# Winter Anthropology Colloquium, Part 1

I am organizing the colloquium for the Stanford Anthropology department this winter. I believe it may be the first time that a faculty member for the Ecology and Environment group has organized the colloquium since the Blessed Event that merged departments back in 2008 (though I’m not certain of that). There have been a few scheduling glitches, as it seems winter quarter 2015 has the highest density of talks I’ve yet encountered in 11 years at Stanford, but we’re off to a great start. Our first speaker came all the way from the UK to speak to us about social dilemmas and cooperation. Shakti Lamba is an ESRC Research Fellow and Lecturer in Human Behavioural Ecology in the Centre for Ecology and Conservation at the University of Exeter.

Shakti talked about her very exciting work on behavioral norms. She uses a variety of methods, including ethnography, experimental games, and advanced statistical techniques to understand the nature of variation in cooperative norms within and between populations (see, e.g., papers here or here for examples of her work). I generally have mixed feelings about experimental games, but I think there is a small cadre of anthropologists, including Shakti and Drew Gerkey, among others, who use them as a tool for eliciting much richer behavioral and social observations than do most field researchers (whether or not they use experimental games!). I was impressed by the sophistication of her approach, her keen experimental design, and the excellent population thinking that it entails. However, I was most impressed with her coolness and eloquence under some pretty heated questioning from a number of senior faculty members who simply misunderstand evolutionary process.  Looking forward to seeing more of her work, especially forthcoming longitudinal research with Alex Alvergne, in the future!

Here is the poster for her talk:

Well, it certainly has been a while since I’ve written anything here. Life has gotten busy with new projects, new responsibilities, etc. Yesterday, I participated in a workshop on campus sponsored by the Woods Institute for the Environment, the Young Environmental Scholars Conference. I was asked to stand-in for a faculty member who had to cancel at the last minute. I threw together some rather hastily-written notes and figured I’d share them here (especially since I spoke quite a bit of the importance for public communication!).

The theme of the conference was “Environmental Policy, Behavior, and Norms” and we were asked to answer three questions: (1) What does doing normative research mean to you? (2) How do your own norms and values influence your research? (3) What room and role do you see for normative research in your field? So, in order, here are my answers.

What does doing normative research mean to you?

I actually don’t particularly like the term “normative research” because it sounds a little too much like imposing one’s values on other people. I am skeptical of the imposition of norms that have more to do with (often unrecognized) ideology and less about empirical truth – an idea that was later reinforced by a terrific concluding talk by Debra Satz. If I can define “normative” to mean with the intent to improve people’s lives, then OK.  Otherwise, I prefer to do “positive” research.

For me, normative research is about doing good science. As a biosocial scientist with broad interests, I wear a lot of hats. I have always been interested in questions about the natural world, and (deep) human history in particular. However, I find that the types of questions that really hold my interest these days are more and more engaged in the substantial challenges we face in the world with inequality and sustainability. In keeping with my deep pragmatist sympathies, I increasingly identify with Charles Sanders Pierce‘s idea that given the “great ocean of truth” that can potentially be uncovered by science, there is a moral burden to do things that have social value. (As an aside, I think that there is social value in understanding the natural world, so I don’t mean to imply a crude instrumentalism here.) In effect, there is a lot of cool science to be done; one may as well do something of relevance.  I personally have little patience for people who pursue racist or otherwise socially divisive agendas and cloak their work in a veil of  free scientific inquiry.  This said, I worry when advocacy interferes with intellectual fairness or an unwillingness to accept that one’s position is not actually true.

I think that we are fooling ourselves if we believe that our norms somehow don’t have an effect on our research.  Recognizing what these norms that shape your research – whether implicitly or explicitly – helps you manage your bias. Yes, I said manage. I’m not sure we can ever completely eliminate it. I see this as more of a management of a necessary trade-off, drawing an analogy between the practice of science and a classic problem in statistics, between bias and variance. The more biased one is, the less variance there is in the outcome of one’s investigation. The less bias, the greater the likelihood that results will differ from one’s expectations (or wishes). Recognizing how norms shape our research also deals with that murky area of pre-science: where do our ideas for what to study come from?

