the ISSS’s special integration group on research towards general systems theories, where we are trying to develop rigorous frameworks for comparing the theoretical approaches of different systems researchers.

While my focus has been and will continue to be on systems science, my time with the group has helped me to appreciate the importance of General System Theory (GST) as a foundational topic that I must explore more deeply.

I’m just over halfway through Bertalanffy’s 1968 magnum opus General System Theory, which played a pivotal role in launching the modern systems movement. Reading it is providing me with incredibly valuable context for my work in the group and studies in Binghamton University’s Systems Science program.

Here are some of my raw notes, organized by theme, provided with no additional commentary.

The Contemporary Need for Systems Approaches

• The fundamental problem of today is that of organized complexity

• We must improve our knowledge of the laws of human society

• A well-developed science of human society and a corresponding technology is needed to save humanity from chaos and destruction

• Society needs to produce scientific generalists

Limitations of Classical Science

• Many scientific fields lack the necessary tools for explanation and prediction that have been developed in physics

• Classical science focused on classification, modern science must confront dynamic interaction

• The "system problem" is the problem of the limitations of analytical procedures in science

Principles of GST

• Identical scientific principles have been found in various disparate fields because certain general principles apply to all systems

• Unity of science can be achieved by identifying structural uniformities at different levels of reality

• The unifying principle of systems and perspectivism is that organization is found at all levels

• Systems theory provides scientific interpretation and theory in new areas, and at a higher level of generality, than the specialized sciences

• GST deals with formal characteristics of systems

Developing Methodology for GST

• A rigorously developed GST should be axiomatic in nature

• If we define system conveniently then we find models, principles, and laws which apply to generalized systems

• If an object is a system, then it must have certain general system characteristics

• GST should be expressed in logico-mathematically strict form and focus on developing general system principles

• Certain system characteristics defined by set and graph theory are broadly applicable to different types of systems, from the physical to the social

Challenges and Practical Considerations

• Set-theoretical connections of axiomatized systems theory with real systems problems are tenuous

• Sometimes it is preferable to start with non-mathematical models

• The intuitive approach to GST lacks rigor while the deductive approach is vulnerable to errors in choice of fundamental terms

• In complex and theoretically immature fields we should be satisfied with "explanation in principle" as defined by Friedrich Hayek

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This newsletter will be undergoing some evolutionary changes in the New Year, so I’m sharing an old post about evolution to end 2024.

I hope everyone is enjoying their Christmas/Hanukkah/Holiday Season!

The sixth principle of systems science states that systems evolve.

Let’s take a look at some of the key aspects of evolution by exploring the ways in which cryptoeconomic systems have evolved over time and some of the important forces that will shape their future evolution.

Evolution

Evolution is an ongoing process that generally leads to systems becoming more complex over time. It involves permanent modifications to the structure and behavior of species or populations which slowly unfold. 

Bitcoin represented a significant stage in the evolution of the digital currency ecosystem. Technologists have been experimenting with ways of creating peer-to-peer decentralized electronic cash systems since the early 1990’s, but none of them took off until Bitcoin.

By combining core aspects of previous attempts such as e-cash, b-money, and Bit Gold, Bitcoin achieved a fundamental breakthrough in computer science and game theory by solving the long standing game theoretical “Byzantine General’s problem.” For the first time in the history of electronic cash, a group of strangers distributed across the world could coordinate to maintain a global ledger of transactions without needing to know or trust each other. Anyone could access the Bitcoin blockchain and use it to send bitcoins without needing permission from a centralized authority or depending on a third party to validate transactions.

While there is plenty of skepticism regarding Bitcoin’s future as a currency, there is a growing consensus that the “blockchain technology” it pioneered can be a valuable tool for improving how our society transmits value and organizes itself. The sprawling ecosystem of blockchain-based technologies, referred to by many as “Web3”, is an environment in which constant innovation is leading to increased complexity with new structures and behaviors appearing on a daily basis.

Evolution is a very complex topic. This piece will highlight just a few basic components of the process that are easily observable and especially relevant within cryptoeconomic systems: adaptation, selection, mutation, and emergence. As usual, see the Principles of Systems Science textbook for a more detailed treatment.

Adaptation

Adaptation occurs when a system changes a form or function in response to a force from the environment acting upon it. It represents the capacity of a system to respond to environmental shifts by reallocating internal resources to respond to novel inputs.

George Mobus makes a clear distinction between adaptation and evolution that I find useful: 

Adaptation applies to reversible changes in structures or behaviors within individual systems. 

Evolution describes the process of selecting which systems are the most fit among individuals over time. Individual organisms may change and adapt, but they do not evolve. Only species or groups of systems evolve over extended time periods. 

Bitcoin adapted to increased demand for use of its blockchain which led to rising transaction fees by allocating resources towards the development of the lightning network. The lightning network is a decentralized system that uses the Bitcoin blockchain as a mechanism for securing “off-chain” micropayments between users. It allows Bitcoin users to experience instant payments, low transaction costs, smart contracts, and cross-blockchain functionality. 

The crypto ecosystem as a whole evolved when Ethereum was created, giving blockchain developers the necessary tools and freedom to easily build whatever sort of programs they wanted on a blockchain, not just currencies.

