The Memoirs of Sherlock Holmes by Sir Arthur Conan Doyle,1894 [https://www.flickr.com/photos/britishlibrary/11305701564]
UNDER THE BANNER HEADLINE “The Biggest Questions in Science” the well-known writer Madhusree Mukerjee didn’t need to go far out on a limb on 9 May 2018 in Nature or later in Scientific American to say something wise although not earth-shattering: “In recent centuries we have learned so much—and such marvelous things—about the worlds around and within us that it may sometimes seem that no nook is left unexplored, no miracles left for us to unravel.” Not so, she ventured. “The truth is, though, that every new discovery leads us to ever deeper questions.”
Following Mukerjee’s lead, the editors at Nature and Scientific American then offered the world, or at any rate the readers of these two publications, a half-dozen lengthy essays by a like number of authors on what they evidently see as the six biggest questions in science today. Why just six? Who knows?
Furthermore, one could challenge the tacit editorial claim that this particular selection of essays is as good as they get. For instance, the opening sentence of the third essay titled “What is consciousness?” reads as follows: “Consciousness is everything you experience.” You don’t have to be a stick-in-the-mud Freudian to suspect this can’t possibly be true. Yet let’s not quibble and get distracted by such a questionable opening thought.
Instead I want to underscore that doing good science first and foremost calls for asking good questions.
Such an observation may come across to you as every bit as obvious as Mukerjee’s pronouncement that every new discovery leads us to ask ever deeper questions. Even so, I hope you will grant me that not all possible questions are equally worthy of careful scientific scrutiny.
For example, I don’t understanding why anyone would take the effort, time, and research funding to do a network analysis to show that being beautiful—whatever one takes beauty to be—can be personally advantageous (O’Connor and Gladstone 2018).
On the other hand, I think it is noteworthy that studying the evolution of life on earth from a relational network perspective can radically change how we envision The Tree of Life (Quammen 2018).
What’s a good question?
As parents often do, my mother had a number of handy sayings she would resort to when I was young and not always the perfect child. One I remember is a keeper: Just because you can doesn’t mean you should. Reading through the abstracts of research papers published in any scientific journal is likely to conjure up this thought more often than not. My own favored way of saying more or less the same thing when it comes to the questions scientists ask is the terse interogative So what?
When it comes to doing network analysis, a more constructive and positive query would be this one: If doing network analysis is the answer, what is the question?
Consider the consequences
As the old saying goes, there is no disputing taste. The big questions in science that rock your boat may not be the questions I’d ask. And vice versa. There is also no gold standard for deciding what makes a question good, bad, or indifferent.
However, it seems reasonable to say that if questions can have answers, then answers may have consequences. Hence one way to judge the quality of a research question is to ask whether those asking it have answers for the So what? question that do more than just hint at what may be the consequences of answering the question they are asking.
After all, asking questions about questions is not only possible, but is at least sometimes wise.
What’s the problem?
Another of the essays in Mukerjee’s half-dozen in Nature and Scientific American in 2018 is one titled “How Much Can We Know?” by Marcelo Gleiser. Dr. Gleiser is a professor of philosophy, physics, and astronomy at Dartmouth College. In suitable homage to Heisenberg and his uncertainty principle, he starts off his essay this way:
“What we observe is not nature in itself but nature exposed to our method of questioning,” wrote German physicist Werner Heisenberg, who was the first to fathom the uncertainty inherent in quantum physics. To those who think of science as a direct path to the truth about the world, this quote must be surprising, perhaps even upsetting. Is Heisenberg saying that our scientific theories are contingent on us as observers? If he is, and we take him seriously, does this mean that what we call scientific truth is nothing but a big illusion?
It hardly needs saying he comes around to the less jaundiced view that what we see in the world around us is not nature itself but nature as discerned through data we collect from machines. “In consequence, the scientific worldview depends on the information we can acquire through our instruments.” I suppose if you are an astronomer, this claim rings true, but of course this sentiment is not universally so. I’d even be willing to say such a statement has a pontifical silliness about it.
Nonetheless, who can disagree with his additional claim that science is “our best methodology to build consensus about the workings of nature”? Yet here, too, caution is required. We need to add to this conventional observation the added reflection that it is easy for us humans to fool ourselves into thinking that consensus is the same as truth.
Therefore, to respond to the question raised earlier—If doing network analysis is the answer, what is the question?—I suggest first that even if good research questions in science might be a dime-a-dozen depending, say, on how generous we are in entertaining unconventional ideas, the ecologist Robert MacArthur back in the early 1970s was right: “The only rules of scientific method are honest observations and accurate logic. To be great it must also be guided by a judgment, almost an instinct, for what is worth studying” (MacArthur 1972: 1).
Perhaps your take on MacArthur’s words may be radically different from mine, but for me what he is saying is that having a keen sense of problem paves the way for asking good, maybe even great, research questions about the world and how it works.
What are good plausible answers?
Those who knew him say Robert MacArthur was more than just a fine field ecologist. He was also a pioneer in practicing the art of mathematical modeling in community ecology, island biogeography, and population biology. As he observed with his colleague E. O. Wilson in 1967:
A theory attempts to identify the factors that determine a class of phenomena and to state the permissible relationships among the factors as a set of verifiable propositions. A purpose is to simplify our education by substituting one theory for many facts. A good theory points to possible factors and relationships in the real world that would otherwise remain hidden and thus stimulates new forms of empirical research. Even a first, crude theory can have these virtues. (MacArthur and Wilson 1967: 5)
If this were a cookbook, the recipe for dynamic network analysis we will be following in the rest of the posts in this series starts as follows:
- PURPOSE: begin by stating a well-defined research purpose.
- HYPOTHESIS: given what is known or may be plausibly assumed, map out what could be “the permissible relationships among the factors as a set of verifiable propositions.”
- MODEL-BUILDING: construct manageable models “with the overlapping but not identical goals of understanding, predicting, and modifying nature” (Levins 1966: 422).
With regard to the last item in this opening list, as the late Richard Levins famously wrote a half century ago: “even the most flexible models have artificial assumptions. There is always room for doubt as to whether a result depends on the essentials of a model or on the details of the simplifying assumptions” (1966: 423). And as he went on to say (specifically about biological models, but the lesson still rings true):
Therefore, we attempt to treat the same problem with several alternative models each with different simplifications but with a common biological assumption. Then, if these models, despite their different assumptions, lead to similar results we have what we can call a robust theorem which is relatively free of the details of the model. Hence our truth is the intersection of independent lies.
The next post in this series introduces a simple network modeling strategy devised nearly half a century ago that only requires three tools to implement: a pencil, a ruler, and map of whatever part of the world you want to simplify in a way that preserves the essential features of the problem you are trying to grapple with.
Levins, Richard (1966). The strategy of model building in population biology. American Scientist 54: 421–431.
MacArthur, Robert H. (1972). Geographical Ecology: Patterns in the Distribution of Species. Princeton University Press.
MacArthur, Robert H. and Edward O. Wilson (1967). The Theory of Island Biogeography. Princeton University Press.
O’Connor, Kathleen M. and Eric Gladstone (2018). Beauty and social capital: Being attractive shapes social networks. Social Networks 52: 42–47.
Quammen, David (2018). The Tangled Tree: A Radical New History of Life. Simon & Schuster.