Some of the the norms that shape my own research and teaching include:

transparency: science works best when it is open. This places a premium on sharing data, methods, and communicating results in a manner that maximizes access to information. As a simple example, this norm shapes my belief that we should not train students from poor countries in the use of proprietary software (and other technologies) that they won’t be able to afford when they return to their home countries when there are free or otherwise open-source alternatives.

fairness: this naturally includes a sense of social justice or people playing on an equal playing field, but it also includes fairness to different ideas, alternative hypotheses, the possibility that one is wrong. This type of fairness is essential for one’s credibility as a public intellectual in science (particularly supporting policy), as noted eloquently in this interview with Dick Lewontin.

respect for people’s ultimate rationality: Trying to understand the social, ecological, and economic context of people’s decision-making, even if it violates our own normative – particularly market-based economic – expectations.

flexibility: solving real problems means that we need to be flexible in our approach, willing to go where the solutions lead us, learning new tools and collaborating. Flexibility also means a willingness to give up on a research program that is doing harm.

good-faith communication: I believe that there is no room for obscurantism in the academy of the 21st century. This includes public communication. There are, of course, complexities here with regard to the professional development of young scholars.  One of the key trade-offs for young scholars is the need for professional advancement (which comes from academic production) and activism, policy, and public communication. Within the elite universities, the reality is that neither public communication nor activism count much for tenure. However, as Jon Krosnick noted, tenure is a remarkable privilege and, while it may seem impossibly far away for a student just finishing a Ph.D., it’s not really. Once you prove that you have the requisite disciplinary chops, you have plenty of time to to use tenure for what it is designed for (i.e., protecting intellectual freedom) and engaging in critical public debate and communication.

humility: solving problems (in science and society) means caring more about the answer to a problem than one’s own pet theory. Humility is intimately related to respect for others’ rationality.  It also means recognizing the inherently collaborative nature of contemporary science: giving credit where it is due, seeking help when one is in over one’s head, etc. John DeGioia, President of Georgetown University, quoted St. Augustine in his letter of support for Georgetown Law Student, Sandra Fluke against the crude attacks by radio personality Rush Limbaugh and I think those words are quite applicable here as well.  Augustine implored his interlocutors to “lay aside arrogance” and to “let neither of us assert that he has found the truth; let us seek it as if it were unknown to both.” This is not a bad description of the way that science really should work.

What room and role do you see for normative research in your field?

I believe that there is actually an enormous amount of room for normative research, if by “normative research,” we mean research that has the potential to have a positive effect on people’s lives. If instead we mean imposing values on people, then I am less sure of its role.

Anthropology is often criticized from outside the field, and to a lesser extent, from within it for being overly politicized. You can see this in Nicholas Wade’s critical pieces in the New York Times Science Times section following the American Anthropological Association’s executive committee excising of the word “science” from the field’s long-range planning document. Wade writes,

The decision [to remove the word ‘science’ from the long-range planning document] has reopened a long-simmering tension between researchers in science-based anthropological disciplines — including archaeologists, physical anthropologists and some cultural anthropologists — and members of the profession who study race, ethnicity and gender and see themselves as advocates for native peoples or human rights.

This is a common sentiment. And it is a complete misunderstanding. It suggests that scientists can’t be advocates for native peoples or human rights.  It also suggests that one can’t study race, ethnicity, or gender from a scientific perspective.  Both these ideas are complete nonsense.  For all the leftist rhetoric, I am not impressed with the actual political practice of what I see in contemporary anthropology. There is plenty of posturing about power asymmetries and identity politics but it is always done in such a mind-numbingly opaque language and with no apparent practical tie-in to policies that make people’s lives better. And, of course, there is the outright disdain for “applied” work one sees in elite anthropology departments.

