Category Archives: Social Network Analysis

AAA Recap, 2013

I guess it’s that time of the year. You know, when I recap, in my bittersweet way, the annual meeting of the American Anthropological Association? I am an anthropologist, yes, but I am deeply torn in my feelings for my discipline, my department, and my flagship (?) professional organization. The question mark arises because I am also a physical anthropologist and a demographer, so an argument can be made that my flagship professional organization is actually AAPA or PAA, but there is something about the unmarked category that is AAA. It’s supposed to represent anthropologists, broadly construed. I honestly don’t think that it does a very good job at this, but the reasons behind that are complex and I’ve only allocated myself a bit of time to blog since I’m desperately trying to catch up from all the travel I’ve done recently.

The meeting this year was in Chicago, which is a pretty amazing town. I stayed in the the Blackstone Renaissance Hotel, which was recently renovated in a lovely Art Deco theme. We did Chicago stuff. Tube steaks were eaten, the quantity of cheese that can be crammed into a deep-dish pizza was marveled at, beer was drunk.

AAA is a pretty bizarre scene. For starters, it’s at the weirdest time. It seems like the peculiar timing of AAA during November must be disruptive for just about every academic anthropology department, particularly because it is nearly a week-long endeavor. It seems that the life in an American university carries on just fine without the anthropologists around for a week in the middle of the Fall term, thank you very much. A couple innovations this year struck me as particularly incongruous, given the content of much current scholarship in anthropology. First, anyone who registered for the meeting as a non-member was given a yellow badge holder to mark them as outsiders. This seemed a bit gratuitous. I’m not sure what’s gained from such marking — they already pay a substantially higher rate for the privilege of attending, do they also need to be shamed for their lack of faith? Second, in the hall outside the main bunch of conference rooms, there was a television that played a loop of anthropologists talking about how important anthropology is. This struck me as unnecessarily propagandistic and it’s not at all clear to me who the target audience for this performance was. Presumably, those of us who were there already think that anthropology is a worthwhile endeavor. Seems to me that it’s the rest of the world we need to convince. Once again, there appears to be almost nothing considered newsworthy to emerge from this meeting of 6,000+ scholars with the exception of a paper on the similarities in street-scanning behaviors by police and fashion scouts.

Another strange feature of AAAs is that computers, cables, remotes, laser-pointers, etc. were not provided in the conference rooms but needed to be provided by the session chairs. This is the first time I’ve experienced this in years at a major conference and it definitely slowed us down quite a bit at the start of our session. I’m not sure what was going on with that. Maybe the budget to pay for AV services was already spent on the fancy video production that reminded us how important we all are?

This year, I organized and chaired a session, which was sponsored by EAS, on social network analysis in evolutionary anthropology. Unfortunately for the EAS party-goers from the previous night, the session ran at 08:00 on Saturday morning. Despite this challenge, the room was packed and the audience generally seemed into it. We had great talks by Stanford’s own Elly Power and Ashley Hazel. Elly talked about her amazing dissertation research on using social capital to understand costly displays of religious devotion in southern India. Ashley talked about her dissertation work in the School of Natural Resources and the Environment on mobility and the changing landscape of STI risk in Kaokoland, northern Namibia. David Nolin, one of our discipline’s most talented young methodologists, presented a very clever test of generalized reciprocity using dichotomous exchange data from his work in Lamalera in Indonesia. Ben Hannowell, yet another talented methodologist to come out of the WSU/UW IGERT program, discussed his collaborative work with Zack Almquist on inferring dominance structure from tournament graphs. The always marvelous Rebecca Sear talked about her recent synthetic work on the effects of kin on fertility (kinship, of course, is the classic application of networks in anthropology since genealogies are just special cases of graphs). John Ziker presented a network-based approach to understanding food sharing and reciprocity from his terrific ethnographic work in Siberia. I closed out the talks with my own combination history of anthropological (and ethological) contributions to social network analysis and pep talk to encourage anthropologists to be confident about their methods and have the courage to innovate new ones the way people like John Barnes or Clyde Mitchell or Elizabeth Bott or Kim Romney or Russ Bernard did!

After schmoozing for a bit post-session, I headed over to the Saturday EAS session on methodological advances in experimental games. While I didn’t see all the talks, the ones I saw were pretty cool. In general, I have mixed feelings about experimental economic games. There are lots of results and some fairly convincing stories to go along with some of the results. However, absent of context, I really wonder what they are measuring and, if they are indeed measuring something, whether it is actually interesting. This session made some real progress in dealing with this question and I think it really highlighted the comparative advantage of anthropologists in the multi-disciplinary landscape of twenty-first century behavioral science. While economists such as Loewenstein (1999) might lament the fact that there is no way to play context-less games and that this jeopardizes the validity and generality of such experimental games, anthropologists are experts in thinking specifically about context and its effect on behavior. Furthermore, anthropologists are still the go-to researchers for providing contextual diversity. In this session, we heard about experimental games played in Bolivia, Siberia, Fiji, and on the streets of Las Vegas. One talk in this session that particularly impressed me was given by Drew Gerkey, who is currently a post-doc at SESYNC in Annapolis, Maryland (and soon to be an assistant professor at Oregon State University — Go Beavs!). I was at SESYNC earlier in the week and got a chance to talk pretty extensively with him about this work. Drew makes the point that seems obvious now that I’ve heard (a sign of an important idea) that, in the evolution of cooperation literature, the counterfactual scenario to cooperation is frequently untenable. One does not simply go it alone when one is a hunter/fisher in Siberia. Drew also designed a number of very clever experimental games that fit the types of social dilemmas faced by his Siberian interlocutors. Very nice work indeed.

In addition to the sessions I attended, it was nice to see and chat with various smart, fun people I know who sometimes find their way to AAAs. I missed my partner in crime from last year’s AAA, Charles Roseman, who left the day I arrived, probably too bloated from the binge on Chicago’s amazing food he no doubt shared with Fernando Armstron-Fumero to be of much use to anyone. However, I got to see Siobhan Mattison, Brooke Scelza, Brian Wood, Rick Bribiescas, Mary Shenk, Aaron Blackwell, Pete Kirby and, briefly, Shauna Burnsilver and Dan Hruschka. Despite my general misgivings about the conference, it is nice to have an excuse to see so many cool people in one place at one time.