Selection

Selection is the process in which the environment gradually passes judgment on the fitness of a system. Fit systems that have the qualities necessary to grow and adapt within their environment survive and propagate more of their kind into the future. Unfit systems which struggle to survive lose out to competing systems and eventually die off. 

Gitcoin’s Kevin Owocki points out that, “natural ecosystems are evolutionary and powered by natural selection. Similarly, Web3 is an evolutionary ecosystem powered by market selection.”

We can observe the market selection process by looking at how the composition of the top 10 cryptoassets has changed over the years. In September of 2014, Bitcoin and Litecoin were the top two cryptocurrencies as measured by market capitalization (price of a coin multiplied by the total units of that coin in circulation.) Ethereum didn’t exist.

Fast forward to March of 2017 and Ethereum has arrived on the scene and secured the number two spot, with Litecoin falling down to number six. Previous heavyweights like Peercoin, Namecoin, and Dogecoin are nowhere to be found. 

In January 2020, Bitcoin and Ethereum were still on top. Aside from Ripple and Litecoin, the rest of the top 10 is filled with newcomers.

Today, Bitcoin and Ethereum remain dominant, maintaining a firm grip on their top spots. The rest of the top 10 consists of a mixture of some winners from 2020 along with newcomers.

Selection is a process in which combinations of subsystems that prove beneficial are favored in the long run, whether by reproductive fitness, business success, or ecosystem stability. Bitcoin and Ethereum have demonstrated fitness where many others have failed.

“If we speed run this ecosystem evolution, we see the degenerative species (which by definition are short term greedy, but over long term tend to self terminate) collapse, and we see the regenerative species (which are long term greedy, resistant to shock, and grow over time) grow. Cast the dice 1000 times, the results will be the same each time!” – Kevin Owocki

Mutation

In biological systems, mutations are the inevitable errors that occur during the process of systems (such as cells or DNA) making copies of structures in order to replicate them.  

Human organizations, unlike biological systems, can consciously alter a system if they believe it might improve its functioning. Humans have a unique capacity to anticipate the future and act strategically to meet their desires. 

Software forks are a type of mutation that generate diversity within blockchain ecosystems. Because public blockchains are open-source, anyone is free to fork (copy and modify) them as they please. 

There are two different types of forks in crypto. Soft forks are upgrades to existing blockchains that bring new functionality. Hard forks create code changes that are so significant that the new software is no longer compatible with the old software, leading to the creation of a new blockchain.

Both Bitcoin and Ethereum have a long and rich history of soft forks being used to improve functionality, and hard forks arising when internal disagreements have led to groups creating new blockchains that are more aligned with their goals. 

Sometimes the mutations result in powerful, widely adopted upgrades like Bitcoin’s Segwit, or Ethereum’s Merge. Sometimes they lead to the creation of new blockchains, like Bitcoin Cash or Ethereum Classic, which die off after an initial burst of activity and enthusiasm. There are also cases where new blockchains manage to find fitness within a certain niche, like Dogecoin and Litecoin which are Bitcoin forks.

Emergence

Emergence occurs when new forms or levels of organization, such as new properties or functions, arise within a system. Emergent entities have unique properties which cannot be found in any of their individual parts. The phenomena of emergence explains how new functions come into existence.

When Vitalik Buterin, college-dropout, Thiel fellow, and co-founder of Bitcoin Magazine realized that Bitcoin was too inflexible to support his creative ambitions, he outlined a vision for Ethereum. 

Ethereum aimed to be “an alternative protocol for building decentralized applications” and “to provide [a] blockchain with a built-in fully fledged Turing-complete programming language.”

It would have “vastly more power than that offered by Bitcoin scripting because of the added powers of Turing-completeness, value-awareness, blockchain-awareness and state.” It would support the creation of “smart contracts, cryptographic ‘boxes’ that contain value and only unlock it if certain conditions are met that could be created “simply by writing up the logic in a few lines of code.”

Buterin laid the foundations necessary for blockchain technology, web3, and crypto as a broad social phenomenon much larger than Bitcoin to emerge. Ethereum was an emergent phenomenon within the blockchain ecosystem because it introduced new functionality and allowed programmers to build complex applications on blockchains in a way that simply wasn’t possible before. Because of Ethereum’s flexibility and capacity to support the development of complex applications, it led to a variety of novel and surprising applications emerging.

“NFTs are probably the one thing that I did not predict, by the way. If you look at the list of applications that were in the Ethereum Whitepaper, and you look at the applications that are popular today, the big thing that's in the second list and not the first is NFTs.” – Vitalik Buterin

No one saw Cryptokitties coming. The game put NFTS on the map by gaining traction among normal consumers who typically didn’t care about crypto and clogging up Ethereum’s network with increased demand leading to high transaction fees.

Competition, Cooperation, and Coordination

Competition

Competition occurs when different systems that require the same resource must fight to acquire what they need. When resources are abundant and entities are scarce, competition isn’t a strong selection factor. But when resources are sparse or there are too many entities competing for what is there, then competition becomes a very strong selection factor. The benefit of competition is that through selection pressures, it improves the fitness of various systems over time. 

