From behind the veil of a screen on that day, I explained what happened at the firm and briskly requested a meeting for new connections of opportunities. Within and with my family, the situation catalyzed a deep period of reflection about life and career that had been lingering. This was amplified by the pressure of maintaining a household and a raising a child, and trying to minimize disruption - at first.

Through the community I’ve developed through Emergent Outcomes (my personal think tank and sometimes consulting platform), and my network of complex systems thinkers, I was introduced to the CEO of the Dutch agriculture technology firm Meridia. The firm has an origin in farm plot mapping in Ghana, Ivory Coast, and Indonesia, and now develops a software product to assess risk in sustainability and compliance of deforestation in the agriculture commodity supply chain. The CEO and I had a discussions about satellite imagery, index development, risk evaluations, and the human side of farming in a global trade system.

He asked me “would you be interested to interview for this new position, and if you get it, would you be willing to move to The Netherlands?”

Yes.

I share with you now that I am the Head of Data & Methodology for Meridia, and I write now from Amsterdam, in The Netherlands. So again, quite simply told, both items worked out, and a new adventure began.

In reality, the physical, emotional, and logistical aspects of this life chapter have been messy, hard, rewarding, and exciting over the past year. Many cherished things faded away, like a land in New Mexico we love, the familiarity of things, routine hang outs with close friends, but also projects, routines, and my updates to this newsletter.

On the other side of those challenges has been so much growth, both personally and professionally. So now, feeling even somewhat settled I wanted to write again and share what I have been up to and thinking about.

I reviewed this guiding question from that post recently:

“what do you want to do more of, and what do you want to do less of?”

• More work at the intersection of climate, technology, and capital

• More team building

• More networking with a wide variety of people

• More research and deeper analysis with a systems science framework

• More ownership and accountability of strategy

Working at the intersection of climate, technology, and capital is the place for me right now. There is no greater reminder that we are all connected than the global supply chain, which links the lives of farmers to the security of society through sustenance. It is clear that new challenges and risks are emerging, as changes in weather, ecosystem decay, and market dynamics unfold. Managing these risk of increasingly relies on high quality, localized data to structure financial contracts that result in a sort of resilience to the overall system by distributing risk or production among the right parties. This combination of planet, people, systems, technology, data, and markets combines the professional, academic, and life experiences I’ve been developing for myself over the past decade, or more.

The move to Europe has enriched my relationships and interactions with a whole range of cultures on a daily basis. I manage a team based in Indonesia, Amsterdam, and Lithuania. My colleagues are Dutch, German, Greek, French, Ghanaian, British, Ivorian, Mexican - that is just a sample. Our clients are international commodity businesses with global operations in Africa, Latin America, Asia; truly it is a reminder that we all share this planet together.

My role as Head of Data & Methodology I consider systems science and cybernetics to be a lens through which I view the world and problem solving. The software accepts the geospatial properties of farm plots as inputs, and returns a risk score across multiple dimensions, such as deforestation, legality of production, and human rights. What information is required, and how much information is enough to form an accurate evaluation of an esoteric risk? How granular of an assessment can we make with the information we have available about a plot of earth and the products that come it?

I view this as an increasingly important capability within the global economy. Sustainability, in the simplest sense, is about choosing actions that result in the long term viability of an asset to produce what’s intended. For agriculture, this means practices that preserve nature and ecosystems so that the land we have currently available remains available, and even increases yield, to support the population of the earth.

In these updates I will continue to share the deeper, more technical, systems side of my thinking and personal philosophy. This is my corner of the digital space to muse a bit more, and connect with other like minded systems thinking folks across disciplines. If you are reading this still after a year gap of no updates from me, then thank you. Many of you have sent me emails with ideas or further questions in the past. These messages always delight me, so thank you for those as well and keep them coming.

In fact, I would love to hear what you have been up to in the past year yourself. Any new ideas or directions at the intersection of business, art, and life for you?

On November 1 I was laid off from Arbol. Due to restructuring the company eliminated my Director of Data Engineering position and let go me and a handful of my colleagues in Tech and Data. This was a surprise to me; I was aware the company was looking to cut costs, extend runway, and consolidate focus into main business drivers (like many startups right now), but I did not think I or my team were the targets for those savings. Those receiving the news rarely do, I believe.

I posted on LinkedIn about my situation and that I’m seeking career opportunities. One path I’m seeking is senior leadership roles at the intersection of climate, technology, risk, and capital. Another, as I mention in my LinkedIn post, is a relaunch of my consulting firm Emergent Outcomes LLC with a focus on analytics engineering and technology implementations for climate, risk management, and market driven businesses.

The outreach I received last week from my post was both staggering and inspring. I booked more than twenty calls with all kinds of creative, innovative leaders and professionals looking to test out or scale up an analytics idea. Currently I’m working on closing a few of those into booked contracts.

So what’s next for me? For one, while I am having these conversations and building my network I reflect on what kind of work I enjoy and am good at, and what I want to do less of. As a leader this is a question I always ask my team “what do you want to do more of, and what do you want to do less of?” - now I turn that question inward and explore it myself.

• More work at the intersection of climate, technology, and capital

• More team building

• More networking with a wide variety of people

• More research and deeper analysis with a systems science framework

• More ownership and accountability of strategy

I’m still reflecting on what I want to do less of.

Do you want to book a call with me? Go ahead, I would love to hear from you 👉 https://calendly.com/christianlemp

Relaunching Emergent Outcomes LLC is full of excitement for me. The creative freedom to seek opportunity, build solutions, and work with all kinds of people from different backgrounds is inspiring to me. Thank you for reading, and stay tuned with me to be on this journey together.