Why the Prediction Market Failed to Predict the Supreme Court

There is a very interesting piece in the New York Times today by David Leonhardt on the apparent backlash against prediction markets such as Intrade and Betfair. In principle, these markets make predictions by aggregating the disparate information of many independent bettors who offer prices for a particular outcome. Prediction markets have enjoyed a fair amount of success in recent elections. The University of Iowa has even set up an influenza prediction market.  But prediction markets are hardly perfect and have had some pretty big recent failures. It turns out that Intrade failed in a pretty spectacular manner to predict the outcome of the recent Supreme Court ruling about the constitutionality of the Affordable Care Act. Leonhardt suggests that some of the failures of online prediction markets is attributable to relatively small number of people who actually trade on the market:

But the crowd was not everywhere wise. For one thing, many of the betting pools on Intrade and Betfair attract relatively few traders, in part because using them legally is cumbersome. (No, I do not know from experience.) The thinness of these markets can cause them to adjust too slowly to new information.

This may have been an issue with the ACA decision but the primary problem with the incorrect prediction is that the crowd doesn’t actually know much about the workings of the very closed social network that is the United States Supreme Court. Writes Leonhardt:

And there is this: If the circle of people who possess information is small enough — as with the selection of a vice president or pope or, arguably, a decision by the Supreme Court — the crowds may not have much wisdom to impart. ‘There is a class of markets that I think are basically pointless,’ says Justin Wolfers, an economist whose research on prediction markets, much of it with Eric Zitzewitz of Dartmouth, has made him mostly a fan of them. ‘There is no widely available public information.’

This point gets at a larger critique of market-based solutions to problems suggested by my Stanford colleague Mark Granovetter over 25 years ago (Granovetter 1985). This is the problem of embeddedness. The idea of embeddedness was anticipated by the work of substantivist economist Karl Polanyi, but Granovetter really laid out the details. Granovetter writes (1985: 487): “A fruitful analysis of human action requires us to avoid the atomization implicit in the theoretical extremes of under- and oversocialized conceptions [of human action]. Actors do not behave or decide as atoms outside a social context, nor do they adhere slavishly to a script written for them by the particular intersection of social categories that they happen to occupy. Their attempts at purposive action are instead embedded in concrete, ongoing systems of social relations.” Atomization is independent bettors making decisions about the price they are willing to pay for a certain outcome.

The argument for embeddedness emerges in Granovetter’s paper from the problem of trust in markets. Where does trust come from in competitive markets? The fundamental problem here regards the micro-foudnations of markets where “the alleged discipline of competitive markets cannot be called on to mitigate deceit, so the classical problem of how it can be that daily economic life is not riddled with mistrust and malfeasance has resurfaced.” (p. 488). The obvious solution to this is that actors choose to deal with alters whom they trust and that the most effect way to develop trust is to have prior dealings with an alter.

Granovetter’s embeddedness theory is a modest one. He notes that, unlike the alternative models, his “makes no sweeping (and thus unlikely) predictions of universal order or disorder but rather assumes that the details of social structure will determine which is found.” (p. 493)

These ideas about the careful analysis of social structure and networks of interlocking relationships are fundamental for understanding when the crowd will be wise and when it will not. They are also essential for developing effective development interventions and, for that matter, making markets work for the public good in general.  The theory of embeddedness allows for the possibility that markets can work but if we are to understand when they work and when they don’t, we need to think about social structure as more than just a bit of friction in an ideal market and take its measurement more seriously. People are not ideal gases. (Dirty little secret: most gases are not ideal gases). This gets at some problems that I have been thinking about a lot recently relating to the implications of additive, observational noise vs. process noise and its implications for prediction of multi-species epidemics, but that must wait for another post…



Wealth and Cheating

I recently read a story in the Los Angeles Times about a team of psychologists at UC Berkeley who showed, in a series of experimental and naturalistic studies, that wealthy individuals are more likely to cheat or violate social norms about fairness. The Story in the Times referred to the paper by Piff et al. in the 27 February edition of PNAS.  Here is the abstract of this paper:

Seven studies using experimental and naturalistic methods reveal that upper-class individuals behave more unethically than lower-class individuals. In studies 1 and 2, upper-class individuals were more likely to break the law while driving, relative to lower-class individuals. In follow-up laboratory studies, upper-class individuals were more likely to exhibit unethical decision-making tendencies (study 3), take valued goods from others (study 4), lie in a negotiation (study 5), cheat to increase their chances of winning a prize (study 6), and endorse unethical behavior at work (study 7) than were lower-class individuals. Mediator and moderator data demonstrated that upper-class individuals’ unethical tendencies are accounted for, in part, by their more favorable attitudes toward greed.

This study was apparently motivated by observations that people in expensive luxury cars are more likely to bolt ahead of their turn at four-way stop intersections in the San Francisco Bay Area, a daily experience for anyone driving in Palo Alto! It’s terrific that these authors actually took the trouble to systematize their casual observations of driving behavior and make an interesting and compelling scientific statement.

On Friday, I made my own observations about class, cheating, and the violation of norms as I flew down to LAX to attend Sunbelt XXXII (the annual conference for the International Network for Social Network Analysis). Of late, I’ve racked up a lot of miles on United and, as a result, occasionally get upgraded to first class or business class seating. My trip Friday was one of those occasions. As I sat in the (relatively) comfy leather seat of the first-class cabin reading Jeremy Boissevain’s rather appropriate (1974) book Friends of Friends: Networks, Manipulators, and Coalitions, I noticed that nearly everyone around me was busily chatting away or otherwise fiddling around with their smart phones. When the cabin door finally closed and the announcement was made requesting that phones be switched off, none of the people in my neighborhood did so. They put their phones down or in their shirt pockets and watched the flight attendants.  When the flight attendants passed through the cabin and were occupied with other business, out came the smart phones again. The one gentleman across the aisle from me looked like a school kid writing a note in class or something. He kept a wary half-eye out for the flight attendants and looked extremely guilty about his actions, but he nonetheless kept doing his, no doubt, extremely important business.  The man on the phone in the row ahead of me was a little more shameless. He seemed completely unconcerned that he might get busted. The woman in the row ahead of me and across the aisle moved her phone so that it was partially hidden by the arm-rest of her seat as she continued to scroll through her very, very important email. Of the six people I could easily see in my neighborhood, fully half of them continued to use their phones right into taxi and take-off.  Based on their attempts at concealment, at least two of them knew what they were doing was wrong. Now, any regular traveler has seen people using their phones on the plane after they are supposed to. However, I had never seen this sort of density of norm violation on a single flight before.

Of course, this is an anecdote but the study by Piff et al. (2012) shows how anecdotes about social behavior can go on to be systematized into interesting scientific studies.

