Caleb Chandler came to the Network Science Institute with a question that has driven him for years: how do human societies actually work? A dual-degree alumnus in Economics and Sociology from Utah State University, where he spent two years as an environmental policy researcher, Caleb found mainstream social science too fragmented to answer that question on its own terms. Now a first-year Master's student in Complex Network Analysis and member of NetSI's founding cohort, he is using network science and complexity theory to study something far more ambitious: the long-term structural evolution of complex human societies, and the forces that shape how they emerge, scale, and adapt over time.
Q1. Your academic and work experience covers a lot of ground through economics, sociology, environmental policy research. What was the moment, or the idea, that made you feel like all of those threads were actually pointing toward the same thing?
I think for me it was mostly a series of things all coming together under the complex systems/network science umbrella. For many years the primary object of my curiosity has been the phenomenon of human society and civilization. We spend our entire lives as constituents within the vastest, most transformative, and most complex living organism to ever emerge on this planet, and I think most of us take for granted just how incredible and awe-inspiring (and still mostly unexplained) that is.
Initially, my interest prompted me to study mainstream social science, but I found the experience underwhelming. The topics felt fragmented and loosely defined, and the explanations rarely connected to broader physical principles or the natural world — which was the kind of grounding I was looking for. My peers and professors seemed to approach the subject differently than I did, and I struggled to find the intellectual framework I was hoping for.
Everyone seemed to think of society as a purely engineered system; something we built intentionally, and subsequently are able to "own" or control. I found this uninspiring, and my interest began to wane. I first came across complex systems sometime during my senior year, and the second I did, I was hooked. At last, it felt like there was a language for talking about societies in the way I had always thought of them: not engineered systems, but complex, adaptive, emergent ones that exist in the same universe (with the same rules and laws) as everything else onEarth. My drive was reawakened, and I knew from that point on that I wanted to make a career out of applying complexity science to social evolution.
Q2. The MS in Complex Network Analysis is the first of its kind at NetSI and you're part of the founding cohort. What drew you specifically to this program?
I spent most of my time in undergrad preparing for a different career (government/policy), so I came out knowing that I had to do a Master's to build a competitive resume for PhD admissions. I knew I wanted to do a complex systems (or adjacent) program if at all possible, so it was a relatively short list to choose from. I decided on this program due to its small size, prominent faculty, excellent location, social science-specific concentration, and opportunity to be in the first cohort.
Q3. Your research interests span some genuinely big questions, like how societies emerge, scale, and adapt over time. Is there a specific course, project, or idea you've encountered so far in the Complex Social Systems concentration that has surprised you, challenged you, or just made you more excited about where this work could go?
It would probably have to be the whole subfield of dynamical process models on networks. To me, it seems like the purest application of network science, where you're directly observing how the topology of a network (aka the "rules of the game") manifests in the real world by constraining the way dynamics can play out. My semester project(and hopefully soon publication) actually uses one of these models—De Groot consensus—to observe the scaling efficiency of social coordination as a function of two orthogonal variables representing hierarchical structure and influence respectively, with the goal of testing long-held assertions that hierarchy was an unavoidable consequence of scaling pressures in early societies.
Q4. When you imagine yourself five or ten years from now, doing the work you're most passionate about, what does that look like? How do you see the tools and frameworks you're learning here building your future path?
I imagine it'd be somewhat similar to the work I'm doing now, only at a much higher level. The academic path is a long one, and I don't expect to have full freedom to research what I want for a long time. Nevertheless, my ultimate goal, whenever I get there, is to do social science in the way I think it should be done; specifically, by analyzing historical data and building computational/mathematical models of social systems and processes to evaluate against it. The tools I'm learning here are vital because they are exactly the ones I will use (and currently am using) to do exactly this.