In the field of cybernetics there’s a law about how control systems must be implemented in order to be effective - the Law of Requisite Variety from W. Ross Ashby. In active information systems, designed to respond to disturbances with an appropriate response to will bring the outcome back to an acceptable range, Ashby proposed that there must be as many “regulators” as there are “disturbances” hence there must be requisitite variety of controls. So what is a disturbance? Well that depends on the system. Anything that threatens the stability or predictability of outcomes.

For my work in data engineering this cybernetic concept becomes a very real and important aspect of the job. When we’re maintaining billions of data points from dozens of external sources and processes to transform them into predictable format the system is constantly under threat from disturbances - servers going down, data is corrupt, bugs in code, changes in requirements. The list goes on. How to keep all of this under control?

As a manager I’ve learned a few best practices; some are technical controls and some require human intuition. First, technically, use unit tests to decompose a complicated system down into individual unit functions with expected inputs and outputs. When a process breaks the root cause can be identified down to the specific function and controls (error handling) can be introduced. In systems design, reducing the number of components to the minimum while still meeting performance, cost, and other business requirements is another good practice.

On the human side, it’s all about developing context for the information we maintain. Knowing right from wrong when it comes to bits requires “data intuition” and that’s a sense good data engineers develop over time working within their domain. I take more traditional systems view of information in that I focus on the bottom up - how was the information generated in the first place? And what was its journey to my terminal?

This last part about intuition, it really comes down to approaching problem solving with a wide open mind, and having curiosity for life and learning. The bigger mind we develop, the more variety we can manage. In the words of W. Ross Ashby: “only variety can destroy variety.”

I’ve changed up my academic schedule for this semester and opted to replace Modern Complexity Theory with a higher level graduate course called Foundations of Adaptive Optimization, taught by Dr. Sara S. Lam. Hiroki Sayama suggested I focus on upper level course with research focus and this is a nice one with lots of practical applications for my research and work.

The point of this course is to develop creative ways of finding the optimal solutions when there is just too much darn data to iterate through every possibility. In those situations there’s inherently this question of “have we reached the top?” The “top” in this sense is often metaphorical - a mathematical formulation to represent an objective we’re trying to maximize or minimize.

Sometimes this objective is straightforward; for example in my work managing data often the goal is to minimize cost of infrastructure without giving up too much performance. But how about those examples where we’re trying to maximize team satisfaction? This kind of problem is much more art than science (but science can be a guide).

Nature is an inspiration for me. Life has a propensity to optimize for survival, and in doing so all sort of creative approaches to coordinating efficiently emerge. I want to learn from those nature-based systems for my work in information and people leadership.

Nature is also a complex adaptive system, always changing, always evolving. So that adds an additional wrinkle to staying on top - what is the algorithm to not only find the best, but stay there as the landscape changes?

That what I’ll be learning about over the next few months, and I’ll be working on some research methods to explore problems that I find interesting in this space.

Do you have any problems in your domain that you think we can take inspiration from nature to learn about?

I’m working on a few longer form pieces for this newsletter, but want to experiment with shorter, more frequent updates. I find it hard to consistently write the longer, more in depth articles so I’ll start sharing more concise writings about my pattern finding in complexity for business, art, and life. I love to hear from my readers so please, if something I write about interests you particularly let me know. Thanks for coming along with me on the journey.

My small world network led me to conversations over lunch at Santa Fe Institute over the past few months. This week was one of those, and my good friend Alex McCaig who runs Tartle came as well. Carlos Gershenson has been on sabbatical at SFI before joining Binghamton University as a professor in the Systems Science department. I will take his graduate course this fall, Modern Complexity Theory. And so we’ve been in touch while he’s here.

The lunch discussion among a small group of faculty and visitors focused on one paper in particular, but ranged way, way beyond. The World as Evolving Information outlines a framework for modeling systems through the lens of information shared between components. This meandered into a debate, can the human mind alter the physical body? And what are the boundaries, or depths, information can go? Well Vajrayana Buddhism has lessons to guide the paths to answering these questions.

This most abstract understanding of the world is in line with the “highest view” of Vajrayana Buddhism [45]. Implications at this level of description cannot be right or wrong, because there is no context. Everything is contained, but no information is needed to describe it, since it is already there. This “maximum” understanding is also described as vacuity, which leads to bliss

• The World as Evolving Information, Gershenson, C. (2012)

The physicists and biologists at the table denied that human thought can be transformative at the physical level of cellular structure. But if we’re information all the way down, why can’t thought (described as information) influence structural change at the lowest level of our body? Not that it would be easy to do so. But perhaps enough energy can be channeled to make microscopic changes that propagate up to noticeable change at human scale?

The debate became what I might call lively and sort of intense. Several people disbanded inconclusively or even unwilling to go there with the thought of possibility. Blasphemous, or ignorant even, the naysayers claimed. The challenge is that it’s hard to predict what changes at the micro level result in through the pathways of hierarchy that make up the whole.

But to be honest, I still believe it’s possible, maybe even obvious. Provable? I’m not sure. My view was in the minority with this argument. Alex joined me there. So at lunch on that day, I’d say we got to be the scientific renegades up in the towers of complexity science.

What do you think?

He was Marco Pierre White, an icon of cuisine and eventual sage of art, creativity, and human insights. This quote of what he regards the “most important knowledge in life” touches me deeply.

Letting nature lead the way

Marco created order in his environment by reducing all noice (except for his own) and demanding specific results over, and over again without variation. Nature creates order in more creative and often subtler ways, allowing for diversity and randomness. That is why we receive such beautiful bounty from the earth.

Though nature’s art and intelligence is not limited to food. It goes much deeper and I believe is fundamental to our universe. Once structure is given to disconnected components via meaningful interactions, something comes alive. Part of that elusive “something”, that which origins of life researchers consistently question and seek, is the way that information is shared and received within systems.