Get Off the Sexual Network

When I was in Uganda last month, I was talking with collaborators, field assistants, villagers, taxi drivers, bartenders – pretty much anyone who would listen – about social networks, I was struck by what a sophisticated understanding of social networks my average interlocutor had.  As part of our project examining the risk of zoonotic disease spillover in rural Uganda, we are gathering data on individual people’s personal networks. We are interested in contact networks, for sure, but we are also examining people’s social capital – the resources to which an individual has access for instrumental action that are embedded in his or her social network. There are generally two classes of definitions of social capital used in the literature. The first, made famous by Robert Putnam‘s book, Bowling Alone, is really a measure of community solidarity. How cohesive are communities and how does this contribute to individuals’ and communities’ welfare?  The definition I typically employ is attributable to Bourdieau and a host of other scholars, especially Nan Lin. This definition emphasizes both the networked nature of social capital and the instrumentality of it.

The reasoning behind doing a social capital inventory in conjunction with our study of zoonotic disease spillover risk is to have a thorough description of the “state” of individuals. Social surveys typically measure income, household wealth, land holdings, etc. One measures such things in a social survey because one is interested in the economic state of the individual or household in which she is embedded. Social capital is a measure of economic – and social – well-being for people where many of the resources that they need to succeed, or even just get by, are not specifically located in the household or with the individual. We suspect that people in rural Uganda will vary in the amount of social capital they have and that this may be a major axess of vulnerability.

So, there I am, talking to anyone who would listen about the best way to gather information on personal social networks and it turns out that everyone I spoke with was amazingly familiar with the whole concept of social networks. The catch is, the networks with which they are familiar are a special type of networks – sexual networks. When I asked how everyone seemed to know so much about sexual networks, they pointed me to a public-service advertising campaign for which the tag line is “get off the sexual network.” Despite the Central African origin of HIV-1, Uganda was an early center for the epidemic. However, as noted by Stoneburner and Lowbeer, in their important 2004 paper, Uganda experienced substantial – and early – decline in HIV-1 incidence because of health communication through social networks. They write:

The response in Uganda appears to be distinctively associated with communication about acquired immunodeficiency syndrome (AIDS) through social networks. Despite substantial condom use and promotion of biomedical approaches, other African countries have shown neither similar behavioral responses nor HIV prevalence declines of the same scale. The Ugandan success is equivalent to a vaccine of 80% effectiveness. (Stoneburner & Lowbeer 2004)

I definitely need to check out the current state of the art to see if other countries in Sub-Saharan Africa have now experienced similar public health gains as a result of network-oriented interventions.

Based on my rather unsystematic sample, I’d say that this campaign has really worked raise people’s understanding of relational interconnectedness.  I was not able to get a picture of the huge billboards on the Kampala-Entebbe Highway (because it was always dark when I drove by them) but the TV ad is available on youtube. On the one hand, this is really great (both for the obvious public health reasons and because people seem to have a good understanding of webs of social relations). On the other hand, it will probably mean we will need to work hard to clarify what types of networks we mean when we gather our network data.

New Grant, Post-Doc Opportunity

Biological and Human Dimensions of Primate Retroviral Transmission
One of the great enduring mysteries in disease ecology is the timing of the AIDS pandemic. AIDS emerged as a clinical entity in the late 1970s, but HIV-1, the retrovirus that causes pandemic AIDS, entered the human population from wild primates many decades earlier, probably near the turn of the 20th century. Where was HIV during this long interval? We propose a novel ecological model for the delayed emergence of AIDS. Conceptually, in a metapopulation consisting of multiple, loosely interconnected sub-populations, a pathogen could persist at low levels indefinitely through a dynamic balance between localized transmission, localized extinction, and long-distance migration between sub-populations. This situation might accurately describe a network of villages in which population sizes are small and rates of migration are low, as would have been the case in Sub-Saharan Africa over a century ago.
We will test our model in a highly relevant non-human primate system. In 2009, we documented three simian retroviruses co-circulating in a metapopulation of wild red colobus monkeys (Procolobus rufomitratus) in Kibale National Park, Uganda, where we have conducted research for over two decades. We will collect detailed data on social interactions, demography, health, and infection from animals in a core social group.
We will also study a series of 20 red colobus sub-populations, each inhabiting a separate, isolated forest fragment. We will determine the historical connectivity of these sub-populations using a time series of remotely sensed images of forest cover going back to 1955, as well as using population genetic analyses of hypervariable nuclear DNA markers. We will assess the infection status of each animal over time and use viral molecular data to reconstruct transmission pathways.
Our transmission models will define the necessary conditions for a retrovirus to persist, but they will not be sufficient to explain why a retrovirus might emerge. This is because human social factors ultimately create the conditions that allow zoonotic diseases to be transmitted from animal reservoirs and to spread. We will therefore conduct an integrated analysis of the root eco-social drivers of human-primate contact and zoonotic transmission in this system. We will study social networks to understand how social resources structure key activities relevant to human-primate contact and zoonotic transmission risk, and we will explore knowledge, beliefs, and perceptions of human-primate contact and disease transmission for a broad sample of the population. We will reconcile perceived risk with actual risk through a linked human health survey and diagnostic testing for zoonotic primate retroviruses.
The ultimate product of our research will a data-driven set of transmission models to explain the long-term persistence of retroviruses within a metapopulation of hosts, as well as a linked analysis of how human social factors contribute to zoonotic infection risk in a relevant Sub-Saharan African population. Our study will elucidate not only the origins of HIV/AIDS, but also how early-stage zoonoses in general progress from “smoldering” subclinical infections to full-fledged pandemics.

I am thrilled to report that our latest EID project proposal, Biological and Human Dimensions of Primate Retroviral Transmission, has now been funded (by NIAID nonetheless!).  I will briefly describe the project here and then shamelessly tack on the full text of our advertisement for a post-doc to work as the project manager with Tony Goldberg, PI for this grant, in the College of Veterinary Medicine, University of Wisconsin, Madison.  This project will complement the ongoing work of the Kibale EcoHealth Project. The research team includes: Tony, Colin Chapman (McGill), Bill Switzer (CDC), Nelson Ting (Iowa), Mhairi Gibson (Bristol), Simon Frost (Cambridge), Jennifer Mason (Manchester), and me. This is a pretty great line-up of interdisciplinary scholars and I am honored to be included in the list.

Biological and Human Dimensions of Primate Retroviral Transmission

One of the great enduring mysteries in disease ecology is the timing of the AIDS pandemic. AIDS emerged as a clinical entity in the late 1970s, but HIV-1, the retrovirus that causes pandemic AIDS, entered the human population from wild primates many decades earlier, probably near the turn of the 20th century. Where was HIV during this long interval? We propose a novel ecological model for the delayed emergence of AIDS. Conceptually, in a metapopulation consisting of multiple, loosely interconnected sub-populations, a pathogen could persist at low levels indefinitely through a dynamic balance between localized transmission, localized extinction, and long-distance migration between sub-populations. This situation might accurately describe a network of villages in which population sizes are small and rates of migration are low, as would have been the case in Sub-Saharan Africa over a century ago.