Crypto projects compete with each other for various forms of resources such as funding, talent, and attention. There will also be new competition emerging between decentralized, open-source cryptoassets and the centralized digital currencies that are being deployed by central banks around the world.

Cooperation

Cooperation occurs when systems send and receive messages to one another in order to facilitate the exchange of material, energy, or information. It is an important mechanism for coordinating activities between systems.

MakerDAO and Aave are two projects that compete within the same market with similar product offerings while still finding ways to cooperate.

Maker’s core product is the DAI stablecoin which avoids volatility by maintaining a peg to the U.S. Dollar. Aave’s core product is a decentralized lending protocol that allows lenders and borrowers to loan out money and borrow with their interactions being managed by smart contracts instead of a corporation. 

Maker forked Aave in February to launch its own Spark lending protocol. Maker, acknowledging the fact that Spark wouldn’t be possible without Aave, will be paying 10% of all profits from Spark to Aave until August 2025. 

The two projects cooperate by exchanging code and money in order to work towards their shared goal of shifting society towards more decentralized financial infrastructure.

Coordination

Coordination is the process of organizing the behavior of systems to ensure they work together effectively.

Coordination within a system is called logistics. MakerDAO handles internal coordination on its governance forums and its governance portal.

Coordination between a system and its environment is called tactics. When demand for DAI started to drop, the organization made a tactical decision to raise the DAI savings rate to 8% . This resulted in an immediate increased demand for DAI and surging revenues for the Maker Protocol.

Coordination that takes place over long time horizons to develop adaptive responses in response to a changing environment is called strategy. Last summer, MakerDAO founder Rune Christensen unveiled his vision for Maker Endgame, a strategic proposal designed to transform Maker “from an unclear structure towards a reality where all aspects of MakerDAO are well documented and understood, and operate based on defensible incentives and derisked processes.”

When there are many systems attempting to cooperate there is a need for a special type of decision making entity to coordinate them. A hierarchical, cybernetic system is needed to process much more information than the individual entities could manage.

When DAI launched in 2017 it was an ambitious experiment. Today, it is a giant within the world of decentralized finance that many people, protocols, and organizations across the world depend on. As MakerDAO grew, its various decentralized governance structures and subsystems became unwieldy.

The Endgame Plan is an effort to develop the necessary control and coordination structures that will enable MakerDAO to continue thriving into the future.

Looking at the evolution of MakerDAO over time shows us that while decentralization can be an asset, any complex adaptive system requires some degree of top-down hierarchical control to operate effectively. With cryptoeconomic systems we get to see how these control systems can emerge based on the needs of the system and be voted on and approved by system stakeholders rather than being arbitrarily imposed through sheer will and force by a select few.

Conclusion

Systems scientists need to constantly be aware that the systems they study are subject to a variety of evolutionary pressures. Understanding evolutionary pressures is vital to making sense of systems.

Adaptation is what allows a system to react and respond to its environment. The process of selection kills off the weak and propagates the qualities of the strong. Mutation introduces variations and changes, some of which are beneficial and others harmful. New functionalities, behaviors, and properties come into being via emergence. Competition, cooperation, and coordination determine how resources are allocated and which systems achieve their goals.

Failure to pay attention to evolutionary processes across time can cause our models to become inaccurate and outdated. Many changes within a system are the result of environmental changes and external pressures. If these factors aren’t considered, your perception of what a system is will be distorted. You may be seeing the system as it was, not as it is or is in the process of becoming.

As I argued last week, cryptoeconomic systems are the perfect space to observe these dynamics at play. 

“In abundant times, 100s of experiments will blossom. In scarce times, 97% will fail. But the 3% that survive will be the keystone species of the next boom. Innovate, iterate, evolve, repeat. It’s elegant, even beautiful!” – Kevin Owocki

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A few points stood out to me.

The authors argue that part of the value of using the term complexity is in “embracing the openness of the community through the vagueness of the term.” I can appreciate the perspective, but am not sure how helpful this will be for a field seeking to establish itself as a science. To that point, they also argue that complexity, “lacking a specific set of shared systems of interests or methodological tools,” is not a science, but rather a community with a shared approach to science.

I appreciate that they drew attention to the fact that complexity has its roots in philosophy, economics, and physics and was originally a community based on abstract thought experiments and models. To me, this stands in stark contrast to systems which has deep roots in theoretical and applied biology along with very practical challenges in technology and engineering. Perhaps this helps explain why the systems community, for all its flaws, has always felt more alive to me?

My favorite part was their mention of a French philosopher and sociologist, Edgar Morin — a name I had never seen. Morin argued in the early 90s that the holistic systems paradigm was flawed because it overlooked the important issues of interaction and organization.2

I appreciate this critique because it speaks to one of the exact issues that I’m addressing with my work on software tools for applied systems science. The problem is highlighted by Susan Stepney, a computer scientist focused on non-standard computing and bio-inspired algorithms, in her review of Bertalanffy’s General System Theory.3

“We need a way to capture the dynamics of the relationships and couplings. This is more than just class diagrams with associations: we also need a view of coupling strength, and more importantly, a high level view of the coupling dynamics: changes in relationships and coupling strengths. We need to move the focus from the nodes to the (dynamics of the) edges of the graphs.”