My systems research is focused on how models of information theory in biology can be extended to create order within the blurry, biased, tangled concepts of groups and human collectives. When I talk about “information” and “structure” what I mean is summarizing communications as discrete messages (“hi”, “hello”, “what’s up” → “greeting”) and using Shannon information formulas (or extensions of them) to quantify these messages. I believe that somewhere encoded in these exchanges are fundamental truths about how we collectively organize and coordinate our desires.

Through this research a small world has connected for me recently that I am very happy about. For my PhD coursework I’m doing an independent study with Dr. Hiroki Sayama to understand how centralized and decentralized systems coordinate at different scales and reach various outcomes through their interactions. He connected me to Dr. Carlos Gershenson who is on sabbatical at Santa Fe Institute before joining Binghamton University as a professor this fall.

Dr. Gershenson’s work is focused on cybernetics, artificial life, and self-organizing systems. He invited me to Santa Fe Institute twice last month for the type of meandering, scientific pondering that happen at the storied old monastery of science high in the hills above Santa Fe.

Our chats led me to focus on three specific aspects when thinking about how to quantify and analyze human collectives:

1. Complexity, in the organizational sense, is correlated with the quantity and variety of information that an entity can receive and meaningfully process

2. Quantifying information processing at different scales (individual → group → organization) requires different measurements, different language

3. Connecting entities at the lower scale to those at the higher scale provides a framework to understand emergence, and to try and direct it via targeted incentives

David Krakauer, president of the Santa Fe Institute, describes the foundations of this thinking in the Complexity Explorer Lecture What is Complexity? - particularly in the Framework for Emergence section I’ve linked to below.

The hard part about humans

Why are human organizations, whether it be a fine dining kitchen, or large, multinational companies, seemingly so much more complex than the digital systems and computer architectures built to process bits and run programs?

Back to Chef Marco. It is because humans have heart, spirit, emotion, and each of these creates noise in the messages we send to one another during our bids for connection and communication. Have you ever left an exchange feeling uncertain, unsure how your messenger intended you to feel by their words? Of course you have, it is human, after all. It’s what makes us beautiful.

Science gives us tools to ask questions about our surroundings, dig deep into the structures and interactions that lead to our lived experience, and help us feel a sense of place both as one and many.

How do individuals become groups? How do norms and behaviors form as groups combine, leading the emergence of culture?

I suspect the answer has something to do with the way information is shared, understood, and propagated forward through time by the entity - whether it be the individual, team, organization, or society. However, this requires some definition of “entity” at each scale. And as our focus zooms out to higher levels of human organization physical boundaries become more and more fuzzy; the individual is bound to their physical body, but membership to a specific society becomes more of a scale as the individuals within have varying senses of self-identity and belonging to the larger group.

Perhaps this fuzziness and complexity as group size increases is the reason for the management adage of keeping “two pizza teams” - teams no larger than can be fed by two pizzas. For larger groups more structure, order, and communication norms must be imposed to reduce the variety and complexity of human idiosyncrasy in order to achieve predictable results.

Why care?

Our world, and the information economy is only becoming more complex.

1. Information is generated at ever increasing velocity and granularity - see social media, niche blogs (like this one), apps and tools to communicate

2. Remote work increases some signals and decreases others - body language and physical indicators give way to digital messages and video

3. Formation of micro cultures and communities of self-identity that have different rules and expectations for conduct

4. Asynchronous communication puts increasing importance on structured, organized communication of thoughts instead of nuance - a challenge and new skillset both for the messenger and receiver

When simplifying complexity there is no free lunch - someone must do the work of reducing noise and creating signal from the bombardment of information. However, knowing how to reduce noise but retain the heart and spirit of a team is the closest thing to art I know of in management science.

Closing thoughts

Chef Marco ends the quote from the beginning of this post with this:

I’ve written about the connection between food, kitchens, chaos, and order before. My heart lies with the topic. My years spent working in kitchens shaped my approach to management, cooperation, time management, and creativity. As my interests and career path evolved into a much more scientific, knowledge based trade of systems research and data engineering I carry forward hard earned lessons and find ways to blend my past and future.

The guiding principle of scientific management in the information economy is that while quantifying and creating bits from actions is useful to understand patterns and create predictable outcomes, a manager can never escape the heart of the team and all the irrationality that comes with it. This is a strength of human organizations that should be appreciate and utilized with care.

A new year brings hope. Arrivals of a new job, new family are moments of change too, each bringing new dynamics to life as lived. These events could also be marked by departures; year end, left a job, saying goodbye. The perspective of whether you're saying "hello" or "goodbye" changes the system for navigating each event.

Much of my work in data engineering and information design can be simply explained as creating mental models of significant arrival and departure events, then creating technology systems to manage that information. Designing and documenting these mental models is a systems thinking exercise. Imposing order onto the fuzzy, unclear, complex business of market interactions feels zen. That said, the intuitive, sometimes emotional process of creating order from seeming chaos is not always easy.

A significant personal arrival for me this year was joining Arbol this year to lead the company's data department. My team manages the arrivals of weather information into our data systems and the departures of transformed versions of that data into products and services throughout the company. With that, I've had the interesting opportunity to learn about climate events and the methods used to collect and model the weather - truly one of nature's premier complex systems.

My research interests have been influenced by this work as well. Arbol invested in a creating a distributed information backend with InterPlanetary File System (IPFS) for its benefits of immutable records of information, operability with blockchain protocols, and decentralized network of data providers. How information is deconstructed and reconstructed via networks of connected nodes lends well to principles of self-organization, nature inspired computing, and complexity science.

