Many, maybe even most, of what we are like as human beings derives not from within ourselves but from our relationships—our bonds, our dealings, our links—with others of our kind.
Voyaging canoe seen near Buka Island by French explorers in the last decade of the 18th century. From J.J.H. de Labillardière's atlas published in 1817 and reproduced by courtesy of the Rare Book Room, Library, Field Museum of Natural History, Chicago.
EARLIER I SUGGESTED THAT network relationships have three general characteristics that can be labeled situational, circumstantial, and consequential. I also noted that along with the spatial dimensions of life conventionally called length, width, and depth, we need to add a fourth one called time—thanks to the insights of Albert Einstein and others. We now know that as we make our way from the cradle to grave, we do so not just across something called space. Not just through something called time. Instead, we are traveling within something cryptically labeled by physicists as space-time: the fusion of time with the usual three dimensions into a 4-dimensional space.
I have also proposed, however, that we must add yet another dimension to the menu. Although physicists and others may have their own ways of imagining what this 5th dimension is like, I propose that we should use it to capture the simple truth that a great many things in the universe exist not in some elemental form (such as copper, silver, and gold), but instead as compounds of differing elements linked, tied, or bonded in some way with one another. Classic examples would be sea salt (NaCl), water (H2O), quartz ( SiO2), and ethanol, also known as drinking alcohol (C2H5OH).¹
As physical entities, all of these everyday substances—even water—can be measured in terms of length, width, and depth. We can measure them, too, in terms of time, although often this 4th dimension of reality is ignored on the unspoken assumption that time is unlikely to have a decided effect, say, on a pound of salt, a bottle of spring water, a lump of rose quartz, or a fifth of the finest bourbon (although, come to think of it, time is often said to improve in wonderful ways the taste of both bourbon and a fine wine).
Now it would be unconventional, even odd, to describe any of these familiar molecular compounds using the language of network analysis. Even so, the character and material existence of each of these substances depends on the particulars of what and how its constituent elements are linked to one another.
Take water, for example, which is the most abundant substance on Earth. Water exists because it is possible for two atoms of hydrogen to be linked to one atom of oxygen by sharing electron pairs. At this basic material level of existence, the preferred way of describing the relationship between hydrogen and oxygen would be to say that water exists when two hydrogen atoms are covalently bonded to one atom of oxygen. Yet why not say instead that water exists as a material substance because two hydrogen atoms are related covalently to one atom of oxygen?
And if we did, what difference would it make?
Depending in part on how old and fat you are, each of us is somewhere between 50% to 65% water. Perhaps even more surprising, the human brain is around 75% water. Needless to say, facts and figures like these in no way mean that people also are bonded to one another covalently by shared electron pairs.
And yet however golden some of us may seem as personalities when compared with others of our kind, it should be apparent to anyone paying attention to what people are like that each of us is a combination, so to speak, of ingredients. Some of our traits may be more or less unique to each of us alone. Yet many, maybe even most, of what we are like as human beings derives not from within ourselves but from our relationships—our bonds, our dealings, our links—with others of our kind. Humans, too, are composites, blends, mixtures. And more than we might like to accept sometimes, we exist precisely because we are linked socially, emotionally, and materially with others. This is so even when we are still in the womb.
However, regardless how true this observation may be, such a statement is just a generalization.
Is there some way to pin down in real terms how much credit (or blame) for what people are like as individuals must be given to their links, bonds, and relationships with others?
Despite their scientific training and accomplishments, even geneticists, anthropologists, and other scientists today still seem willing to accept the old-fashioned folk belief that people belong to more or less separate and distinct collective “kinds” that can be labeled as “populations.” (These are also sometimes called groups, peoples, tribes, ethnicities, races, and the like.) But does it really make sense to say that human beings naturally come in separate kinds? What is a population? How can we tell whether we are “inside” a population or “outside” it? If they are as real as conventional wisdom assumes, how many of these kinds of things are there “out there” in the real world?
The late anthropologist Fredrik Barth (1969) once famously observed that people can be different from one other in their own ways without having to be cut off and isolated from other people. Contrary to conventional wisdom, in other words, contact and interdependence—i.e., social relationships—do not rule out what generally gets labeled as “human diversity.”
Instead of just accepting Barth’s claim, can we turn this observation into a testable hypothesis about human similarities and differences? Phrased more floridly, is each of us more like a nugget of gold or a drop of water when it comes to what we are like as a human being?
Out of all the places on earth where people live, it would not be a flamboyant exaggeration to say the New Guinea region in the southwestern Pacific contains more human diversity than anywhere else on earth. Take language, for example. It is far from easy to count the number of languages extant in this region of the world for two major reasons. First, the island of New Guinea itself (the 2nd largest island in the world) is cut in half by two different nation states. Indonesia rules the western half. The independent nation of Papua New Guinea governs not just its eastern side, but also a great many other smaller islands in the surrounding waters. Papua New Guinea is usually said to have something like 850 or so different languages. The estimated number spoken in the Indonesian provinces on the western side of the island is harder to piece together from available information, but a conservative number would be 250-300 languages. Whatever the actual count, add these various figures together and the amount of linguistic diversity is remarkable.
The second reason is worthy of mention, and that is all I will do here. One of the grand aphorisms in linguistics is this one: “a language is a dialect with an army and a navy.” This saying is often attributed to the Yiddish linguist Max Weinreich, although Weinreich himself never claimed the saying was his. He was evidently just the first person to publish it. In any case, an honest answer to the question “How many languages are spoken on New Guinea?” would have to be the compound question “Who is counting them, how are they being recognized as separate languages, and who cares?”