Ultimately, reading the paper mainly served to reinforce my conviction that systems is a more useful overarching paradigm for transdisciplinary research, one that can easily include complexity and complex systems under its umbrella.

But I’m curious to hear the steel man argument for complexity. What do you think of the piece in Nature? Do you think complexity is a more useful paradigm than systems, or have you seen anyone make a convincing case?

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This question was posed during a group discussion I was a part of earlier this week. The group is working on a curriculum and educational technologies designed to get students excited about systems thinking and cybernetics.

Someone made the argument that in order to get younger people interested in these topics, it’s important to present inspirational role models who have demonstrated what can be accomplished by approaching the world in a systemic/cybernetic fashion. Elon Musk was highlighted as an example of someone who has demonstrated a clear capacity to think and act systemically and cybernetically across multiple domains.

The argument was compelling, but unsurprisingly there was pushback and several questions arose.

• Is Musk actually a systems thinker, or is he just great at hiring and making use of talented systems thinkers?

• From a philosophical and ethical perspective, do we really want to encourage people to emulate Musk?

• How useful is it to use someone with such an abnormally high level of intelligence and obsession with work as a role model?

The conversation got me thinking, who else might we use as inspirational figures?

So, with a bit of help from Claude and the book Systems Thinkers, which provides a comprehensive biographical history that profiles 30 systems thinkers, I came up with an initial list of seven figures for consideration.

To spice things up, I asked Claude to imagine and describe each person as if they were characters in a role playing game. I figure that, especially for the lesser known historical figures, it might help bring them to life. I tried to include a diverse set of individuals with expertise spanning the natural and social sciences along with technology and engineering.

What do you think of this list? Are they all systems thinkers or cyberneticians in practice, even if not in name? Who would you want to include on a short list?

1. Ludwig von Bertalanffy (1901-1972)

The Foundational Sage - "The Unifier of Sciences"

Class: Theoretical Biologist/Philosopher

Special Ability: General Systems Theory - Can identify universal principles across different systems

Bio: The original systems theory architect, Bertalanffy pioneered the view that systems of all types share common organizing principles. His ability to see patterns across biology, psychology, and social sciences revolutionized how we understand complex systems.

2. Norbert Wiener (1894-1964)

The Cybernetics Wizard - "Master of Feedback Loops"

Class: Mathematician/Engineer

Special Ability: Cybernetic Vision - Can perceive feedback loops and control mechanisms in any system

Bio: A mathematical prodigy turned cybernetics pioneer, Wiener understood how information and control work across mechanical and biological systems. His mastery of feedback loops and communication theory makes him the party's master of system behaviors.

3. Margaret Mead (1901-1978)

The Cultural Systems Oracle - "Bridge Between Worlds"

Class: Anthropologist/Social Systems Expert

Special Ability: Cultural Insight - Can understand how human systems interact and evolve

Bio: A masterful observer of human systems, Mead brought systems thinking to anthropology and public discourse. Her ability to understand how cultural systems work and communicate complex ideas to the public makes her the party's expert on human dimensions.

4. Friedrich Hayek (1899-1992)

The Market Sage - "Navigator of Spontaneous Order"

Class: Economist/Social Philosopher

Special Ability: Emergent Order Detection - Can identify self-organizing patterns in complex social systems

Bio: Hayek understood how distributed knowledge and spontaneous order emerge in complex social systems. His insights into how markets and social institutions evolve make him the party's expert on emergent phenomena.

5. Elinor Ostrom (1933-2012)

The Commons Guardian - "Architect of Collective Action"

Class: Political Economist/Institutional Designer

Special Ability: Commons Wisdom - Can design systems for sustainable resource management

Bio: The first woman to win the Nobel in Economics, Ostrom revolutionized our understanding of how communities can manage shared resources. Her practical wisdom about governance systems makes her the party's expert on designing sustainable institutions.

6. Elon Musk (1971-)

The Tech Systems Innovator - "Industrial System Disruptor"

Class: Entrepreneur/Engineer

Special Ability: System Disruption - Can identify and transform large industrial systems

Bio: A controversial but impactful figure in modern systems transformation, Musk applies systems thinking to revolutionize industries like automotive and space. His ability to see opportunities for system-level change makes him the party's industrial systems specialist.

7. Vitalik Buterin (1994-)

The Crypto-Systems Architect - "Digital Commons Designer"

Class: Programmer/Economic Systems Designer

Special Ability: Digital System Design - Can create new forms of decentralized coordination

Bio: The youngest member of our party, Buterin represents the new generation of systems thinkers working on digital coordination systems. His ability to design complex cryptoeconomic systems makes him the party's expert on digital governance and incentive structures.

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Note: This is a repost of a piece from the principles series that I published last year. I’m making slow but steady progress on some formal analyses and models which build on these informal explorations. I look forward to sharing this work soon, likely early in 2025.

The eighth principle of systems science states that systems use governance subsystems to achieve stability.