We will test our model in a highly relevant non-human primate system. In 2009, we documented three simian retroviruses co-circulating in a metapopulation of wild red colobus monkeys (Procolobus rufomitratus) in Kibale National Park, Uganda, where we have conducted research for over two decades. We will collect detailed data on social interactions, demography, health, and infection from animals in a core social group.

We will also study a series of 20 red colobus sub-populations, each inhabiting a separate, isolated forest fragment. We will determine the historical connectivity of these sub-populations using a time series of remotely sensed images of forest cover going back to 1955, as well as using population genetic analyses of hypervariable nuclear DNA markers. We will assess the infection status of each animal over time and use viral molecular data to reconstruct transmission pathways.

Our transmission models will define the necessary conditions for a retrovirus to persist, but they will not be sufficient to explain why a retrovirus might emerge. This is because human social factors ultimately create the conditions that allow zoonotic diseases to be transmitted from animal reservoirs and to spread. We will therefore conduct an integrated analysis of the root eco-social drivers of human-primate contact and zoonotic transmission in this system. We will study social networks to understand how social resources structure key activities relevant to human-primate contact and zoonotic transmission risk, and we will explore knowledge, beliefs, and perceptions of human-primate contact and disease transmission for a broad sample of the population. We will reconcile perceived risk with actual risk through a linked human health survey and diagnostic testing for zoonotic primate retroviruses.

The ultimate product of our research will a data-driven set of transmission models to explain the long-term persistence of retroviruses within a metapopulation of hosts, as well as a linked analysis of how human social factors contribute to zoonotic infection risk in a relevant Sub-Saharan African population. Our study will elucidate not only the origins of HIV/AIDS, but also how early-stage zoonoses in general progress from “smoldering” subclinical infections to full-fledged pandemics.

Post Doctoral Opportunity

The Goldberg Lab at the University of Wisconsin-Madison invites applications for a post-doctoral researcher to study human social drivers of zoonotic disease in Sub-Saharan Africa.   The post-doc will be an integral member of a new, international, NIH-funded project focused on the biological and human dimensions of primate infectious disease transmission in Uganda, including social drivers of human-primate contact and zoonotic transmission.  This is a unique opportunity for a post-doctoral scholar with training in the social sciences to study human-wildlife conflict/contact and health and disease in a highly relevant ecological setting.  The following criteria apply.

  1. Candidates must have completed or be near to completing a PhD in the social sciences, in a discipline such as anthropology, geography, sociology, behavioral epidemiology, or a relevant discipline within the public health fields.
  2. Candidates must have a demonstrated interest in health and infectious disease.
  3. Candidates must have prior field experience in Sub-Saharan Africa.
  4. Candidates must be willing to relocate to Madison, Wisconsin for three years.
  5. Candidates must be willing to spend substantial time abroad, including in Sub-Saharan Africa and at partner institutions in the United Kingdom.
  6. Candidates must have experience with collection and analysis of both quantitative and qualitative data.  Familiarity with methods such as social network analysis, GIS, participatory methods, and survey design would be advantageous.

The successful candidate will help lead a dynamic international team of students and other post-docs in a multi-institutional, multidisciplinary project.  Duties involve a flexible combination of fieldwork, analyses, and project coordination, in addition to helping to mentor students from North America, Europe, and Africa.  The successful applicant will be expected to explore new research directions of her/his choosing, assisted by a strong team of collaborators.

University of Wisconsin-Madison is a top-notch institution for research and training in the social and health sciences.  Madison, WI, is a vibrant city with outstanding culture and exceptional opportunities for outdoor recreation.

Applicants should send a current CV, a statement of research interests and qualifications (be sure to address the six criteria above), and a list of three people (names, addresses, e-mails) who can serve as references.

Materials and inquiries should be sent to Dr. Tony L. Goldberg (  Application materials must be received by September 12, 2011 for full consideration; the position is available starting immediately and requires a three-year commitment.

My Erdős Number

Paul Erdős was the great peripatetic, and highly prolific, mathematician of the 20th century. A terrific web page run by Jerry Grossman at Oakland University provides details of the Erdős Project. Erdős was a pioneer in graph theory, which provides the formal tools for the analysis of social networks.  A collaboration graph is a special graph in which the nodes are authors and an edge connects authors if they co-author a publication. Erdős was such a prolific collaborator that he forms a major hub in the mathematics collaboration graph, linking many disparate authors in the different realms of pure and applied mathematics.

For whatever reason, today I used Grossman’s directions for finding one’s number. <drum roll> My Erdős number is 4.  The path that leads me to Erdős is pretty sweet, I have to say.  This past year, I published a paper in PNAS with Marc Feldman.  Marc wrote a number of papers (here’s one) with Sam Karlin (who, I’m proud to say, came and slept through at least one talk I gave at the Morrison Institute). Karlin wrote a paper with Gábor Szegő, who wrote a paper with Erdős.  Lots of Stanford greatness there that I feel privileged to be a part of. It turns out that I have independent (though longer) paths through my co-authors Marcel Salathé and Mark Handcock as well.

An Alternate Course Load for the Game of Life

In a recent editorial in the New York Times, Harvard economist and former chairman of the Council of Economic Advisers, N. Gregory Mankiw provides some answers to the question “what kind of foundation is needed to understand and be prepared for the modern economy?”  Presumably, what he means by “modern economy” is life after college.  Professor Mankiw suggests that students of all ages learn something about the following subjects: economics, statistics, finance, and psychology.  I read this with interest and doing so made me think of my own list, which is rather different than the one offered by Mankiw. I will take up the instrumental challenge, making a list of subjects that I think will be useful in an instrumental sense — i.e., in helping graduates become successful in the world of the twenty-first century. In no way do I mean to suggest that students can not be successful if they don’t follow this plan for, like Mankiw, I agree that students should ignore advice as they see fit. Education is about discovery as much as anything and there is much to one’s education that transcends instrumentality — going to college is not simply about preparing people to enter “the modern economy,” even if it is a necessary predicate for success in it.

