It is often said (including by me) that complex systems is inherently inter-disciplinary. It wasn't until recently that I realized people mean different things by that.; or, more accurately, that people typically don't mean anything specific by that claim. It's just another slogan of complexity. So, in preparation for a presentation on the conceptual foundations of complexity science I drew out these distinctions as an example of what kinds of results conceptual analysis can produce. Thinking more deeply about these distinct practices will likely clarify each of our role in the scientific enterprise.

Keep in mind that I am not trying to correct the way people talk about their research, or impose my own nomenclature on others. I don't care which words people use for these categories. The point is that these are different kinds of projects, and so they require different skills and involve different approaches. I chose different words that I think fit for ease of reference only. One purpose to this disambiguation is to determine which of these approaches, if any, constitutes a core element of complexity science. Another (related) purpose is to defuse overblown and unsupportable claims about particular research projects and their relation to complexity (see Studying Complexity vs Studying Complex Systems coming up soon).

First, research that isn't inter-disciplinary is:

  • Intra-disciplinary: research addressing questions/problems within one established discipline, and using techniques intended for that discipline.

This is not to claim that the topics/questions/problems within the disciplines do not change over time, or that the methodologies do not progress. Of course they do. However, an economist using game theory to study banks or a physicist using simulations to study condensed matter are clearly within this category.

I have identified the following four distinguishable approaches to inter-disciplinary research (there are probably more, but this is a good start). Also note that a single research project could be multiple of these types.

  • Cross-disciplinary: using a technique or insights from one established discipline to address questions/problems within another distinct established discipline.

  • Multi-disciplinary: research requiring insights from multiple disciplines to address problems/questions shared by those disciplines.

  • Trans-disciplinary: a problem/question or insight that has useful applications in more than one established discipline.

  • Extra-disciplinary: research that does not fall within any established discipline or combination of established disciplines.

Applying these categories to research is clearer for some than others. For example, using game theory to study animal behavior or airplane wing design was once clearly cross-disciplinary, but after it has been done and published it seems that game theory became a technique within those fields. Using ideas from population ecology to understand inter-bank loan networks seems to count; as does using fluid dynamics models to understand traffic flow.

Multi-disciplinary research is easier to spot and recently quite common. Combining gene expression with cognitive/behavioral studies is a clear example. Physical models of protein folding is another. Developing self organizing materials also falls into this category, and could also be cross disciplinary insofar as they are sometimes bio-inspired (e.g. artificial gecko-like adhesive materials). Political economy and political philosophy seem to fit here as well. Both fields contribute to and benefit from the research. Neuro-psychology fits this too, but in a different way: both fields benefit, but the benefits are distinct rather than shared.

Trans-disciplinary research can occur any time a basic insight has implications for other fields at a higher level of organization. If we take genomics as a basic insight, it has implications for microbiology, physiology, population ecology, social evolution, etc. But it can also be across domains at the same level; e.g., learning an important feature of human decision bias (a psychological insight) can affect research in economics, political science, marketing, ethics, etc. In each of those cases the flow of insight is one-directional.

Extra-disciplinary research seems to be quite rare, and this should be expected. Once some novel research thrust is initiated, it tends to attract similarly interested scientists, and quickly blossoms into another field or subfield. The nascent field of information science is a clear example, and the study of artificial intelligence was formed like this not too long ago. So this category includes research studying new phenomena that are not already covered by another domain (in the way that nanotechnology is covered by physics, chemistry, and engineering). Complexity, if there is such a distinct and independent phenomena to study, would now be in this category. Many people study particular complex systems with the belief that there is a shared phenomena, but few study the complexity of those systems directly nor attempt to make connections across disciplines.

Methodology work is also often extra-disciplinary, although it's clear that there should be an exception for methodological fields like computer science and statistics, and for particular methods like simulation, network theory, and equation models. These are, of course, often both extra-disciplinary and trans-disciplinary according to the above distinctions, but just because a person includes (say) a network model in their research shouldn't qualify it as inter-disciplinary. It must be the case that the network model is somehow used to port results from the home domain to other domains. An example of this porting is comparing power-law relationships across words in books, book sales, city sizes, star sizes, etc. Work that actually makes use of these connections (rather than just stating them) would count as trans-disciplinary on this account. Developing new measures for hypergraphs does not fall within any established discipline, and would benefit many of them. But network theory does have its own journals and conferences, so it is becoming its own methodological field in the way that statistics is. Some people study statistics itself, and others use statistics to study specific systems within established domains.

Okay, I hope that is all clear, and now back to the original question: in which sense or senses is complexity science "inherently inter-disciplinary"? The short answer is that studying complexity, and the results from that research, would fill all of those categories. Even though complexity has its own journals and conferences, it is not "owned" by any existing field, thus it is extra-disciplinary. If one gained insight about a fundamental feature ordering the behavior of complex systems, then it would have implications across many disciplines all the fields with systems of that type and would therefore be trans-disciplinary. Because complexity is a general principle of ordered system behavior, the problems/questions of complexity are shared across many fields different fields for different problems and therefore qualifies as multi-disciplinary. And, the discovery of such a general organizing principle would allow insights from one field to apply other fields, so it is also cross-disciplinary (or maybe it just facilitates cross-disciplinary research the way methodology does).

However, much of the research done under the name of complexity is intra-disciplinary rather than inter-disciplinary. Take as a common example a physicist studying social networks: the research crosses no discipline boundaries. There are no insights from physics being applied, just a method that some physicists are proficient in being applied within a social science discipline. If you disagree, then consider whether a sociologist using social networks is in any way doing physics. Thinking about city growth using analogies from biology seems to count as inter-disciplinary, as does finding commonalities in flocking birds, stock market trades, and political opinions. But flocking birds, schools of fish, and crowds of humans are all in the same field: animal social behavior. This is not to downplay that research in any way. And I do believe that studying particular complex systems may occasionally lead to insights regarding complexity itself. But on deeper analysis, studying any particular complex systems, even one outside the discipline in which a person was originally trained, will typically fall under in the category of intra-disciplinary. So complex systems research is not inherently inter-disciplinary, although it sometimes just depends on what one studies and how.