Governance is the process that systems use to regulate their long-term behaviors. Systems use governance subsystems to maintain their internal functioning and ensure they can sustain their existence over time.

This post will examine the role of governance within cryptoeconomic systems. Since the point of a blockchain is to provide a ledger of universally accepted truth, well-functioning governance is an essential aspect of maintaining the integrity of these complex systems. 

We will look at three public blockchains with very different approaches to governance: Bitcoin, Ethereum and Cosmos Hub. Examining governance crises faced by each system will help us understand the strengths and weaknesses of their governance processes.

Off-chain and On-chain Governance

Off-chain governance refers to a process in which decisions about changing the rules of a blockchain are made through an informal process of social discussion. If the decisions are approved, they are implemented in code.

On-chain governance is a process in which decisions about changing the rules of a blockchain are decided by a formal stakeholder vote. Users who hold a token with governance rights vote directly on the blockchain.

Bitcoin users who hold bitcoin (BTC) and Ethereum users who hold ether (ETH) can’t use their tokens to vote on changing the rules of the underlying blockchains. Cosmos Hub users who hold the ATOM token can.

Bitcoin’s apolitical Governance

Subsystems and Processes

“Nobody owns the Bitcoin network much like no one owns the technology behind email. Bitcoin is controlled by all Bitcoin users around the world.” — Bitcoin.org

Bitcoin’s governance process can be summarized as off-chain governance with no centralized foundation.

One useful way to conceptualize Bitcoin governance is as a system based on checks and balances, somewhat analogous to the US government.

Bitcoin core developers legislate by proposing software changes in the form of Bitcoin Improvement Proposals. Bitcoin miners who maintain the infrastructure that processes the transactions judge the validity of those changes and decide whether or not to adopt them. Bitcoin node operators have executive veto power which can be exercised by refusing to run a software version which aligns with the one used by the miners. Bitcoin users are the citizenry, who hold the ultimate power to revolt by abandoning software they don’t like, effectively rendering the system useless.

Economic incentives are the glue that holds the system together, ensuring that it is in the best interest of all parties to maintain the integrity of the system. If the miners or developers alienate the users, their tokens will also decrease in value.

This is a useful analogy for the purposes of this piece, but it is imperfect. While Bitcoin governance can be usefully compared to constitutional, corporate, and internet governance, it possesses unique features, such as the capacity for users to fork the software and create their own blockchain, that distinguish it from these other models.

“The possibility of forking means that unlike many other ways to resolve societal collective action problems, including a reliance on coercion, Bitcoin governance is based on deliberation, persuasion, volition, and choice.” — Bitcoin Governance as Decentralized Financial Market Infrastructure

Bitcoin’s Civil War

In 2015, Bitcoin faced an existential crisis. The Guardian newspaper proclaimed that “bitcoin was facing a civil war.”

The core issue being debated was “how should Bitcoin be scaled?” As bitcoin had grown from a niche experiment into a system with signs of meaningful adoption, users were faced with increasingly high transaction fees and slower processing times. The two belligerents in The Blocksize War were the Big Blockers and the Small Blockers.

Big blockers advocated for modifying Bitcoin to increase the 1MB block size limit specified in Satoshi’s original code through a hard fork. They believed this was necessary to ensure Bitcoin could grow into a payment network with the capacity to rival the likes of Paypal or Visa. 

Small Blockers fought to maintain the original 1MB size limit. They were concerned that increasing the block limit would make it too expensive for individual users to run Bitcoin nodes. This would leave large companies as the only actors capable of hosting nodes, thus compromising the network’s decentralization and security.

The debate dragged on until 2017, culminating in a “user-activated soft fork” which embraced a proposal from the Small Blockers. The block size limit was modestly raised to 2MB and the groundwork was laid for “second-layer” scaling solutions such as the Lightning Network. Dissenters forked off into their own network, “Bitcoin Classic”. The fate of the system was decided by the users.

This saga illustrates how Bitcoin’s highly informal governance process leads to situations in which governance debates are carried out in a messy, prolonged fashion because there is no formal framework for resolving internal disputes. The fact that it’s hard to make significant modifications to Bitcoin increases its resilience, but also hampers its capacity to adapt to changing circumstances. 

“The block size debate is a good illustration of this tendency. Although the debate was framed as a value-neutral technical discussion, most of the arguments in favour or against increasing the size of a block were, in fact, part of a hidden political debate.

Indeed, except for the few arguments concerning the need to preserve the security of the system, most of the arguments that animated the discussion were, ultimately, concerned with the socio-political implications of such a technical choice (e.g. supporting a larger amount of financial transactions versus preserving the decentralised nature of the network). Yet, insofar as the problem was presented as if it involved only rational and technical choices, the political dimensions which these choices might involve were not publicly acknowledged." — The invisible politics of Bitcoin: governance crisis of a decentralised infrastructure

Ethereum’s Benevolent Dictatorship

“No one person owns or controls the Ethereum protocol, but decisions still need to be made about implementing changes to best ensure the longevity and prosperity of the network. This lack of ownership makes traditional organizational governance an incompatible solution.” — Ethereum.org

Ethereum’s approach to governance can be summed up as off-chain governance steered (but not controlled) by a benevolent dictator and centralized foundation.

