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How to Become a Cliodynamicist

Every once in a while I get an e-mail from students interested in a career in cliodynamics. What kind of courses does one need to take, and what is the possible career path that could lead to cliodynamics research?

Let’s start by acknowledging that there are no departments of cliodynamics – and it is quite likely that there never will be. In fact, I would prefer it to stay that way. Academic science is already fragmented along many artificial lines, and the last thing we need is yet another department of social science. The way of the future is doing interdisciplinary – indeed, transdisciplinary – research. We need to take steps to overcome the balkanization of science, not add to it.

Thus, my advice to a budding cliodynamicist is to first decide on a disciplinary home, and then build a career within it. Only after you have tenure, can you truly pursue cliodynamics. You have to think long term.


In principle, any social science department can serve as a decent disciplinary home (a bit later I discuss which ones I would choose if I were starting my scientific career). You can even be based in a natural science department. For example, my primary department is Ecology and Evolutionary Biology, and I have cliodynamic colleagues who were trained as physicists and engineers. Still, while my biological background was a great help in training me for my current research in social evolution and cliodynamics, if I were starting my career now, I’d go into one of the social science disciplines. The problem is that I have a large baggage of completely useless – for cliodynamics! – information (to give an example, I still remember a lot about how juvenile hormones regulate insect physiology – don’t ask).

So which discipline would I choose? My first choice is anthropology/archaeology (in fact, I am adjunct in my university’s anthropology department, as well as in mathematics). History is pretty much out, at least as it is practiced in the United States, where it is considered to be one of the humanities, rather than sciences. It may be a viable option outside of the US (in fact, I know of several highly promising young scientists who have, or are getting Ph.D.s in history outside the US). Archaeology is the closest thing to scientific history we currently have. Graduate students in archaeology can get rigorous training in substantive disciplines that require knowledge of natural sciences (geology, ecology, evolution, etc.). Statistical and mathematical skills are not frowned upon (towards the end I will discuss the mathematical curriculum that is useful for cliodynamics). Archaeologists deal with both general theories and the nitty-gritty of getting the data to test theories.



If I were an aspiring cliodynamicist, I would actually do a largely empirical Ph.D. – participate in the excavations, and do the laborious, often boring, and at times mind-numbing stuff that is necessary to force the uncooperative universe to yield ‘data’. I did this as a Ph.D. student and a post-doc in ecology, so I know intimately how data are obtained. All theoreticians should design their own field studies and do all the donkey work, at least once in their career, to understand where scientific ‘facts’ come from.

So archaeology/anthropology would be my first choice. Another good choice is sociology, because it is fairly strong on general theory. Problems with sociology, however, are two-fold. First, there is too much emphasis on reading and re-reading the great ‘dead white men’, Weber, Durkheim, Marx, etc. Most evolutionary biologists never read Darwin, even though they are Darwinians. When sociology becomes a ‘rapid discovery science’ (using the terminology of Randall Collins, although he is somewhat pessimistic on whether social sciences are capable of making this transition), sociologists similarly will be too busy churning out new results, to read and re-interpret Weber, Durkheim, and Marx.

The second problem with sociology is that only a relatively small proportion of sociologists address big questions in historical or macro sociology. Most of the rest do microscopic science of great value to specific societal problems, not doubt, but of little relevance to big questions.

Political science is also not a bad choice. It is very quantitative, with many practitioners being quite comfortable with statistical methods and analyzing large data sets. Its theory tends to lean towards what I would call ‘phenomenological models’, that is, a reliance on regression, rather than arguing from first principles (a mature science needs both). Also, political science tends to be, for obvious reasons, heavily politicized/ideologized, which is a drag.

Finally, there is economics. Paul Krugman, as a young man, read Isaac Azimov’s Foundation series, and wanted to become a psychohistorian. He went into economics because it was the closest thing, at the time, to psychohistory. Today, however, there are some disadvantages to choosing economics as your disciplinary home, if you want to do cliodynamics. The problem is that economics was the first social science to become mathematized, and it did so before the complexity science and nonlinear dynamics revolution of the 1970s–1980s. So traditional economic theory is heavily reliant on equilibrium methods. This is changing – many economists are embracing non-equilibrium approaches, and there is now a strong current of evolutionary thinking in economics (‘evonomics’). However, because of its initial success, economics seems to be somewhat isolated from other social sciences, which is a problem for transdisciplinary research. Still, most economic historians are housed within economics departments and, as I said, many economists are now pursuing explicitly historical questions, as well as evolutionary thinking and dynamical methods. So one of such nontraditional economics departments could also be a good place to get a Ph.D. which would lead, eventually, to cliodynamic research.

OK, so choosing a disciplinary home is the most important decision. Also, doing a Ph.D. project that is not entirely theoretical or, even better, mostly empirical or experimental, is what I would suggest. The goal is to become embedded in a supporting network of colleagues, both senior and peers. Getting a job in academia these days is not easy, and one must be very strategic about it.

But let’s not forget that the goal is not just to get a tenure-track job, but to do cliodynamics. This means that you need to acquire the necessary quantitative tools.


What are they? Here’s my list of suggestions. First, you need a solid grounding in mathematics and dynamical systems. This means taking the following courses:

Ordinary differential equations
Qualitative theory of differential equations (stability analyses)
Partial differential equations
Stochastic Processes

Next, you need several statistics courses:

Introduction to Statistics
Time-series Analysis
Bayesian Statistics
Numerical Methods

Finally, you need good programming skills, because most of the models that are realistic enough will have to be solved numerically, or simulated using the computer. My personal take is that you don’t need to take formal courses in programming; you can simply learn it by doing. But it wouldn’t hurt taking a general course in good programming practices.