People should probably know something about economics.  However, I’m not convinced that what most undergraduate students are taught in their introductory economics classes is the most useful thing to learn. Contemporary economics is taught as an axiomatic discipline.  That is, a few foundational axioms (i.e., a set of primitive assumptions that are not proved but considered self-evident and necessary) are presented and from these, theorems can be derived.  Theorems can then be logically proven by recourse to axioms or other already-proven theorems. Note that this is not about explaining the world around us.  It is really an exercise in rigorously defining normative rules for how people should behave and what the consequences of such behavior would be, even if actual people don’t follow such prescriptions. Professor Mankiw has written a widely used textbook in Introductory Economics. In the first chapter of this book, we see this axiomatic approach on full display.  We are told not unreasonable things like “People Face Trade-Offs” or “The Cost of Something is What You Give Up to Get It” or “Rational People Think at the Margin.” I couldn’t agree more with the idea that people face trade-offs, but I nonetheless think there are an awful lot of problematic aspects to these axioms.  Consider the following paragraph (p. 5)

Another trade-off society faces is between efficiency and equality. Efficiency means that society is getting the maximum benefits from its scarce resources. Equality means that those benefits are distributed uniformly among society’s members. In other words, efficiency refers to the size of the economic pie, and equality refers to how the pie is divided into individual slices.

Terms like “efficiency” and “maximum benefits” are presented as unproblematic, as is the idea that there is a necessary trade-off between efficiency and equality.  Because it is an axiom, apparently contemporary economic theory allows no possibility for equality in efficient systems. Inequality is naturalized and thereby legitimized. It seems to me that this should be an empirical question, not an axiom. In his recent book, The Bounds of Reason: Game Theory and the Unification of the Behavioral Sciences, Herb Gintis provides a very interesting discussion of the differences between two highly formalized (i.e., mathematical) disciplines, physics and economics.  Gintis notes, “By contrast [to the graduate text in quantum mechanics], the microeconomics text, despite its beauty, did not contain a single fact in the whole thousand page volume. Rather, the authors build economic theory in axiomatic fashion, making assumptions on the basis of their intuitive plausibility, their incorporation of the ‘stylized facts’ of everyday life, or their appeal to the principles of rational thought.”

If one is going to learn economics, “the study of how society manages its scarce resources” — and I do believe people should — I think one should (1) learn about how  resources are actually managed by real people and real institutions and (2) learn some theory that focuses on strategic interaction.  A strategic interaction occurs when the best choice a person can make depends upon what others are doing (and vice-versa). The formal analysis of strategic interactions is done with game theory, a field typically taught in economics classes but also found in political science, biology, and, yes, even anthropology. Alas, this is generally considered an advanced topic, so you’ll have to go through all the axiomatic nonsense to get to the really interesting stuff.

OK, that was a bit longer than I anticipated. Whew.  On to the other things to learn…

Learn something about sociology. Everyone could benefit by understanding how social structures, power relations, and human stocks and flows shape the socially possible. Understanding that social structure and power asymmetries constrain (or enable) what we can do and even what we think is powerful and lets us ask important questions not only about our society but of those of the people with whom we sign international treaties, or engage in trade, or wage war. Some of the critical questions that sociology helps us ask include: who benefits by making inequality axiomatic? Does the best qualified person always get the job? Is teen pregnancy necessarily irrational? Do your economic prospects depend on how many people were born the same year as you were? How does taste reflect on one’s position in society?

People should definitely learn some statistics. Here, Professor Mankiw and I are in complete agreement.

Learn about people other than those just like you. The fact that we live in an increasingly global world is rapidly becoming the trite fodder of welcome-to-college speeches by presidents, deans, and other dignitaries. Of course, just because it’s trite doesn’t make it any less true, and despite the best efforts of homogenizing American popular and consumer culture, not everyone thinks or speaks like us or has the same customs or same religion or system of laws or healing or politics. I know; it’s strange. One might learn about other people in an anthropology class, say, but there are certainly other options. If anthropology is the chosen route, I would recommend that one choose carefully, making certain that the readings for any candidate anthropology class be made up of ethnographies and not books on continental philosophy. Come to grips with some of the spectacular diversity that characterizes our species. You will be better prepared to live in the world of the twenty-first century.

Take a biology class. If the twentieth century was the century of physics, the twenty-first century is going to be the century of biology.  We have already witnessed a revolution in molecular biology that began around the middle of the twentieth century and continued to accelerate throughout its last decades and into the twenty-first. Genetics is creeping into lots of things our parents would not have even imagined: criminology, law, ethics. Our decisions about our own health and that of our loved ones’ will increasingly be informed by molecular genetic information. People should probably know a thing or two about DNA. I shudder at popular representations of forensic science and worry about a society that believes what it sees on CSI somehow represents reality. I happen to think that when one takes biology, one should also learn something about organisms, but this isn’t always an option if one is going to also learn about DNA.

Finally, learn to write.  Talk about comparative advantage! I am continually blown away by poor preparation that even elite students receive in written English. If you can express ideas in writing clearly and engagingly, you have a skill that will carry you far. Write as much as you possibly can.  Learn to edit. I think editing is half the problem with elite students — they write things at the last minute and expect them to be brilliant.  Doesn’t work that way. Writing is hard work and well written texts are always well edited.

The Little Mouse on the Prairie

We have a new paper in the Early Edition of PNAS on the ecology of plague in prairie dogs. The Stanford News Service did a nice little write-up of the paper (and Mark Shwartz’s full version is available on the Woods Institute site) and it has now been picked up by a number of media outlets including USA Today, ScienceDaily, The Register (UK), as well as a couple of radio news shows. This paper has been a real pleasure for me because of my incredible collaborators.  Dan Salkeld, who has been a post-doctoral fellow with me and now splits his time between teaching in Human Biology at Stanford and working as an epidemiologist for the California Department of Health, is the lead author.  Dan is clearly one of the leading young disease ecologists working today and his understanding of the field and willingness to do the sometimes unglamorous grunt work of ecology in pursuit of important research questions continually impresses me. The paper uses data that he collected while he worked for co-author Paul Stapp on Paul and collaborators’ plague project in the Pawnee National Grasslands in Colorado. Dan and Paul had the idea that grasshopper mice (see below) might have something to do with the episodic plague outbreaks in prairie dog towns.  Apparently, this idea was met with skepticism by their colleagues. When Dan came to Stanford, I suggested that we could probably put together a model to test the hypothesis. While we were waiting for our research permits to come through for a project in Indonesia (also dealing with plague; another long story), we decided to take up the challenge. What really made the whole project come together was the fortuitous office-pairing of Dan with Marcel Salathé, another post-doc with whom I have collaborated extensively on questions of social networks and infectious disease.  In addition to being a brilliant theoretical biologist, Marcel is an ace Java programmer.  Following a few white-board sessions in the studio near our offices, Dan and Marcel put together an amazing computer simulation that achieves that perfect balance between simplicity and realism that allows for scientific insight.