Subsystems and Processes

Ethereum’s governance process is very similar to Bitcoin’s. Core developers propose software changes with Ethereum Improvement Proposals. Validators (Ethereum’s equivalent of Bitcoin miners) can decide whether or not to adopt those changes. Node operators run software, and users hold ultimate veto power.

One of the most significant differences is that Ethereum’s creator, Vitalik Buterin, is a well-known public figure who remains active in protocol governance. Whereas we still don’t know who Satoshi Nakamoto (publisher of the Bitcoin whitepaper) is, and their last public message was on December 12, 2010.

Last December, Buterin published a detailed long-term roadmap that dictates the direction for Ethereum’s future upgrades. It will be followed in large part because the community trusts Buterin and depends upon him for technical vision and leadership. He can’t force anyone to adopt the changes, but the community tends to listen to him. Bitcoin lacks such a singularly powerful and active figurehead.

The DAO Fork

A crisis that took place early in Ethereum’s history demonstrates how having strong leaders stewarding blockchain development can influence governance.

In 2016, an attacker discovered an exploit in a smart contract for The DAO which allowed them to drain 3.6 million ETH (~$60 million.) The DAO had raised more than $150 million from more than 11,000 investors and its contracts held approximately 14% of all circulating ETH.

Developers at The Ethereum Foundation proposed a hard fork which would roll back Ethereum’s network history to before the attack, and reallocate The DAO’s Ether to a new smart contract where investors could withdraw their funds.

This was controversial because blockchains are supposed to be immutable and censorship resistant. There were no issues with Ethereum at the protocol level and the attacker hadn’t broken the rules of The DAO’s smart contract. The developers who wrote the contract simply had not anticipated it could be used in that way. To roll back the chain in response to someone obeying the rules of the system was perceived by some as against the ideals of blockchain technology.

The majority of stakeholders chose to adopt the change and the fork was implemented. However, a minority of users refused to accept the fork and continued supporting the old chain, which is now known as Ethereum Classic.

The DAO hack helps to illustrate how central, trusted figures within the Ethereum ecosystem wield the power to propose significant changes in times of crises and have their suggestions be accepted voluntarily by the community. Unlike Bitcoin’s messy civil war which dragged on for years, Ethereum swiftly resolved its first major crisis within weeks. However, it could be argued that Ethereum sacrifices some degree of stability and predictability in order to achieve greater capacity for rapid innovation and adaptation.

“Practically speaking, I'm a large part of the story for why the platform will get better of the next few years," Buterin said…"My short-term goal is to shift the story to, 'the protocol will succeed because of myself, plus all of these other people who are capable of moving things forward.'” — Ethereum's Boy King Is Thinking About Giving Up the Mantle

“Least proud moment? Definitely the handling of the DAO fork situation. A lot of people did feel betrayed as a result of the DAO fork. A lot of people did feel like their expectations got violated. And a lot of people felt like their opinion was disrespected, especially people who opposed the fork.” — Vitalik: Ethereum, Part 2

Cosmos’ Decentralized Political Economy

"Cosmos blockchains come prepackaged with a governance system — a decentralized process for making decisions. — State of Cosmos Governance 2021

The Cosmos governance process can be summed up as hybrid on-chain/off-chain governance with several leaders and foundations coordinating to steer development.

Subsystems and Processes

The Cosmos Hub is a blockchain which sits at the center of an “internet of blockchains,” the Interchain. Its native cryptoasset is called ATOM. Each blockchain in the Cosmos Network  is built using the Cosmos “design framework for building blockchains.” Each chain maintains its individual sovereignty while benefiting from the ability to communicate with other chains.

This architecture is significantly different from that of Bitcoin and Ethereum. Decentralized and seamless communication between Bitcoin, Ethereum, and most other blockchains is difficult because they operate under different technical rules. The Cosmos vision is one of “an ever-expanding ecosystem of interconnected, blockchain-based apps and services” that can easily talk to each other because they are based on the same protocol. 

Cosmos Hub developers sponsored by foundations like Informal Systems and the InterChain Foundation create Cosmos Improvement Proposals. Validators secure the network, and node operators run software. But that’s about where its similarities with Bitcoin and Ethereum end.

Cosmos Hub eagerly embraces on-chain governance to enact protocol level changes. ATOM token holders wield power in the form of the ability to submit proposals which are voted on by validators and delegators, committed to the blockchain, and enacted by validators.

Code-based proposals are used to suggest changes to technical, on-chain parameters, such as the number of active validators. Text-based proposals are used to do things like request funds, tackle security issues, and agree upon governance principles.

The Veto of Cosmos 2.0

In November of 2022 the Cosmos Hub community voted on the text-based Proposal #82, a referendum on a new Cosmos White Paper which outlined an ambitious new vision for the future of the Cosmos Hub that would secure its role as the key driver of growth and collaboration within the broader Cosmos ecosystem.

Under the proposed plan, any Cosmos-based chain would be able to “borrow” security from the main Cosmos hub, ATOM holders would gain new and more convenient ways of earning interest on their holdings, and a “Cosmos Assembly” would be formed, accountable to ATOM holders and managing the Cosmos Hub.