Writing specifically about Foucault, Chomsky captured my take on this whole mode of intellectual production:

The only way to understand [the mode of scholarship] is if you are a graduate student or you are attending a university and have been trained in this particular style of discourse. That’s a way of guaranteeing…that intellectuals will have power, prestige and influence. If something can be said simply, say it simply, so that the carpenter next door can understand you. Anything that is at all well understood about human affairs is pretty simple.

Ultimately, the simple truths about human affairs that I find anyone can relate to are subsistence, health, and the well-being of one’s children. These are the themes at the core of my own research and I hope that the work I do ultimately can effect some good in these areas.

# Tragedy in Norway

I am saddened and sickened to learn of the horrific events in Norway today. As I write this, the news is that a total of 80 have died, 7 in the bombing in Oslo and the rest, presumably, at the youth camp in Utoya Island. This is an unimaginable tragedy for the parents of these children and would be wherever such an event occurred.  The impact on aggregate mortality  just happens to be particularly acutely noticeable in a low-mortality country such as Norway.  I look at Norwegian mortality data quite a bit because I use mortality change in Norway as an example in at least two classes I teach. To give a sense of what an enormous impact 80 violent deaths have on the overall mortality of a relatively small, and very low-mortality country like Norway, I plotted the number of deaths by age on semi-logarithmic axes for the latest year for which we have data (2009). I then added the 73 deaths (in red), assuming for simplicity that they all fell on 16 year-olds (since it was a youth camp).  While clearly not true, this allows us to compare the scale of this mass murder with the pace of death in Norway as a whole.

It is plain to see that, beyond the clear impact such an event has on the families directly effected, this senseless act has a substantial effect on the aggregate pattern of mortality for the entire country of Norway.

# Models of Human Population Growth

The logistic equation is a model of population growth where the size of the population exerts negative feedback on its growth rate. As population size increases, the rate of increase declines, leading eventually to an equilibrium population size known as the carrying capacity.  The time course of this model is the familiar S-shaped growth that is generally associated with resource limitation. This model has only two parameters: $r$ is the intrinsic growth rate and $K$ is the carrying capacity. The rate of increase in the population declines as a linear function of population size.  In symbols:

When the population size is very small (i.e., when $N$ is close to zero), the term in the parentheses is approximately one and population growth is approximately exponential.  When population size is close to the carrying capacity (i.e., $N \approx K$), the term in parentheses approaches zero, and population growth ceases. It is straightforward to integrate this equation by partial fractions and show that resulting solution is indeed an S-shaped, or sigmoid, curve.

Raymond Pearl was a luminary in human biology.  A professor at Johns Hopkins University, a founder of the Society for Human Biology and the International Union for the Scientific Study of Population (IUSSP), Pearl also re-discovered the logistic growth model (which was originally developed by the great Belgian mathematician Pierre François Verhulst).  In the logistic model, Pearl believed he had found a universal law of biological growth at its various levels of organization.  In his book, The Biology of Population Growth, Pearl wrote:

… human populations grow according to the same law as do the experimental populations of lower organisms, and in turn as do individual plants and animals in body size. This is demonstrated in two ways: first by showing as was done in my former book “Studies in Human Biology,” that in a great variety of countries all of the recorded census history which exists is accurately described by the same general mathematical equation as that which describes the growth of experimental populations; second, by bringing forward in the present book the case of a human population-the indigenous native population of Algeria-which has in the 75 years of its recorded census history practically completed a single cycle of growth along the logistic curve.

In addition to Algeria, Pearl fit the logistic model to the population of the United States from 1790-1930. The fit he produced was uncanny and he confidently predicted that the US population would level out at 198 million, since this was the best-fit value of $K$ in his analysis.  I have plotted the US population size (from the decennial census) as black points below, with Pearl’s fitted curve in grey. We can see that the curve fits incredibly well for the period 1790-1930 (the span to which he fit the data), but the difference between prediction and empirical reality becomes increasingly large after 1950 (yep, that would be thanks to the Baby Boom).

Why does the logistic model fail so spectacularly in this case (and many others)?