In my personal life I follow a loosely organized version of this structured mindset. Sometimes I track my health, sometimes I don't. I never count calories. My son grows, seeing new milestones, and my wife and I navigate life together, sometimes counting but mostly feeling. I love and appreciate them and their support of me. Balanced optimization is a guiding principle, both in business and life.

As we depart one year and arrive into a new one I reflect on what I want to bring with me and what I will leave behind. For one, I will bring Emergent Outcomes, and all of you reading this, with me. I keep as regular a writing schedule as I can, and I thank you for your interest and patience.

This newsletter began as my outlet to muse on systems thinking for business, art, and life, and build connections with an interesting audience of thinkers, doers, and dreamers all the same. Through this writing I’ve met new people and encountered new ideas, creating a platform to share my interests and receive feedback in return. I look forward to discovering what arrives and what departs in the year to come.

What are you looking forward to?

Where did September go? Thank you for being patient with me and my newsletter while I only just barely held onto time as it barreled by this past month. I’ll use this week to share some personal and professional updates in my life.

Personal

My son turned one year old in September, which provided moments of awe, wonder, and pure curiosity for how my wife and I managed through such a big year of change via parenthood. It is a hard job. Life, and the love surrounding it, has expanded with new friends, connections, and experiences as a growing family. Many of them we celebrated and spent time with in generally offline, “touch grass” type of settings. I think it is healthy to separate and recharge occasionally.

Professional

I’ve started a new role as Director of Data Engineering at Arbol, a parametric insurance and derivatives company for hedging climate risk across the world. Renewable energy and agriculture are two of the focus industries right now. In addition, the company believes in the power of decentralization and has invested in creating dClimate, a network for critical climate data feeds and geospatial datasets. My team is fully remote and I will continue building an outstanding distributed, async team culture.

This means I have left the Chainverse and Diamond DAO core teams. I remain deeply interested in knowledge graphs, collective search and discovery, and blockchain technologies. However professionally I needed a change and a new problem domain to learn about.

Academic

Additionally, I continue taking courses toward my Systems Science PhD. This fall I’m taking Advanced Topics in Network Science with my advisor Hiroki Sayama. Here’s a list of papers I’ve read for that this past month:

Network science

• Barabási, A. L. (2013). Network science. Phil. Trans. R. Soc. A, 371(1987), 20120375.

What are the essential concepts about networks?

• Sayama, H., Cramer, C., Porter, M. A., Sheetz, L., & Uzzo, S. (2016). What are essential concepts about networks?. Journal of Complex Networks, 4(3), 457-474.

Computation of the traveling salesman problem by a shrinking blob

• Jones, J., & Adamatzky, A. (2014). Computation of the travelling salesman problem by a shrinking blob. Natural Computing, 13(1), 1-16.

Exploring network structure, dynamics, and function using NetworkX

• Hagberg, A., Swart, P. J., & Schult, D. A. (2008). Exploring network structure, dynamics, and function using NetworkX. In Proceedings of the 7th Python in Science Conferences (SciPy 2008), pp. 11-16.

Collective dynamics of ‘small-world’ networks

• Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’networks. Nature, 393(6684), 440-442.

Emergence of scaling in random networks

• Barabási, A. L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509-512.

Community detection in networks

• Fortunato, S., & Hric, D. (2016). Community detection in networks: A user guide. Physics Reports, 659, 1-44.

Graph spectra and the detectability of community structure in networks

• Nadakuditi, R. R., & Newman, M. E. (2012). Graph spectra and the detectability of community structure in networks. Physical review letters, 108(18), 188701.

Quite the list! The themes connecting each of these papers are computation of network structures and algorithms to define communities within graphs. As I settle into my new role and find a new rhythm for updates I will be sure to add more insights to future Emergent Outcomes newsletters.

Thank you for reading and sticking around while I create some order during a time of change in my life. Please reach out or leave a comment if you are curious about anything here.

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Above is a short video showing how the rumor of the Higgs-Boson particle spread across Twitter. This was a team project I worked on at NECSI, the full presentation is here.

I believe the structure of networks - representing elements as nodes and relations - is a principal pattern of organization in our universe. They can be used to model social networks, information systems, biological ecosystems, businesses, and more. It's often intuitive to think in networks; there's an essence conveyed through nodes and edges.

Network thinking refers to a scientific mindset where the focus of study is on the relationships between entities rather than the entity itself

• Complexity: A Guided Tour - Melanie Mitchell

In this photograph from NASA I see a network of cities scattered across our earth. Lights on means people are home, then scaling in I see neighborhoods, suburbs, and countryside, all interconnected by trade, family, and communication.

That amazing feeling of being connected to one another is well illustrated by a graph. The idea of edges connecting nodes and carrying meaning is intuitive. We think about our friends, family, neighborhood and can imagine an intricate web of relationships. Then, scaling up, to consider nodes as connections between nations, cultures, or entire species.

Small worlds

When I moved to Santa Fe I quickly found friends of friends and became friends, expanding my network. This small world we know has a model. In 1998 Collective Dynamics of Small World Networks by Duncan Watts and Steven Strogatz was published in Nature. This paper provided formulas and a structure to represent this phenomenon of social connections with graphs.

It turns out this pattern of social connections mimics the neural pathways within our brain. I do wonder if these small-world networks we intuit so vividly are intuitive because the network model of interactions maps to the architecture of our mind. From neurons to receptors and into the formula.

As with healthy scientific debate there are nuanced views to this idea of the brain as a true “small-world network.” This published paper Is the brain really a small-world network? is intriguingly classified as Brain Mythology in the open access Brain Structure & Function journal. The paper presents that perhaps the small-world network is a good conceptual starting point but the actual architecture of the brain is more connected and more complex; even moreso at different scales.

Finding meaning

So what is the use of these nodes and networks stuff in practical application?