So there you have it. You’ve got to become ‘numerate.’

It’s quite a lot, so if you are still in college, you should start taking math and statistics now. It will be much more difficult once you are in a graduate school and need to concentrate on your Ph.D. research. But although acquiring quantitative skills takes a lot of work (you will have to do innumerable homework, because that’s the only way to learn math), it will pay, and continue paying for years and decades. I am still living off the mathematical capital I acquired in the first 25 years of my life.


One of the earliest known examples of math homework

Also, while my strategy suggests doing a mainly empirical project for Ph.D., having theoretical and quantitative skills is a very important competitive advantage in the job market. Once you have established solid disciplinary credentials, theoretical skills indicate additional breadth that most departments will value (and want to get).

And there you have it. It’s a lot of work, but it will pay for itself in the long run. As the famous Russian general Alexander Suvorov liked to say, “Hard in training, easy in battle.”


Join the discussion


  1. T. greer says:

    I would not count history totally out. Quantitative history – particularly quantitative economic history – prepares you in many of the same ways, I think, and gives you the added benefit of being able to assess the validity of the numbers a cliodynamist might use.

    P.S. You might be interest in Adam Elkuk’s recent post where he explains why he switched from pursuing a PhD in International Relations to one in Computational Social Sciences. Touches on a few similar themes.

    • Peter Turchin says:

      True, but my reading of the field of quantitative economic history is that most of it takes place in economics departments, not in hisgtory. If you pursue a Ph.D. in history, I wouldn’t be surprised if your taking math courses would be frowned upon.

      Computational social science is a good route. I know people at GMU (Rob Axtell and Claudio Cioffi-Revilla) who could be great Ph.D. supervisors. This would be my second choice after archaeology/anthropology, if I were to embark on cliodynamics career today.

  2. I like this post a lot, but I think your math background is a bit too focused on dated tools. Although the differential equations approach has proven to be great for physics, I think it is less and less useful for studying evolution and complex systems. I don’t have personal experience with cliodynamics, but it seems that the theoretical methods are close to those of CAS and evolutionary game theory (that I have experience with).

    As such, I would encourage students to pursue computer science (not just programming) and discrete math courses. These skills will be essential if one ever wants to turn case-by-case models into a general theory. I would argue that this is what Turing would have wanted.

    • Peter Turchin says:

      I agree with much of what you say. However, learning continuum dynamical models (differential equations, both ordinary and partial) is very important as a complement to learning discrete math. It trains intuition and also provide a reality check. If your differential model and a model based on difference (discrete) equations yield different results, you’d better understand why.

      Learning evolutionary game theory is very useful – there I agree with you.

      As to computer science, I personally never took any classes in it and learned everything I know about programming simply by doing it. I don’t necessarily urge all to do the same, but it worked for me.

      The most important point is the value of learning different mathematical approaches. Training your math intuition. Becoming _numerate_.

  3. Dave Hochfelder says:

    As a professional historian, I find that most of my colleagues are math–phobic. Some do understand and use statistics, but very few have taken even a freshman calculus class. When we admit grad students, we don’t even look at the math GRE scores. I am unusual in that I started out as an engineer, so I took mathematical physics courses up to PDE’s, etc.

    That said, I think the real issue for historians is this question: what ought we be doing? Should we study the evolution of large-scale, long-lasting social structures (Annales School approach) or should we attempt to recapture the lived experiences of ordinary people (“new” social history)? Most of us would answer with the latter. The question matters for intellectual and even ethical reasons. Studying large social structures means treating history like a social science, with the goal of understanding social evolution, but leaves out real people. Recapturing lived experience means treating history like a branch of the humanities, and, I think, involves us in a sort of moral responsibility to our subjects, to treat them as active agents and shapers of history. But this approach can leave out much of the context in which people live their lives.

    I’m sympathetic to both approaches, but admit that getting to know the people I’m researching is very rewarding, intellectually and psychologically.

    • Peter Turchin says:

      There is no question in my mind: ‘we’ (meaning all of us collectively) should be doing both. Some of us should specialize on recapturing the lived experiences of individual people, others should study the dynamics of large human collectives. We need both approaches. So far, historians have done a great job with the first, not so great with the second, which is where cliodynamics comes in.

      I have said it in many other venues, and I repeat – historians are doing great job. I don’t want to ‘reform’ the historical profession. If it was up to me, I would create a directorate in the NSF to fund historical research, so that historians could do more of what they do.

      But we also need scientists who will advance and test general theories about how historical societies evolved. The Annales School was a great step forward, but they did not go far enough (and now they kind of petered out). We need to do more, but not at the expense of traditional historical research – rather, by complementing it.

      Incidentally, I also enjoy delving into the particular and, even, microscopic. I don’t publish papers on this, because my expertise lies in other dimensions. But I enjoy this aspect of history, and as I said elsewhere, there is no reason why the two approaches should be mutually exclusive:

      link to

  4. Your post reminded me of an excise I did a few years ago of trying to construct a sensible undergraduate curriculum in quantitative evolutionary social science from the courses then available at UC Davis.

    I just posted the course list:
    link to

  5. David Leigh says:

    Interesting article, and undoubtedly helpful. However, the mathematics suggestions do not look completely up to date.

    Category theory has been around since 1946 and has transforming, not only mathematics, but also computer science, physics and, increasingly, biology (Rosen) and neurology (Ehresmann & Vanbremeersch). It is essential for transdisciplinary work. Spivak has provided a good general introduction for scientists (link to

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