The logistic model is phenomenological, rather than mechanistic. A phenomenological model is a mathematical convenience that we use to describe some empirical observations, but has no foundations in mechanisms or first principles. Such models can be useful when theory is lacking to explain some phenomenon or when the mathematics that would be required to model the mechanisms is too complicated. You can make a prediction from a phenomenological model, but I wouldn’t bet the farm on that prediction. In the absence of an actual understanding of the mechanisms producing the population change, the predictions can go horribly wrong, as we see in the case of Raymond Pearl’s fit.

Specifically, the logistic model  fails to consider mechanisms of population regulation. When density increases, what is affected?  Birth rates? Death rates? The $r$ parameter in the logistic model is simply the difference in the gross birth and death rates when there are no conspecifics present.  In general, when the birth rate exceeds the death rate, a population increases.  The linear decrease in $r$ with increasing population size presumably can come about by either the birth rate decreasing or the death rate increasing.  The logistic model is indifferent to the specific cause of slowing.  It just stops increasing when $N=K$. Is it possible that, in real populations, increasing the death rate and decreasing the birth rate might have qualitatively different effects on population growth? We’ll see.

This probably goes without saying, but there is no capacity for the positive feedbacks with population size. In her classic work, The Conditions of Agricultural Growth, Danish economist Esther Boserup noted that population growth often stimulates innovation. Population pressure might cause an agricultural group that has run out of land to intensify cultivation by improving the land or multi-cropping, thereby facilitating even greater population growth.  Various authors, including Ken Wachter and Ron Lee (both at Berkeley) and Jim Wood at Penn State have noted that real populations probably incorporate both Malthusian (i.e., conditions leading to increased mortality, decreased fertility, and general misery with increased population size) and Boserupian phases in their dynamics.  Wood coined the term “MaB Ratchet” (MaB = Malthus and Boserup) which describes the following dynamic: Malthusian pressure incites  Boserupian innovation, relaxing negative feedback and allowing further population growth.  While a population is undergoing a Boserupian expansion, quality of life improves. Alas, given enough time, the population will always return to “the same level of marginal immiseration.” (Wood 1998: 114). Such complex regimes of positive and negative population feedback are not a possibility .

One final problem with the logistic model is that there is no structure — all individuals are identical in terms of their effect on and contribution to population growth. Human vital rates vary predictably – and substantially – by age, sex, geographic region, urban vs. rural residence, etc. And then there’s the issue of unequal resource distribution.  All individuals in a population are hardly equal in their consumption (or production) and so we should hardly expect each to exert an identical force on population growth.

So are there better alternative models for human population growth that incorporate the sensible idea that as populations push the limits of their resource base, growth should slow down and eventually cease? There is now.  My Stanford colleague and collaborator in various endeavors, Shripad Tuljapurkar, has a series of papers in which he and his students develop mechanistic population models for agricultural populations that specifically link age-specific vital rates (i.e., survivorship, fertility), agricultural production and labor, and specific (age-specific) metabolic needs for individuals engaged in heavy physical labor.  The models start with an optimal energy supply for survival and reproduction.  As food gets more scarce, mortality increases and fertility decreases.  The model has an equilibrium where birth and death rates balance. A key feature of the model is the idea of the food ratio, which is the number of calories available to consume in a given year relative to the number of calories needed to maximize survival and fertility. The food ratio tells us how hungry the population is. In the first of a series of three papers, Lee and Tuljapurkar (2008) develop this model and show how changes in mortality, fertility, and agricultural productivity actually all have distinct effects on the population growth rate, equilibrium, and how hungry people are at equilibrium. Analysis of their model yielded the following results:

• Increasing agricultural productivity or the amount of time spent working on agricultural production increases the food ratio, while keeping the population growth rate largely unchanged
• Increasing baseline survival increases the food ratio but decreases the population growth rate
• Decreasing fertility only decreases the growth rate – the food ratio remains unchanged

So, we see that it is possible that increasing the death rate and decreasing the birth rate might have qualitatively different effects on population growth. In fact, it seems quite likely, given Lee & Tulja’s model.