Applying network thinking has practical application in nearly any domain. Networks are a simple model framework that describes a diverse set of systems: from economics, to biology, social science, language, and beyond. Intuition for networks seems universal in my travels as a data communicator. Aesthetically they provide a vast canvas to express creatively.

So I encourage you to ponder your world as a network of interactions. Try this network thinking framework to work through the next complex problem you face. The elegance and simplicity of a universal structure can be clarifying. By thinking in terms of nodes, relations, and interactions you may tap into a pathway for problem solving that is not only useful, but perhaps in coordination with a universal order of structure and organization.

Thanks for reading

If you felt particularly curious by anything in this article, please leave a comment. Do you have questions or ideas to discuss? Please share.

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Agent-based modeling philosophy

Agent-based models are endlessly fascinating to me. Instead of starting a model with a large dataset of historical data and trying to find statistical correlations, the agent-based modeler tries to recreate the dynamics that generated that data in the first place.

There's something curious and interesting about searching for the essence of a dynamic. Simplifying the chaos to a few simple parameters feels like a mastery of nature, if only for a moment. The fewer number of variables the better - simplicity wins.

And yet this godly scientific moment is illusory. The simulation and the story that can be inferred by data is bounded by the modeler's design.

"We should recognize that systems originate with us, human beings. We construct them by making appropriate distinctions, be they made in the real world by our perceptual capabilities or conceived in the world of ideas by our mental capabilities."

• George Klir, Facets of Systems Science

Variables that matter

A famous complex systems model is the Boids model of distributed behavior by Craig Reynolds to simulate flocking behavior of birds (paper here). This undulating flow of agent birds across the screen has only three parameters: separation, alignment, cohesion.

Values between zero and two generates the complex waves like starlings in flight. Try for yourself with this P5.js Flocking app by Coding Train . Example parameter values

separation = 2.0

alignment = 1.4

cohesion = 1.0

However, arbitrary parameters are not anchored to a measure in the real world. What does 1.4 alignment actually mean? How could that possibly be measured?

Without understanding the meaning behind these parameter values in an agent-based model history can be repeated, but the behaviors creating the future cannot be influenced.

Behavior and context

Behaviors are everywhere in our digital lives. Invisible, hidden and collected by our phone, browser, and institutions. Predictions calculated about what we'll like, and each one of us has our own personal digital journey through the web.

Frequencies and stats like "likelihood to click this ad based on all the ads we've ever shown them" are trivial to implement but don't give any explicit understanding as to *why* that action was chosen. This understanding is implicit inference.

Actionable agent-based models will have behavioral parameters linked to explicit human behavior data, which can be collected via surveys, studies, and polls.

Beyond the build

Still, a modeler cannot escape their role as constructor of narratives and a small sliver of a bounded universe of interacting agents. When data scientists and executives eager to try new techniques there’s a tendency to “just collect the data and we’ll figure out what to do with it later.”

Data collection is biased, and questions have power. There is a reason why politicians fight over census questions - data is power and can be used to tell stories. So this endeavor of data curation must be thoughtful and, of course, kind

Building an agent-based model is not trivial. It takes a creative mind to observe the chaos of reality (drank through a lens of id, ego, and uniqueness of self) and distill the behavior essence to a few interacting variables drawn through time.

“ABM is a mindset more than a technology”

• Eric Bonnabeau, Simulating Human Systems

This 2002 paper Simulating human systems by Eric Bonnabeau had a large influence on my interests and perspective around modeling and simulation. He discusses real-world applications for agent-based models of organizations, markets, and more.

Agent-based models for simulating human systems are useful when the population is mixed with different personas, the interaction between people is complex or nonlinear, and when the aggregate behaviors are emergent or feedback loops signaling with the environment.

Nature is the ultimate distributed system; the outdoors is a great inspiration for systems thinking. For all the constituent parts there are rules of interaction, randomness, and a shared ecosystem where dynamics unfold and create future pathways of being.

To reiterate Bonnabeau: agent-based modeling is a mindset. And the deeper we can go into the human psyche to actually understand and measure behavioral motivations, the more impactful our models and simulations become.

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Hello there ⛰.

When I meet people interested in systems thinking or complexity science they often tell me their story of having a paradigm shift in perceiving the world and problem solving approach. There’s an “aha” of appreciation for the intricate connections between things and their environment, and the compounding effect that one small change can have over time.

I am the same way. If I trace back my interest in complexity it takes me on a journey to the early 2000’s wave of new media art. I remember Jared Tarbell’s Complexification.net seeding a curiosity in me about recursion, fractals, networks, and using computers to simulate and explore the beautiful effects of randomness, elegantly ordered.

I also love Sand Dollar, another Tarbell work from 2004 (made with Processing, no less) that I look at today with renewed wonder. How simple changes to base rules create recognizable but unique iterated patterns. There’s a poetry to seeing life through this lens.

This early interest in aesthetic simulation led to a model thinking framework through which I began to see many patterns in life. The vast expanses of nature’s order could be recreated simply through code and variables. I discovered Dan Shiffman’s Nature of Code, which used Processing to create particle systems, automata, fractals: the models of complexity science.

Then there is the technology used to explore these domains. Once I began a career I found information systems and data engineering. Inspirations from my early interests carried through to me working in pseudo operations research and data modeling. Sequence, process, dynamics of change over time.

Though my complexity “aha” occurred at the NECSI Winter School program in Cambridge, MA; two weeks of intensive foray into foundations of complexity science with researchers, practitioners, and curious minds from all over the world. With its conclusion I felt like I had a different, more organic psyche to problem solving and observation.

I began to problem solve from the bottom up. Name the parts, define the interactions, create parameters, introduce randomness, simulate, rinse, repeat. Unlike the big data hype that encouraged executives to “just get the data!” and architect massive data lakes to support a dream that deep learning algorithms will find valuable correlations if you just feed the machine enough data, I start from the other side of the problem.