I don’t think anyone would have predicted this particular collaboration and this particular outcome.  The results described in this paper come from an incredibly interdisciplinary collaboration. I am really struck at how great science can come from a few simple ingredients: (1) long-term ecological data collection facilitated by a visionary program at the National Science Foundation, (2) a space where people from quite different disciplines and with different scientific sensibilities can get together and brain-storm, (3) flexible funding that permits researchers to explore the interesting – if offbeat – scientific questions that arise from such interactions.  So, I have many debts to acknowledge for this one.  The field data come from the project for which Mike Antolin at Colorado State is the PI (out co-author Paul Stapp is a Co-PI for that as well).  The funding source for this project was the joint NSF/NIH Ecology of Infectious Disease program. This is a cross-cutting program that “supports the development of predictive models and the discovery of principles governing the transmission dynamics of infectious disease agents” (from the EID home page).  The space – both physical and intellectual – that permitted this work to happen was provided by the Woods Institute for the Environment.  This paper literally came into being in the project studio on the third floor of Y2E2 in the Land Use and Conservation area.  Amazingly, this is exactly what these studios were designed to do.  My office in Y2E2 has adjoining office space for grad-students and post-docs and this is where Marcel and Dan did most of their hashing. It was always amusing to pop my head in and see them both huddled around a computer, having animated discussions about how best to represent the complex ecology in a computational model that is simple enough to understand and flexible enough to allow us to test hypotheses.  Finally, funding. Dan was funded by a Woods Environmental Ventures Project grant for which I am the PI.  Marcel was funded by the Branco Weiss Science in Society Fellowship.  My own flexibility was assured by a career grant form the National Institutes of Health. Research funding is almost always important, but the requirements of research funding can sometimes be too constraining to permit exploration of really new ideas.  All three of these mechanisms (Woods EVP, Branco Weiss, NIH K01) provide exactly the type of flexibility that fosters creativity. I wish there were more programs like these.

One of the fundamental questions in disease ecology is how extremely pathogenic infectious agents persist both through time and across landscapes.  Plague is a bacterial disease that affects a wide range of rodents throughout the world and, in North America, particularly afflicts prairie dogs (Cynomys ludovicianus).  Plague epizootics (the animal equivalent of epidemics in humans) are dramatic affairs with almost complete mortality of massive prairie dog ‘towns’ of thousands of animals. If plague is so deadly to prairie dogs, how does it persist?  Is there another reservoir (i.e., an other host species that can maintain an infection in the absence of prairie dogs)?  Does plague get into the soil and persist in some sort of suspended state (the way that some Mycobacteria do, for example) waiting to reinfect a re-colonized prairie dog town? Or is plague really enzootic (i.e., when an infection persists at low levels in an animal population) and we just haven’t detected it? This question has wide applicability. Consider diseases of people such as Ebola Hemorrhagic Fever or SARS, or, going back a few hundred years in human history, that nastiest of bacterial diseases, bubonic plague. Yes, the same beastie.  A disease that killed a third of the population of Europe in the fourteenth century exists in prairie dogs in North America today (and sometimes spills over to produce human infections).

Prairie dogs are a keystone species of the grasslands of the American West. They are  threatened by various anthropogenic forces, including habitat destruction and human persecution.  But most importantly, prairie dog viability is threatened by plague.

Plague, a disease caused by the bacterium (Yersinia pestis) and the causative agent of Black Death, arrived in USA via San Francisco ca. 1900, and still infects (or threatens to infect) people each year, including in California. Plague killed as many as 200 million people in Medieval Europe. It is still important in Africa and Asia.  There have been sizable epidemics as recently as the middle twentieth century in India and China and a substantial outbreak in Surat, India in 1994 that, in addition to death, caused widespread panic and social disruption.

Previous modeling and ecological work tended to assume that die-outs occur very rapidly.  But questions dogged this work (as it were): were the apparently rapid die-offs simply an artifact of finally seeing dead dogs dropping all over the place? Prairie dogs do live underground, after all, and they live in enormous towns.  Who would miss a few dead dogs underground in a town of thousands?  Our paper suggests that previous modeling efforts get the story wrong. They fail to account for observed patterns because they missed key elements of the picture.  Previous models that could describe the phenomena lacked an actual explanation – it’s a magical reservoir? It’s a carnivore? Certainly it’s something somewhere?

While prairie dogs live in enormous towns, they are highly territorial within the towns.  Towns form because of the benefits of predator defense. They live in small family groups known as coteries, and these coteries form a more-or-less regular grid of small defended territories within the towns. Because of this regular structure induced by their territoriality, a directly-transmitted infectious disease can only move so quickly through a town since it could only be transmitted to immediate neighbors and each coterie only has a couple of these.  Plague is not directly transmitted though.  It is carried by flea vectors, but if the dispersal distance of a flea is less than the diameter of a coterie’s territory, then the transmissibility of this vector-borne disease is similar to something that is directly transmitted.  Prairie dogs are territorial and this territoriality limits the rate of disease propagation through prairie dog towns. However, prairie dogs are not alone on their eponymous prairies.

Grasshopper mice – smelly, carnivorous mice, happy to eat through prairie dog carcasses – get swamped by fleas that normally live on prairie dogs. And grasshopper mice have no respect for prairie dog territories. They spread fleas across prairie dog coteries. This is the critical piece of the puzzle provided by our analysis.  Grasshopper mice are the key amplifying hosts for plague in prairie dogs.  Grasshopper mice increase the spread of disease by moving fleas across the landscape, similar to the way that highly promiscuous people may spread HIV or so-called ‘super-spreders‘ transmitted SARS in the global outbreak of 2003.  Of course, there are interesting differences between the plague model and these other diseases. Grasshopper mice are like super-spreaders in that they push the system over the percolation threshold.  They are unlike super-spreaders in that they don’t have that many more contacts than the average – they just connect otherwise unconnected segments of a population already near the threshold of an epidemic.

Without grasshopper mice, plague still kills prairie dog families, one at a time, but it moves very slowly, and it is extremely hard to detect (who misses 5 dead prairie dogs in a colony that stretches for 200 hectares and has upwards of 5000 animals?).  The grasshopper mice take a spatially-organized system that is on the verge of an epizootic and push it over the threshold.  The term ‘percolation threshold’ in the title of our paper relates to a branch of theory from geophysics that explains how and when a fluid can pass through a porous random medium.  This theory uses random graphs, which are the same mathematical structure that we use to model social networks, to understand when, for example, a medium will let water pass through it – i.e., to percolate. When the density of pores in, say, a layer of sandstone passes a critical density, water can pass from the surface through to recharge the aquifer. Similarly, when the density of susceptible prairie dog families crosses a critical threshold, plague can sweep through and wipe out a town of thousands of individuals.  The spatial structure induced by prairie dog territoriality turns out, on average, to be not quite at the percolation threshold (though it’s close).  What the grasshopper mice do is provide the critical connectivity that puts the system over the threshold and allows a slowly simmering enzootic infection turn into a full-blown epizootic.