We don’t, as yet, have the kind of test that we gave Raymond Pearl’s application of the logistic model to US population size. It would be very nice if we could use the Lee-Tulja model to make a prediction about the future dynamics of some population (and its distribution of hunger) and challenge this prediction with data not used for fitting the model in the first place. This said, I think that theoretical exercise alone is enough to demonstrate the importance of moving beyond phenomenological population models whenever possible. We are unlikely to make accurate predictions or understand the response of population to environmental and social changes in the absence of mechanistic models.

References

Lee, C. T., and S. Tuljapurkar. 2008. Population and prehistory I: Food-dependent population growth in constant environments. Theoretical Population Biology. 73:473–482.

Wood, J. W. 1998. A theory of preindustrial population dynamics: Demography, economy, and well-being in Malthusian systems. Current Anthropology. 39 (1):99-135.

# Update on Stanford Workshop on Migration and Adaptation

Since my last update, we have added another faculty member to the workshop on Migration and Adaptation. Loren Landau, the Director of the African Centre for Migration and Society (ACMS) (formerly Forced Migration Studies Programme, FMSP) at Wits University in Johannesburg, South Africa will be joining us to discuss conceptual issues in understanding African migration as well as research opportunities through ACMS. This means that we have the following confirmed speakers:

• James Holland Jones, Department of Anthropology and Woods Institute for the Environment, Stanford University (organizer): Formal Models of Migration; Population Projection
• Shripad Tuljapurkar, Department of Biology, Stanford University (organizer): Stochastic Forecasting
• Eric Lambin, Environmental and Earth Systems Science and Woods Institute for the Environment, Stanford University: Pixels to People Approaches to Studying Migration
• David Lobell, Environmental and Earth Systems Science and Woods Institute for the Environment, Stanford University: Global Climate Change and Food Insecurity
• William H. Durham, Department of Anthropology and Woods Institute for the Environment, Stanford University: Smallholder Responses to Risk and Uncertainty
• Ronald Rindfuss, Carolina Population Center, University of North Carolina and The East-West Center: Population and Environment; Microsimulation
• Amber Wutich, School of Human Evolution and Social Change, Arizona State University, Water Insecurity
• Lori Hunter, Department of Sociology, University of Colorado: Migration and Health
• David Lopez-Carr, Department of Geography, University of California Santa Barbara: Migration and Fertility on the Forest Frontier
• Loren Landau, African Centre for Migration Studies, Witwatersrand, Conceptual and Empirical Issues in African Migration

This is a great line-up and I’m very excited about this (and there are still a couple invitations pending based on complicated field schedules). We will hold the workshop at the IRiSS facility at 30 Alta Rd., bordering the main campus. This is a lovely spot for a workshop.

Details on applying for the workshop are contained here. We will pay for approved travel expenses of accepted students, post-docs, and junior faculty associated with NICHD-funded population centers.

# Stanford Migration and Adaptation Workshop

Information on our NICHD-funded April formal demography workshop on migration and adaptation is now posted on the website Stanford Center for Population Research (SCPR, pronounced “scooper”).  SCPR is itself hosted by Stanford’s Institute for Research in the Social Sciences (IRiSS), which is also the umbrella organization for the Methods of Analysis Program in the Social Sciences (MAPSS), a program that I currently direct. We will be having this little shindig at the new IRiSS facility on Alta Road, a lovely location on the hill behind Stanford’s main campus, quite near the Center for Advanced Study in the Behavioral Sciences. All of these workshops have been terrific, but I am particularly excited about this one because it brings together so many of the threads of work going on right here at Stanford on human ecology, demography, and the biophysical environment.  Much of this work is facilitated by the Woods Institute for the Environment, where I and a number of the other Stanford-based speakers sit.