Lively discussion about the proposal took place on the governance forums, in interviews with media outlets, and on Twitter.

Ultimately proposal #82 was rejected. One Cosmos co-founder felt the proposal contained major risks. Some community members argued that the proposal was too broad, and that each piece of content mentioned in the whitepaper should be voted on in separate proposals.

After the defeat of prop #82, it appears that the “piecemeal” approach to incorporating individual elements of the original proposal is exactly what’s playing out. This March, proposal #187, which narrowly focused on allowing the Cosmos Hub to provide security to other chains in the Cosmos network, was passed with 95% in favor.

Observing how the Cosmos Hub community makes on-chain governance decisions in a highly participatory manner gives us insight into what it looks like when public blockchain infrastructure is governed in a way that resembles direct democracy, rather than the representative variants found in Bitcoin and Ethereum.

Cosmos gives users in its ecosystem enormous amounts of sovereignty at the expense of not allowing founders to push through swift, sweeping changes. Cosmos founder Ethan Buchman instead embraces the messy reality of deliberative politics as a key feature of the system.

“At the heart of Cosmos are the principles of sovereignty and interoperability — the right for communities to be sovereign over their own infrastructure and applications, and for sovereign communities to be able to peacefully interoperate with one another.”

“The lack of a singular centralized company led by a singular figurehead means decision-making can be more complex — but it can also be more representative and robust.” — The Mind, Body, and Soul of Cosmos.

Without an integrated governance structure, Bitcoin developers and users must fight battles of opinion out on public forums like twitter, leaving the system stable but slow to innovate. Comparatively, Ethereum's network is more dynamic with greater capacity to react and adapt in relation to external pressures. Cosmos opts for internalizing these political pressures as a driving force of the system, enabling users to constantly improve and modify their own ecosystem.

Public blockchains, DAOs, and on-chain organizations are all part of a vast, global “laboratory for democratic governance that interweaves civic and corporate governance traditions in a way previously impossible.”

Coinbase co-founder Fred Ehrsam wrote in 2017 that “we are currently in the primordial ooze phase of blockchain governance.” I would argue that we are now in the era of complex life emerging, with multi-cellular organisms cooperating and competing in a rapidly growing evolutionary landscape.

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Since I was moderating the discussion, I was more focused on facilitating productive conversation than on sharing my perspective. Here are a few of my personal thoughts on the importance of boundaries as a systems concept.

Philosophy and Worldviews

A significant aspect of the conversation was the philosophical one. A decent amount of time was spent considering questions such as:

Do real “objective” things exist outside of our perception?

Does it make sense to treat humans as being “separate from nature”?

For me, this highlights the importance of making our philosophical assumptions and worldviews explicit. The goal isn’t to form consensus, but rather to make it as easy as possible for others to understand where we are coming from.

My current philosophical stance is best described as “Bertalanffian Perspectivism”, as summarized in General Systemology (p.39).1

I believe that there is a universe that exists independently of the observer and that the scientific method can reveal aspects of reality’s nature. I also believe that science is limited in what it can reveal, and that the unique cognitive capacities and purposes of different agents condition the observations, models, and theories that they make.

I also largely agree with the following seven tenets of a “General Systems Worldview” as outlined in General Systemology (p. 68) :

1. We can progressively gain more complete real knowledge of the real world.

2. A real concrete world underlies some of our experiences, but experiences can also be distorted, or constructed, or hallucinated.

3. Nothing supernaturalistic exists, but concrete phenomena cannot all be reduced to Physics.

4. The concrete world is inherently systemic (but we can also project systemicity onto our experienced world).

5. Every concrete thing (i.e. everything that has causal powers) is always a real system or part of one.

6. Values are largely constructed via cultural processes, but natural systemic processes also influence them.

7. We have the capacity and freedom for un-coerced choices and actions, but our choices and actions can also be conditioned by natural and cultural factors.

These philosophical assumptions inform how I approach the topic of boundedness.

Systems Science Requires Boundary Identification

In General System Theory (p. 215), Bertalanffy states that “any system as an entity which can be investigated in its own right must have boundaries, either spatial or dynamic.”2

While I don’t hold a strong position on whether or not boundedness is an essential systems concept from the perspective of a pure theorist — I do believe that the development of General System Theory must be informed by feedback from the practice of systems science.

I don’t see how a general science of systems can progress and provide meaningful empirical data to advance theory if systems scientists don’t develop formal methods for identifying boundary conditions in all of the traditional scientific domains.

Mathematical Definitions of Boundary

So, how can we develop such formal methods?

My current focus is on developing formal frameworks for the analysis of real world systems which can inform the design of ultra-realistic computational models and simulations. As a result, I’m most interested in exploring the various mathematical definitions of boundary that have been proposed by systems theorists and scientists that can be used in the creation of these tools.

Theoretical physicist and systems philosopher Mario Bunge argued that the concept of a boundary belongs in general topology. He proposed a set-theoretic definition of a “boundary component” as an element whose every neighborhood contains at least one system component and at least one thing from the environment of that component. A boundary of a system is then defined as the set of all its boundary components.3

Andrew Cavallo argues that Bunge’s definition is deficient because he made the error of including “the environment of a system in the general definition of system.”4 He goes on to propose a slightly refined version of Bunge’s definition.