How is information being generated? What is the frequency and source of signal? What does it mean when you record it in this format? Time and time again delivering large scale data science solutions for organizations I’ve seen that less is more when the meaning and source of data is deeply understood. There are a few reasons for this.

First, more data means more to manage. The incremental cost - both capital and human attention from the engineering teams - yields diminishing returns to model outcome, which rarely justify the economics; e.g. 1.2% improved model performance yields no additional capital benefit to the business process, but costs an additional FTE to maintain the data pipeline.

Second, just because data is captured doesn’t mean it can be integrated. Sometimes information was just not designed to be combined. One of the big projects I worked on in the past five years was building a “customer journey” data asset to connect every interaction across many corporate functions. It required a lot of work and fuzzy matching algorithms (that were still quite imperfect) to match the customer profile in one department to the next. To fix these issues we recommended going back to the source and defining common keys to use across the company.

Third, data within meaning is nothing. Even worse, meaningless data combined with other data pollutes the entire dataset. Data costs something to store and capture. So there needs to be a very clear purpose for keeping that information. And there is no purpose without meaning.

With these three principles information management can be greatly simplified. Just as immense and beautiful structure can be created from simple rules with good aesthetic, great systems of insight can be built with simple, thoughtful information design. I apply complexity science thinking to my approach for designing technology systems, and if I trace my roots back far enough there’s art to it all.

Do you have an aha moment that inspired interest in complex systems?

If so I would love to hear about it. Either leave a comment on this post 👇

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Or reply to my tweet 👇

The discussion introduced the concept of a coordination discount and opportunity multiplier.

What I like about Jon and Spencer’s thoughts here is the beginning of a model to quantify the relationship between different aspects of a community.

So in this series I will conduct a journey of model thinking and build a simulation of these dynamics to amplify our intuition about the dynamics of governance value. And you, as a reader, are welcome to join this journey. I begin without an end result or final system of equations determined yet. Discussions will be encouraged on various platforms and your feedback will influence the outcome. As I release this series I will keep a Python notebook of code to generate data based on what’s written.

Truly an emergent outcome.

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Opening musings

I am interested in the way value and impact influence each other, both at the micro and macro levels. So we have two levels of scale: individual and the community as a whole. Also, I want to think through the coordination parameters mentioned in the tweet.

Here is a Python Jupyter notebook with code to work through these ideas as they evolve in this newsletter series: Governance Value Curve.

Base value

Moloch is a DAO and governance framework with an interesting history. Unlike token governance Moloch DAOs use non-transferable shares to vote and give claim to a proportion of the treasury. Read so much more about them here. I am a fan.

Base value is the proportion of the treasury you can claim with your shares.

TREASURY_AMT = 100000

SHARES_PCT = .03

base_value = SHARES_PCT * TREASURY_AMT

Base value is 3000.

Source code

Coordination cost discount

There’s a “tax” we pay to coordinate in a DAO. Checking chats, hopping on community calls, sifting through documents, forum posts, proposals, timelines.. this takes work! So this parameter must reduce the value of governance shares as coordination becomes harder. To be extreme, governance is worthless if it takes infinite energy to coordinate.

Let’s assign a number between zero and one to a variable. This is the percent of value leftover after to spent your time keeping up with the community.

coordination_cost_discount = 0.85

Value is 3000 * 0.85 = 2550.

Source code

Coordination opportunity multiplier

While coordination takes effort, it can bring multiplicative value in the right community. For example, in well connected groups coordination in one area brings about value in another because of social dynamics.

What is the range of values that can take on? From 1-1000? 0-1? -1000 - 1000?

This variable is trickier for me to work out. If there is no value to the adjacent opportunities in a group, it shouldn’t penalize the base value. At a minimum it has no effect at all, so I think a minimum value of one accomplishes that.

The hard part is deciding a maximum. How to quantify the most economic value one could possibly achieve given their connections to others? I’m going to make a modeling decision for now and declare that the most one could ever benefit is by doubling their current base value. 2x. Simple, for now. This one I’m sure I (and hopefully you) will come back to and help define more thoughtfully.

coordination_opportunity_multiplier = 1.19

Value is 2550 * 1.19 = 3034.5

Source code

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Finding equilibrium

An important I have to ask: is the juice worth the squeeze?

Are you getting enough value in exchange for the time it takes you to maintain it? That’s a personal and complex question but in this model we’ll make it simple: does the base value go up or down after adjusting for coordination?

If up, it’s worth it. If down, it’s not. The choices an agent makes can be programmed in; I’ll leave that for another deep dive. To simplify, an equation.

coordination_eq = 1 / coordination_cost_discount

Equilibrium is 1 / 0.85 = 1.1764..

Source code

Calculating value

In this simple world value is (currently) calculated as the product of each above term. Economic claim associated with that governance, times the effort and benefit of coordination. Visualizing this coordination adjustment as a heatmap shows that we want to be up and to the right - low friction, high opportunity. Sounds reasonable.

Source code

Next up

Now that we have a rough equation of value, next we’re going to think about impact. After that, dynamics, simulation, visualization, interpretation. Like I said, I do not have the end result already created. The notebook for this analysis is here.

Inform the direction of this series by leaving a comment. Here are a few prompts.

• How do you think about impact, both at the individual level, and community as a whole?

• What should the max value of coordination opportunity multiplier be?

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Follow JonathanHillis and spengrah for some of the most thoughtful, forward looking takes on DAOs, governance, and web3 meets irl culture building.

Simple molecules from our earth get cultivated, organized, shipped, distributed, combined, then served for a sensory experience. In every culture people gather, form tradition, and find joy in the creative, sensory expression of a chef and their story.