These are all good questions. Answering them first and foremost calls for having ways to measure relationships in this part of the world not just in terms of length, width, depth, or time, but also in how dependent or independent people happen to be in any given location is this vast and geographically complex part of the world (Figure 1). After all, language is a prime way by which people communicate with one another. (Although I must confess I have always been someone who talks to himself a lot.) It is not unreasonable, therefore, to suspect that focusing on language diversity may give us a good sense of how dependent or independent people in any given place in this region are likely to be.
Based on historical information, it can be said with confidence that people and communities in the southwest Pacific are definitely not isolated from one another despite the scale and geographic ruggedness of these islands. Although I suspect he would choose different words to use today, back in 1983 the human geographer Bryant Allen summarized the state of life in this part of the world succinctly this way (see Figure 2):
Before white contact Papua New Guinea communities were small and very localized. With few exceptions they could provide for themselves all their subsistence requirements from within their own territory. Many men travelled no further than a few kilometers from their birthplaces all their lives. Travel was discouraged by rugged terrain and hostile neighbors. Nevertheless, the country was criss-crossed with well established trade routes along which travelled food, animal products, bird plumes, dogs’ teeth, sea shells, fish, clay pots,
potassium and sodium salt, sago, stone axe blades, obsidian, wicker baskets, wooden dishes, string bags, paints, dyes and coloured earths, as well as art forms, dances, magic, sorcery and new ideas. (Allen 1983: 19)
Given this historical information, the hypothesis seems worth pursuing that human diversity—including but not limited to language diversity—in this region must somehow be a combination of traits, some local and others more broadly shared. But given the vast scale and geographic diversity of this part of the world, what is it that we should we try to measure first, and how should we try to do so?
Back in the early 1970s long before today’s personal computers, I wanted to confront this challenge in a way that was feasible given the state of technology then available. Specifically, I wanted to create geospatial maps or models that could be used to estimate what would be the most likely paths or routes for human relocation, interaction, trade, and the like among local communities on the premise that the most probable paths of interisland travel can be estimated given only two measurable variables: distance and land area.
Information can be found elsewhere on how I created what I called back then the “network graph” shown in Figure 3 superimposed over a conventional map of the Solomon Islands (Terrell 1977). The strategy used to create this simplification of the complex spatial geography of the Solomons archipelago was so elementary (Figure 4) that I was honestly worried that my research colleagues wouldn’t take the resulting mapping seriously. Hence I decided on a deliberately pretentious way to label it, one that goes fairly trippingly off the tongue. I dubbed this type of network graph a “proximal-point analysis.”
I am both delighted and amused to say that not only is this basic network mapping strategy still in use (with or without employing a pencil and a ruler), but it is widely known at least in archaeological circles in Europe as a “ppa analysis” (Amati et al. 2018).
Furthermore, I should note also that a less precise but similar network structure had been independently devised by the biogeographer P. J. M. Greenslade to predict the taxon cycle of faunal migrations through the Solomons archipelago (Greenslade 1968, 1969; Wilson 1961).
Human geographers have long been familiar with the effects of distance and population numbers (and thus area) on the probability of human movement, interaction, trade, and the like among people and the places where they live. Back in the 1970s when we were starting to develop network approaches to human diversity, some working then in the Pacific may have thought that what we were doing was simplistic, deterministic, and pointless. It was reported to me by a friend that one well-known archaeologist dismissed what we were trying to do by saying that all an archaeologist really needs is a few good sites to dig. We paid little attention to this sage advice.
Having determined what you want to do, however, can lead to a painful realization. There may be little on hand to do it on. The challenge we faced was frightening and also invigorating. Could we decide not only what we needed to know about, but also where to find it?
¹ Another classic example would be the eukaryotic cells.
Allen, Bryant (1983). Human geography of Papua New Guinea. Journal of Human Evolution 12: 3–23.
Amati, Viviana, Termeh Shafie, and Ulrik Brandes (2018). Reconstructing archaeological networks with structural holes. Journal of Archaeological Method and Theory 25: 226–253.
Barth, Fredrik (1969). Introduction. In Ethnic Groups and Boundaries: The Social Organization of Culture Difference, Fredrik Barth, ed., pp. 9–38. Boston, MA: Little, Brown and Company.
Greenslade, P. J. M. (1968). Island patterns in the Solomon Islands bird fauna. Evolution 99: 751–761.
Greenslade, P. J. M. (1969). Land fauna: Insect distribution patterns in the Solomon Islands. Phil. Trans. R. Soc. London B 255: 27 1-284.
Terrell, John (1976). Island biogeography and man in Melanesia. Archaeology and Physical Anthropology in Oceania 11: 1–17.
Terrell, John (1977). Human biogeography in the Solomon Islands. Fieldiana: Anthropology 68: 1–47.
Wilson, Edward O. (1961). The nature of the taxon cycle in the Melanesian ant fauna. American Naturalist 95: 169–193.
This is Part 10 in a series of posts on dynamic multidimensional network analysis. Next up: 11. Where?
© 2018 John Edward Terrell. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author(s) and source are credited. The statements and opinions expressed are those of the author(s) and do not constitute official statements or positions of the Editors and others associated with SCIENCE DIALOGUES.
“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.”— Robert MacArthur
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:
- PROBLEM: 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.