George Mobus has proposed a set-theoretic definition of an “effective boundary” that is produced by some kind of internal binding between a special set of a system’s components. A boundary, then, consists of a set of properties (such as porosity and perceptive fuzziness) and a set of interfaces that are responsible for determining whether or not substances are allowed to pass in and out of a system.5

Formal mathematical definitions like these provide essential frameworks for studying real-world systems that can support systems scientists in the important process of boundary identification.

From the perspective of a General Systems Theorist, I could probably make an argument for boundedness not being an essential systems concept. But as a systems scientist, I see no choice but to treat boundedness as a first-class and essential systems concept. Without boundaries, there are no separate distinguishable systems for me to study.

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Note: I’ve been busy this week at a Personality Hacker conference in Orlando, Florida and didn’t have time to write new material. This is a repost of one of my favorite short pieces from earlier this year that is very relevant to some of the current applied systems projects I’m working on. Would love any feedback, questions, and critiques!

During the inaugural meeting of the newly relaunched ISSS special integration group (SIG) focused on research towards general systems theories, one participant made a comment which grabbed my attention.

"Being useful is really the mark of real science."

For me, this statement symbolizes one of the existential issues facing the ISSS and the broader systems science community. It is misleading and confusing if we don’t specify what sort of science is being pursued.

Basic vs. Applied Science

To provide useful theories and novel solutions, systems scientists must make a clear distinction between efforts in basic and applied science. The distinction was made clearly in 1945 by Vannevar Bush in the report which led to the establishment of the United States National Science Foundation.1 The differences and unique benefits of each were clarified more recently in 1997 by Christopher Smith, former Director General of CERN. 2

Basic science is motivated by curiosity.

It leads to the development of general theories which give us understanding but not answers to specific problems. General theories, including atomic theory, cell theory, electromagnetic theory, and general relativity provide a shared understanding for physicists, chemists, and biologists about fundamental concepts such as atoms, cells, electromagnetism, and gravity. Developing shared foundations was a prerequisite for the advancement of applied science in these fields.

Applied science is motivated by a desire to answer specific questions.

It depends upon the fundamental knowledge of how things work, acquired through basic research. Curious minds were fixated on understanding the fundamental nature of reality. As a result, we can build semiconductors to power our computers, create stem cell therapies to regenerate damaged tissues, and construct wireless networks and GPS systems for global communications.

Systems science is still in the formative stages where there is no shared consensus about what it means for something to be a system. There are disparate "general systems theories" such as information theory, cybernetics, complexity theory, and network theory. But as General Systems Theory co-founder Ludwig von Bertalanffy stated in 1967, "they differ in basic assumptions, models, mathematical techniques and intentions and occasionally contradict each other.”3

As a result, the word “system” might mean very different things to a complexity scientist, a network scientist, and a cybernetician. This diversity makes comparing findings between the sub-disciplines of systems science in a structured manner nearly impossible.4 There is no solid ground upon which applied systems science can advance in a unified fashion.

As members of a fledgling and immature discipline, we must recognize two distinct forms of scientific utility. Basic science can provide broad universal principles which apply to systems in general. Applied science can test theories and help solve systemic interdisciplinary problems. While the two domains develop in parallel, understanding the unique personal motivations driving each is crucial for progress.

I believe that success for the SIG, the ISSS, and the systems science community as a whole requires developing distinct strategies for pursuing basic and applied systems science along with strategies for facilitating effective collaboration between the two.

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During the symposium on political economy I attended in September, there was a strong emphasis on the idea that economists should be studying markets as active, ongoing processes rather than static, planned outcomes.

Lynn Rasmussen’s recently published book Seeing: A Field Guide to the Patterns and Processes of Nature, Culture, and Consciousness highlights Len Troncale’s Systems Processes Theory (SPT) as a promising theoretical framework for advancing systems science. SPT and the concept of process has been discussed at great length during meetings for the ISSS working group on research towards general systems theories.

A couple of weeks ago, I came across a paper titled Economics in Nouns and Verbs written by Brian Arthur.1 Arthur — coming from a background in electrical engineering, operations research, and economics — has helped pioneer the field of complexity economics during his long tenure at the Santa Fe Institute.

Arthur’s core argument is that standard economic theory, grounded in algebraic mathematics, is inherently limited because it “restricts economic modeling to what can be expressed only in quantitative nouns, and this forces theory to leave out matters to do with process, formation, adjustment, and creation—matters to do with non-equilibrium.”

After making his case for the limitations of the algebraic approach, he urges economists to add algorithmic mathematics to their arsenal as a tool for formally accounting for and expressing verbs, or processes. He argues that:

“…admitting process to economics would reveal a world where structures large and small continually form, where agents and organizations continually respond to their internal and external environment and change from within as they do, where fresh undertakings continually create novelty. The economy would become a living thing.”

I thought the paper presented a timely and lucid critique of mainstream economics along with a compelling partial antidote. I’d consider it a must read for anyone interested in taking a systems approach to understanding economics.

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