Welcome to The Flux, where we hear stories from people who have asked what if questions to better understand the world and talk about how data can help tell stories that impact decisions and create an intentional impact on the future. This is your host, John Cordier, CEO at Epistemix. In a world where the flux capacitor from back to the future does not yet exist, people have to make difficult decisions without always knowing how the future will play out.

Our guests are people who've taken risks, made decisions when uncertainty was high, and who have assisted decision makers by using data and models. We hope you can turn lessons from our podcast into foresight, so you or your organization can make better decisions and create an intentional impact for others.

Hey there, Welcome to the Flux where we hear stories from people who've asked what if questions to better understand the world and learn how data can help tell stories that impact decisions to create an intentional impact on the future. This is your host, John Cordier. Today we're on with Dan Eichelsdoerfer. Dan has a PhD, and after a while, he ended up working in the energy sector. He later moved into consulting, did some oil and gas consulting, and is now the CEO of Macrocosm, one of my favorite companies. They have an excellent tagline: "Better economics for a better world."

A lot of their work builds off of the work of Doyne Farmer, particularly his work in complexity economics and agent-based modeling. What they're doing is really important, not just for the field of agent-based modeling, but for their broader goal: better understanding the world to make a difference. If the economy works better, it's better for more people. I'm really glad to have Dan on the podcast today. Dan, why don't you give our listeners a bit about yourself and your current role?

Dan: Yeah, thanks John. I'm happy to be here. As you mentioned in your intro, I came to Macrocosm through a bit of an unusual path. I started with an undergrad in physics and earned a PhD in chemistry. While I was in graduate school, studying nanolithography, I became really concerned about climate change and wanted to make a difference. After my PhD, I went into the oil and gas sector with the idea that I could help drive change from within some of the largest energy firms in the world.

I worked at ExxonMobil for several years. After a while, I realized I wanted more exposure to both the business and operations sides of things, and to better understand the electricity sector. So I went into management consulting and spent about six and a half, almost seven years at BCG.

While I was there, I started collaborating with Macrocosm, had some great conversations with Doyne, and became really convinced of his vision and the potential impact. That led me to join Macrocosm as CEO to help drive the change we're envisioning.

John: Absolutely. Let’s talk a bit about the team at Macrocosm. There’s Doyne he’s been doing complexity economics for a long time and probably coined the term himself, right?

Dan: Yeah.

John: So who else is involved in Macrocosm? What are some of the early use cases you’re excited about?

Dan: I’m really excited about the team, both intellectually and interpersonally. We’re working with several of Doyne’s former graduate students and postdocs, who come from backgrounds in physics, economics, and mathematics. One of them has a strong background in machine learning, so we’ve got a great mix of skills modeling in physics and economics, plus ways to bring machine learning into that work.

In addition to our full-time researchers, we’re also working with three professors who advise us part-time. They’ve all worked with Doyne before, either as students or postdocs, and they’re helping us push the frontier of complexity economics and strengthen our models.

John: I remember a similar stage at Epistemix when our team was very research-heavy and academic kind of the glory days, with so many ideas flying around. What’s it like for you, leading a team where people approach modeling from their own domains? Is it fun? Are there lively debates? When you’re modeling something as big as the energy system from the ground up, those conversations must get intense.

Dan: I love it. "Glory days" is a great way to describe it. I’ve even joked with my partner that it feels like being back in graduate school just surrounded by smart people with great ideas, debating them in a really rigorous way.

It’s a challenge, but a fun one. I truly believe the best science and the best products come from interdisciplinary collaborations. The boundaries between fields force you to explain your thinking more clearly, especially to people who don’t share your jargon. That’s a powerful clarifying force.

The debates are healthy. People argue over the best way to model something, but the team is good at stepping back and asking, “What’s pragmatic?” We don’t need to model something down to the fifth decimal place if we can capture the essential behavior. We’ll try X and Y, see if it works, and refine later if needed. So far, it’s been very collaborative and productive.

John: One of my favorite authors, Steven Johnson, calls that the “adjacent possible” being right on the edge, applying your insights to something new and potentially groundbreaking.

Before we dive deeper into Macrocosm, let’s talk about you. What got you into complex systems and modeling? Was it before your PhD, or something that emerged later?

Dan: It’s been a few chapters. The first was in undergrad, during my sophomore year. I had a physics professor whose job was to teach us mathematical methods. He had this phrase: “I’m here to teach your wrists to do physics.” It was computational math for quantum mechanics and electrodynamics.

This professor had a bit of an obsession with chaos and complexity, so we took a two- or three-week detour into Lorenz weather modeling, strange attractors, bifurcation plots all that. I was fascinated, but at the time, I also thought, “This is cool, but what’s it useful for?”

Then I went on to study chemistry and moved away from that space. Fast forward seven or eight years into my career at BCG, and I started thinking more about systems-level views of the energy transition. I felt like everyone was working in silos, but we were missing a holistic lens. That started turning some wheels.

At BCG, I joined an internal think tank working on these questions. I came across Doyne’s work. A partner who knew him through the Santa Fe Institute introduced me. It reignited that dormant spark. I could finally see the practical applications of complexity, chaos, nonlinear dynamics. And by then, a few things had changed computing power had improved, and the field had made progress embedding behavioral and psychological insights into models.