I wrote about working at a three Michelin star restaurant (nearly ten years ago now) and with my research into complex systems I reflect on the balance between chaos, order, and the sweet spot of creativity in between. It takes a lot of order to keep the food system running.

Intricate global supply chains churn and deliver ingredients to central distribution centers, which then send raw foods to restaurants where raw compounds are transformed into a sensory experience by brigades of chefs.

Management thoughts

Our food systems are still at the whim of natural phenomena like weather patterns. Supply chains are delicate and can break down unexpectedly. Kitchens are known to be riotous, demanding workplaces. Diners are fickle and customer preference changes over time. Yet through it all, cuisine prevails and culture continues. Creativity remains.

To achieve desirable emergent outcomes from a complex system you need to design both rigid order and unbounded creativity. The question then is where to impose order, and where to allow creative freedom?

Lesson 1: When results are time bound, impose role clarity.

You’ve heard the phrase “too many cooks in the kitchen.”

In the late 1800’s French chef Auguste Escoffier, working at the Savoy Hotel in London, elevated fine dining by implementing the brigadier system into the kitchen. What we know today in western cuisine - head chef, sous chef, station cooks, expediter, dishwasher, etc. - is the result of Escoffier imposing role clarity and focus into the system to reduce the riotous chaos of too many ideas and unclear expectations about decision authority.

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Lesson 2: Complicated is easy, simple is hard. Choose simple.

The amazing documentary Jiro Dreams of Sushi follows the life of Japanese sushi master Jiro and his routine toward perfection. Every detail matters, and is taken seriously - how the rice is cooked, fish cut to razor precision, presentation, and honor to the craft.

The constraint of simplicity channels creativity because there is nowhere to hide uncertainty, sloppiness, or incomplete thoughts. The team must focus on the most important principles of the problem you are trying to solve. Complicated solutions are often the result of unclear role clarity (see Lession 1), or incomplete understanding of the problem domain itself. When a solution becomes too complicated, either delegate decisions, or learn more about the problem itself.

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Lesson 3: Give space to make mistakes

There is a bit of lore surrounding the molten chocolate cake dessert.

Legend has it a young Jean Georges Vongerichten was cooking for a banquet in New York City and was in a rush to serve dessert. He pulled the chocolate cakes from the oven, quickly plated them, and sent them out to a room full of guests. To his horror, chocolate from the undercooked center oozed from the cake all over the plate. As the story goes, he received a standing ovation from the guests for this brilliant, innovative, delicious new dessert. A creative legend was born.

The lesson here is that mistakes are essential to the creative process of life. Evolution and Darwinian fitness relies on genetic mistakes to express new forms that eventually propagate into the population to form the basis for new solutions of survival.

It takes a curious and mature mindset to discern which mistakes are worth exploring, and which need correction. How do you make that call?

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I can relate almost anything to a parable about food and working in the kitchen. The end result of this complex, dynamic, creative system is shared sensory experience. What could be more human that that?

Designing for emergence is a delicate art. Where to take control, and where to let chaos creep in? The takeaway is that through thoughtful and intentional constraints one can channel the creative process toward specific goals, while allowing for mistakes, messiness, and creative chaos to have their place in building the organization you desire.

Five links

• Aligning ‘Decentralized Autonomous Organization’ to Precedents in Cybernetics - a working paper by Michael Zargham and Kelsie Nabben examining DAOs as a cybernetic system a la Ross Ashby

• DAO Health Survey - an effort led by talentDAO and Ocean Protocol to release an open source tool and survey for evaluating the health of your decentralized community

• Tokenomics Basics - a Twitter thread by The DeFi Edge explaining the basic concepts of tokenomics in web3

• Diffusive Geometry - the art of Jiabao Li, who I saw present her immersive art in Santa Fe last month; in her words: “Vapor as a Tectonic Element to Sculpt Microclimates in Architectural Space”

• Our organizations were never disenchanted - speaking of organizational “lore” this paper examines how narratives about an organization emerge from within

  Thank you for reading Emergent Outcomes. Please help me build this community of systems thinkers, leaders, and technologists by sharing this post.

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The term animate has Latin roots meaning reason, mind, soul, spirit, and breath. In a sense, to be alive means to have energy, and essence. In these terms, to be animated is to be alive and death is the loss of spirit.

How peculiar of a phenomenon life seems to be. How does it come about? What is it that makes a thing alive? Examining the separation between life and death - and beyond - is a human trait that shaping our interactions, values, and choices as we navigate through our own finite time.

Discovering life

In the modern age artificial intelligence is applied to almost every aspect of our day. Smart algorithms use vast amounts of collected data to predict our choices about what to buy, listen to, and spend our money on. When IBM’s Deep Blue computer defeated chess master Gary Kasparov in a six-game tournament a new age of artificial intelligence arrived. Modeling the decision center of the human intelligence seemed in reach.

But what about the living essence? How to create artificial life?

The answer to this question continues to evolve with technology over time. In the 1700’s Jacques de Vaucanson created a mechanical duck that ate, drank, shimmied, and pooped. By the 1940’s, in the halls of Los Alamos National Labs, Jon von Neumann had moved beyond the mechanical idea of life and into computational approach with cellular automata.

Replicating the behaviors of a living organism is a quest of incarnation and human desire to dance with the divine.

But the essence of life is not only a mechanical rote routine. Pondering the qualities of life in order to recreate it artificially provides a way to question what is it that really makes something alive anyway. Simulation can emulate lifelike properties, but true life exists only within the vitality of an organism.

Living organizations

I believe there are three states of being: dead, alive, and inanimate. The scale of liveliness is a measure of how vast the possibilities of evolution and the complexity of information processing system made up of the individual nodes. The mammal species has a wide variety of evolutionary outcomes, and a brain is highly complex.