It is in thinking about percolation thresholds that we see how important the behavior of affected species is for understanding disease dynamics.  Plague in Asian great gerbils, while effectively modeled using the same mathematical formalism, only requires one species in order to achieve the percolation threshold. Because great gerbils roam more widely and mix more, what matters for plague epizootics in this species is simply overall gerbil density.

It seems quite likely that this pattern of diseases smoldering at low-level below the detection threshold before some dramatic occurrence brings them to general attention is common, particularly with emerging infections. For example, there is evidence for extensive transmission of H1N1 ‘swine flu’ in Mexico before a large number of deaths appeared seemingly quite suddenly in April of 2009.  A number of other diseases – both of people and wildlife – show this pattern of being seemingly completely lethal, burning through host communities, and disappearing only to reappear some years later.  Important examples include Ebola in both humans and gorillas, hantavirus in people, anthrax in zebra, or chytrid fungi and frogs.

What are the key take-home messages of this paper? There are five, as far as I see it: (1) plague is enzootic in prairie dogs and there is no need to posit an alternate reservoir, (2) this said, the transition from enzootic to epizootic infection in prairie dogs is mediated by grasshopper mice, (3) understanding disease ecology – including species interactions – is a key to understanding (and predicting) dynamics, (4) behavior matters for disease dynamics, and (5) epidemiological surveillance is essential for controlling infectious disease – just because you don’t see a disease, doesn’t mean it’s not there!

I’m sure I’ll have more to say about this.  I did want to note that the publication of this paper coincides with a personnel transition here in our group at Stanford. Marcel has moved on to a faculty position, joining the spectacular Center for Infectious Disease Dynamics at Penn State.  Peter Hudson and his crew have assembled an amazing and eclectic group of scientists in Happy Valley and kudos to them for landing Marcel.  I frequently think that only a total fool would pass up an offer to join this exciting and productive group, but that’s another story. I expect Marcel to do great things there and look forward to continued collaborations.

New Paper: Dynamics and Control of Diseases in Networks with Community Structure

Marcel Salathé and I have a brand new paper out in today’s issue of the Public Library of Science, Computational Biology. There is also a news piece by Adam Gorlick in the Stanford Report this morning. This is an idea I’ve been bouncing around for a few years now and I was very fortunate to have Marcel – and his programming wizardry – show up with an interest in the very same topic just at the right time. It’s not every day that one of the most talented young theoretical biologists in the world shows up at your office wanting to collaborate. If it ever happens to you, I suggest you act!

The fundamental question is: Does social structure affect that course of epidemics? The answer seems obvious, particularly for infectious diseases that are transmitted by direct person-to-person contact. However, specific work demonstrating the effects of social structure on epidemics can be hard to find. Part of the problem, of course, is that you can hardly do experiments in which you change social structure and then subject populations to an infectious disease. To overcome this ethical and practical barrier to research, epidemiologists, biologists, and social scientists interested in disease and human behavior use mathematical and computational models to study how changes in host behavior affect the outcome of simulated epidemics.

Two specific topics that clearly have some bearing on social structure have been investigated extensively: individual heterogeneity in contact number and individual assortativeness. Epidemic behavior in all but the simplest models has been seen as being driven by heterogeneity. When there is a lot of variance in the number of potentially infectious contacts that individuals in a population have, epidemics are more likely, they infect large segments of the population more quickly, and ultimately infect a larger fraction of the total population. Consider the extreme case where all members of a population have one contact except for one person, who has a contact with everyone else. If we were to draw a picture of such a contact network, it would resemble a star or a wheel with a central hub and spokes:


Infect any random individual on this star and everyone else is at risk for infection. At the opposite extreme, if everyone has exactly one contact, then a randomly infected person can infect, at most, one other individual.


Assortativeness, the tendency for individuals to associate with others like themselves, can either aid or hinder the spread of infections. People in contemporary nation states like the United States show an incredible capacity to form associations with like individuals. We form social relationships, particularly intimate relationships, with people who are similar to us in age, socioeconomic status, sexual orientation, ethnicity, education, religion, forms of deviance behavior such as drug use or criminal activity, etc. Frequently, this assortativeness has the effect of localizing and concentrating epidemiologically important contacts. When this happens, individuals who act as bridges between different communities take on central epidemiological importance. For example, married men who visit commercial sex workers can serve as a critical bridge connecting high-risk populations of sex workers and injection drug users with the general population. Similarly, health care workers can bridge hospital populations with the general population, a phenomenon important for the emergence of SARS in 2002. (Note that for epidemiological applications, we call such individuals “bridges” but in other applications we might call them “brokers” or “entrepreneurs,” highlighting the general importance of such ideas for understanding society.) The existence of such social bridges highlights the fact that people can also assort on characteristics that are not visible attributes and this type of assortative behavior can increase connectivity. In particular, if people with few contacts tend to be connected to people with many contacts (as in the case of the star), then such disassortativeness can increase the epidemic potential in a population.

The aggregate effects of individual behavioral decisions can have a profound effect on the shape and composition of human populations, but there is more to human populations than simply individual behavior. For one thing, human populations are characterized by a hierarchical structure: individuals typically belong to households and households are aggregated into communities, which are, in turn, aggregated in towns, states, nations, etc. Naturally, there are cross-cutting ties in such hierarchical organization (much like bridges in individual contact networks). Freudian fantasies of primitive hordes aside, even the largely egalitarian societies of hunter-gatherers are characterized by a hierarchical structuring of families, bands, and tribes. Hierarchical structuring is clearly important for understanding social process in human societies.

So what effect does such community structure have on epidemics? To address this question, Marcel and I combined the formalisms of social network analysis and computational models of epidemics. We already know that heterogeneity in contact number can have profound effects on the outcomes of epidemics and that such heterogeneity can change aggregate social structure in complex ways. To avoid such complications, we generated networks where every individual had the exact same number of contacts. The only thing that varied in these toy networks was the likelihood that any randomly chosen connection between two individuals would be either within or between more or less cohesive subgroups (a.k.a., “communities”). Using metrics derived from Graph Theory, the branch of discrete mathematics that provides the basic tools for Social Network Analysis, we were able to characterize the degree of community structure and relate this to the outcome of epidemics simulated on the resulting networks.

It turns out that community structure has an enormous effect on epidemic outcome. In particular, we found that there is a remarkably abrupt transition from small outbreaks to very large outbreaks as we moved from the most structured populations to more moderately structured ones. Populations characterized by extreme community structure have smaller outbreaks because the infection has a hard time getting out of a community before dying out. As more connections to other communities are made – i.e., the community structure is lessened – there are more opportunities for the infection to escape and affect a larger fraction of the total population. While the result sounds intuitively satisfying after the fact, there was little precedent for expecting such an outcome in the mathematical theory of epidemics. This is because none of the standard metrics of an infectious disease – the basic reproduction ratio, in particular – changed as the populations’ community structure changed.