As I spoke with Doyne, I kept checking: Am I seeing this clearly? Is it really ready for prime time? He confirmed it, and that’s what led to our collaboration and eventually Macrocosm.

John: That’s awesome. We’ll definitely come back to aha moments later. But first, I want to ask about innovation environments. You’ve done modeling at Exxon, at BCG, and now in your own startup. What are the pros and cons of innovating in those three types of organizations?

Dan: I've thought about this a lot since joining Macrocosm, and surprisingly, there are fewer differences than I expected. In any innovation setting, you’re basically trying to get enough funding to keep doing the research. The question is just who you're convincing to get that money.

At Exxon, which has a robust R&D function, the challenge was that it takes a long time to unlock funding. There's internal risk aversion, especially around anything outside the core business. Everyone knows it’s important for the company’s long-term health, but the day-to-day pressures make it hard to prioritize.

At BCG, the pace was faster, which helps you make quick progress but can also leave good ideas on the table. There’s more focus on MVPs, quick feedback, lean methods that’s something consulting firms are really good at.

At Macrocosm, the autonomy is much higher, of course, being a small team. But you still face similar survival challenges: finding funding, meeting client needs, and balancing intellectually interesting problems with practical ones that cut across markets. The specifics are different, but that fundamental tension is the same.

John: That makes a ton of sense, and I think a lot of our listeners can relate. Now I want to shift to agent-based modeling. How do you deal with legacy modeling beliefs, especially in energy? What’s the status quo, and how big of a shift is what Macrocosm is doing?

Dan: Great question. In the energy sector electricity, oil, and gas there are lots of long-established modeling approaches. Power flow modeling, grid optimization, those are well understood. But they often lack that integrated, system-level economic perspective.

Companies tend to be asset-focused how to operate their plants, wires, fields. There's less attention to external economic signals, which often affect profitability. Asking “what should we model?” enough times leads you to modeling your company, then your country, then the world.

A great example is the 2022 Russian invasion of Ukraine. Most could have predicted a gas price spike in Europe. But Australia also saw gas and electricity price spikes because of global LNG export dynamics. That kind of second- and third-order effect is really hard to capture without modeling the entire interconnected energy system. That’s where Macrocosm’s agent-based models shine.

John: So for those familiar with agent-based modeling, do you mix in other methods, or is it purely ABM?

Dan: We start purely from an agent-based modeling foundation. That said, we sometimes use statistical models or econometrics as sanity checks or to build simple v0 prototypes. Agent-based models can be tricky you want to make sure you're not just seeing what you expect to see.

We also envision using techniques from deep learning and reinforcement learning to make our agents more realistic. For example, we might train agents on a company’s historical financials and behaviors so they act like that company in the model. That’s still a bit down the road, but the potential is there.

John: That’s really cool. So what’s held ABM back over time compared to other techniques? You mentioned computing power and data. What else?

Dan: One big one is communication. A lot of explanations of ABM and complexity economics are hard to digest, even for someone with my background. I think there's a communication barrier to be overcome.

We also haven’t had that “AlexNet moment” like deep learning had in 2012 something that proves the value so clearly the world takes notice. And there’s still a gap in systems thinking. It’s hard to broaden your lens and really understand interconnections. That kind of thinking needs better pedagogy and more institutional support.

John: Definitely agree. Outside of the Santa Fe Institute, what other groups are doing great work in this space?

Dan: Oxford’s Institute for New Economic Thinking is one, where Doyne and others are leading the charge. The Complexity Science Hub in Vienna is another. There may be more, but those are the two that come to mind.

John: Awesome. One or two more questions. One of my favorites: what’s a “What the Flux” moment a time in your life or in history that you’d want to rewind, simulate, and explore alternative outcomes?

Dan: I’d go back to the 1970s and 80s, after the Arab oil embargo. That was a moment when the US and Europe were reevaluating their energy strategies. Solar, wind, batteries, and nuclear were all known technologies, even if expensive. If we’d had the modeling tools to show how deploying those technologies at scale could drive down costs and improve sustainability, I think we could be living in a very different world. We might not even be worried about climate change the way we are today.

John: Is that still true today? Does modeling energy systems still hold that kind of transformative potential?

Dan: Absolutely. That’s exactly why we started Macrocosm.

John: I want to tie this back to Doyne’s emphasis on sustainability. Where do you see modeling’s value proposition when it comes to sustainability over the next decade?

Dan: I think we’re at a critical point. There’s a lot of capital ready to be invested in sustainable solutions, but a lot of uncertainty about where to put it. If agent-based models can reduce that uncertainty by even 10%, it could unlock significant investment and accelerate the energy transition.

It goes beyond energy. Long term, these tools can help with emissions from industry, agriculture, even inequality. It’s about building a better understanding of complex systems so we can make smarter, more sustainable decisions.

John: Dan, I’m glad we’re on this journey together. If people want to get in touch with you or learn more about Macrocosm, where should they go?

Dan: You can email us at info@macrocosm.group or check out our LinkedIn page. We monitor both regularly.

John: Awesome. We’ll include that for our listeners. Dan Eichelsdoerfer, CEO of Macrocosm thank you for being on The Flux.

Dan: Thanks John, appreciate it.