Scaling beyond the individual to consider groups, these properties of a living system should hold. How do we know whether an organization is dead or alive?

When people gather and work together to solve problems there is certainly a feeling of vitality when things are going well and demise when they are not. Demise gone too far, and the organization ceases to exist; people each move on to find life somewhere else. It’s simple to say that a company is alive when profits are growing, and dead when it stops making money.

However, a profit maximizing company is only one form of organization. Vitality goes much deeper than a balance sheet.

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Algorithms of community

In my work at Diamond DAO earlier this year we sought to understand community health across decentralized autonomous organizations in Web3 by building a dashboard to track health metrics over time. What we learned instead is that there is no singular definition of health to apply across different communities. Vitality is defined by the individuals participating in the community. Because of this we shifted our focus from a top down toolkit of community metrics to a bottom up, emergent mindset.

What gives essence to one organization is meaningless to another. Ways that different organizations might measure health, depending on their purpose and values:

• Adoption and adaptation of an open source protocol developed by the community

• Frequency of real-life events and number of people attending them

• Conversations and sharing of ideas within a social network

• Impact made toward a cause by community members

These examples are just a sample of ways that life of an organization can be measured. The more dynamic the community, the more likely it evolves, adapts, and creates new opportunities for members to join, contribute, and communicate their why.

The algorithm of life to create a dynamic organization capable of adaptation and perpetual evolution I break down into five core rules:

• Focus on the individuals and their interactions

• Provide a purpose for people to strive toward

• Choose intentional constraints and enforce them

• Be adaptable and flexible as the community grows

• Communicate clearly and provide efficient information systems

With these principles in mind I urge you to go forth and give life to the communities in which you participate. Proceed with curiosity, wonder, and appreciation for the sacred vitality that gives you life and the power to bring your own community alive.

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Five links

• Complexity Explorer - Courses offered by the Santa Fe Institute on all aspects of complexity science. Dive deeper into automata and genetic algorithms with Intro to Complexity

• Life Inspired - a blog about natural computing and simulated life by Dr. Luis Rocha, who I’m currently taking the course Evolutionary Systems and Biologically Inspired Computing from for my PhD coursework

• Artificial Life - a book by Chris Langton that inspired much of my thinking for this post

• The Adjacent Possible - a transcribed talk by Stuart Kauffman about the possibilities within the biosphere

• Dynamic Mathematics - very cool sketches of mathematical functions using p5.js

Footnotes

You see, my brother has been living in Kyiv for the past year with his Ukranian girlfriend as they built a life together. We keep in touch frequently, and each week he recounted the arts, culture, and vibrant young community they continued to discover in the capital city. She had just finished her Masters degree and was embarking on a new career with her valuable fluency in four languages.

They sensed tensions as Vladimir Putin made threats to Ukraine and ordered troops to surround the border. While conflict seems ongoing in the eastern region this seemed maybe different. Taking heed, they moved to the western city of Lviv in case tensions escalated. Still, they seemed dismissive that an all out war could emerge. They were not alone. Together they reassured friends and family that they had a pulse on the situation.

Tipping points

By February 23rd they decided to take action and flee if military activity happened outside of the Donbas region - eight hundred miles away and where skirmish is common. Easy to feel like that is a different world.

February 24th by noon they were on train to Poland seeking refuge from the largest European land war since WWII as bombs scattered the country.

I won’t claim to deeply understand the history, culture, and nuance that triggered this war. But I do know that at any scale humans are complex, unpredictable, and irrational. As a society the choices of individuals compound and move through a network of interacting fears, intent, and dreams.

This quality gives us character, creativity, and variety; it also makes us prone to tipping points, where in a moment life changes and a new normal is now. Sometimes that change is direct - like escalation of bombing between two countries - and sometimes the change is one of context - like life after a new world order.

Tit for tat

In 1980 Robert Axelrod, a professor of political science at the University of Michigan, held a tournament to test various strategies of prisoner’s dilemma using computer simulation.

In prisoner’s dilemma two agents can either choose to defect or cooperate with each other. There’s a payoff table that rewards mutual cooperation, but incentivizes individual betrayal.

In essence, a prisoner's dilemma is a situation of Moloch. Much of Axelrod’s research drew on situations of war and political strategy.

After many rounds of play the simplest strategy for mutual success emerged: Tit for tat.

• Start by cooperating

• Copy your opponents next move

Over the long run this simple strategy consistently resulted in the highest collective payoff.

Poetically, given the inspiration for this post, this strategy was submitted by Anatol Rappaport, a Russian mathematical psychologist born in what is today’s Kharkiv, Ukraine. He dedicated his life to the science of conflict and resolution.

Hidden complexity

Of course life is not so simple. The cost of failure can be mutually assured destruction and we don’t always have the luxury of testing our decisions in a lab. Imperfect choices and human interactions can change everything overnight. Life is real, consequences are meaningful, and the future is uncertain. All I can hope for is peace and resolve. God help us all.

“You’re captives of a civilizational system that more or less compels you to go on destroying the world in order to live.”
― Daniel Quinn, Ishmael: An Adventure of the Mind and Spirit

Five links

• Meditations on Moloch - An famous essay musing about collective action problems and their roots in society.

• Tipping Points - a Santa Fe Institute working paper by Scott E. Page and PJ Lamberson on the science of tipping points

• Madeleine Albright interview on Tim Ferriss podcast - Former US Ambassador to the UN and Secretary of State under Bill Clinton reflects on a life of international relations

• Letters from an American - newsletter by Heather Cox Richardson that is always insightful, relatable, and informative

• Regenerative Economics blog post by Curve Labs - proposes a new incentive system for the economy of natural resources

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