When we investigated the further structural network correlates of epidemic size, we found that one measure in particular predicted epidemic behavior quite well. This measure, known as “betweenness centrality,” harkens back to previous epidemiological interest in bridging individuals. A person with high betweenness lies on many of the shortest paths that connect all individuals in a network. When a person bridges two distinct subpopulations, he or she typically has high betweenness because all paths from individuals in one cluster have to pass through this person to get to the other cluster, and vice-versa. As a population moves from a condition of very high community structure to a more moderate level, the number of people with high betweenness increases. This highlights a particularly interesting contrast with previous models: epidemics are more likely and larger in populations with highly unequal distributions of contacts on the one hand, but also in populations with more equal betweenness.

With the information that betweenness predicts the extent of epidemic spread in populations with community structure, we sought a means to use such information to design intelligent control measures. How do you find people who have high betweenness? As abstract as the concept of betweenness may seem, it turns out to not be that difficult. We start with an infected person and do standard contact tracing. That is, we ask the index case about his or her contacts. Contact tracing is one of the most important tools in the toolkit of the gumshoe epidemiologist. From the index case’s contacts, we pick a random individual and trace his or her contacts. Picking a random individual from this second generation of contact traces, we simply ask “do you know the index case?” If so, we keep going: trace the contacts of a random contact, ask again if this person knows the index case. When we come to an individual who does not know the index case, we have found our bridge. It is the penultimate person in the chain – the person who links the index case to someone he or she doesn’t know. Basically, we do a “random walk” on the social network looking for people who link otherwise unconnected individuals. When we find the bridge, we vaccinate all of his/her contacts. We call our vaccination algorithm the “Community Bridge Finder” (CBF).

When we vaccinate according to this algorithm, we reduce the final size of the epidemic far more than randomly vaccinating the same fraction of people. More interestingly, CBF also does better than the other vaccination algorithm that uses only local network information typically available to epidemiological investigators. This algorithm, known as the “Acquaintance Method,” vaccinates a randomly selected contact of an index case. The idea behind the acquaintance method is that the contacts of a case are more likely than chance to be highly connected individuals themselves in a population with heterogeneous contacts. That is, given that you have a contact, you’re on average more likely to be connected to a hub than to someone with few connections because hubs simply have more connections.

Of course, the way that we constructed our contact networks, we stacked the deck against the acquaintance method. Remember, everyone has the same number of contacts; what varies is how many contacts are within versus between communities. One of the great limiting factors for progress in social network analysis – and network epidemiology in particular – is the paucity of detailed network data from well-defined human populations. A domain that has garnered a lot of interest recently is the analysis of networks created by social media such as Facebook and Twitter. We used data from Facebook when its use was still restricted to particular college campuses to provide networks on which infections could pass. Facebook users typically have many contacts, probably way more than people have in epidemiologically relevant networks. However, because the data come from college acquaintance networks, we were able to prune the networks down toward something hopefully more epidemiologically appropriate. We kept contacts in the networks only if two individuals shared one a several key attributes such as shared dorm or major. What this yielded were a series of networks with heterogeneous contact structure and quite a bit of community structure (the measure of community structure hovered near the values where epidemics transitioned from small to large in our simulated networks). Once again, CBF outperformed the acquaintance method. This provided very strong evidence that community structure really matters for epidemic behavior and that exploiting information on community structure allows us to better control outbreaks of infectious disease.

More on Diamond

I’ve been thinking some more about the issues that are raised by the debacle over Jared Diamond’s 21 April 2008 New Yorker piece and the recent announcement of a lawsuit against him.  There are many things to think about here.  Probably foremost amongst these are the ethical concerns relating to preserving research subjects’ privacy and informed consent.  There are secondary concerns regarding scholarship, standards of research, and obligations to adequately describe research methodology.

I am troubled by a point raised by Alex Golub in the Savage Minds blog. Golub writes, “There is also a more serious problem with [Diamond’s New Yorker] article which is also the most obvious thing about it: it contrasts ‘tribal societies’ with ‘modern state societies’. ”  This is something that bothers me too though I think that my response may be somewhat different than that of many contemporary cultural anthropologists. In general, I have sensibilities very much akin to Diamond’s. I see tremendous value in comparative studies, and I think that there is something that we can call, for lack of a better term, a robust and fairly general Human Nature.  Human beings are biological entities with material needs and (many) material motivations and we ignore these at our explanatory (and possibly literal) peril.

The Myth-of-Isolation criticism, which also arises in the Diamond debacle, is not new in Anthropology.  I am reminded of the Kalahari Debate of Lee, Wilmsen and others. Globalization as a phenomenon of anthropological inquiry has certainly increased in currency of late and I think that this scholarship tends to make many of my colleagues skeptical of any research on, say, foraging decisions by hunting and gathering people.  The answer to this criticism is that foraging people in a globalized world, like all people, still make decisions about what to eat, what not to eat, how to eat, etc.  Their choices may be constrained by a hegemonic state or by extra-state organizations, but choices are still being made.  Understanding how such choices are made in a globalized world strikes me as being at least as important as it was 50 or 100 years ago.   This goes for hunter-gatherers as well as urban elites, agrarian peasants or just about anyone else.

Rather than taking labels such as “tribal” or “state” as sufficient descriptions of the differences between groups, I think that the science requires us to describe (and hopefully quantify) the dimensions of their difference.  I have been thinking a lot about social networks lately.  One dimension on which two societies might differ is the composition of ego networks.  How many people does a given person know?  What fraction of those are kin?  What is the gender composition of the ego network? How socially similar are the member’s of ego’s network to him/herself? How many would provide emotional/economic/agonistic support to you in a crisis?  Does an individual’s ego network include socially important figures like government functionaries, doctors, lawyers or the equivalent? How much does ego’s network overlap with his/her spouse’s? Brother’s? Neighbor’s? Member of the next village/town? Gathering such data is clearly a major undertaking, but that’s what science is about, no?

The fraught question of how to do ethical, meaningful anthropology in a globalized world that struggles with the legacy of colonial depredations has, in my view, driven too many anthropologists from science. Protecting human subjects and doing unto others what we would have done to us are important guiding principles for anthropological research, indeed, any research in the human sciences.  Describing — and, ultimately, understanding — how societies differ and what the implications of these differences are for human behavior should, in my opinion, be another principle.  Facile labels relating to social or economic complexity, ethnicity, religion, nationality, etc. do not help us understand the diversity of human behavior.