As a quick teaser of the kind of work that we will discuss, I want to draw people’s attention to two papers by Stanford faculty participating in the workshop that are just out this week.  Eric Lambin has a paper (which also happens to be his inaugural paper in PNAS as a member of the NAS) on the interactions between globalization, land use, and future land scarcity. I saw a talk on this last week and it was terrific. Lambin and co-author Patrick Meyfroidt argue that there are four socio-economic mechanisms (displacement, rebound, cascade, and remittance effects) that are amplified by by the process of economic globalization and that can accelerate land conversion. David Lobell has a new paper out today in Nature Climate Change in which he and his co-authors capitalize on a treasure-trove of historical agricultural trials in Africa to measure the impact of warming on maize production.  They find that approximately 65% of areas will experience a decline in productivity with a one-degree rise in global temperature if rain patterns are optimal.  If rain is sub-optimal, as is likely to be the case, then every site would experience reduced productivity.  This supports David’s contention that the effects on agricultural productivity of temperature increase from global climate change can not be understood except in the context of changes in rainfall as well.

Potential students who are interested in studying these issues at Stanford have a number of options.  If anthropology is your thing, we have a Ph.D. focus area in Ecology and Environment within the Department of Anthropology.  Bill Durham, Lisa Curran, Rebecca Bird, Douglas Bird, and I all teach in this area. Another option, for the more interdisciplinarily inclined, is E-IPER.  This is a topic I will have to take up in more detail in a later post since I actually have to do some work organizing our workshop now!

# New Formal Demography Workshop: Migration and Adaptation

We will be having another of our occasional Stanford Workshops in Formal Demography this April 28th-30th. The theme this time will be “Migration and Adaptation,” and we have a terrific lineup of speakers coming. As in the past, the workshop is funded by NICHD and receives substantial suport from the Stanford Institute for Research in the Social Sciences (IRiSS). What is somewhat different this time is that we actually have our own center now, The Stanford Center for Population Research (SCPR). Here’s the basic idea for the workshop:

Mobility is a common form of human adaptation to social or environmental risks.  Forms of human mobility vary with regard to permanency and spatial scale.  For example, foragers or pastoralists may move seasonally in response to resource scarcity and opportunity throughout a more or less stable greater home range. Smallholders and agrarian peasants might be displaced on a more permanent basis as a result of conflict or extreme resource scarcity, migrating internally to cities or other relatively nearby localities perceived to be less risky.  International economic migrants may travel long distances on a more or less permanent basis in search of economic opportunity abroad.

Global climate change is predicted to increase migration rates substantially by the middle of the 21st century.  This increase in migration is likely to result from multiple, interacting causal mechanisms including an increase in adverse weather events (e.g., droughts, floods), an increase in resource-related conflicts, or declining viability of local environments arising from various forms of land-use/land-cover change.  These increases will add to the already substantial movement of human population from rural to urban areas, in response to internal social displacement, and from other economic migration.

Understanding human migration requires the input from scientists from a wide range of disciplines. We are particularly interested in approaches that combine the formalism of demography, on-the-ground social research, and remotely-sensed information of the biophysical environment, the so-called “pixels to people” approach.

In this workshop, we will bring together demographers, anthropologists, economists, and geographers to develop a methodological toolkit for understanding migration as an adaptation to risk.  The specific aim of the workshop is to promote knowledge of methods and perspectives from different disciplines, disseminate information about the growing wealth of demographic data on the biophysical environment and human migration, and to foster collaborative and interdisciplinary work. The format will consist of lectures by invited researchers to an audience of other researchers, selected graduate students, and junior faculty. The three-day workshop will have approximately ten faculty and 20 students, whose travel, lodging, and meals will be covered.  The format provides substantial time for discussion. The workshop will be held at the Institute for Research in the Social Sciences (IRiSS), Stanford 28-30 April 2011.

Confirmed speakers include:

• James Holland Jones, Department of Anthropology and Woods Institute for the Environment, Stanford University (organizer): Formal Models;
Population Projection
• Shripad Tuljapurkar, Department of Biology, Stanford University (organizer): Stochastic Forecasting
• Eric Lambin, Environmental and Earth Systems Science and Woods Institute for the Environment, Stanford University: Pixels to People
• David Lobell, Environmental and Earth Systems Science and Woods Institute for the Environment, Stanford University: Global Climate Change and Food Insecurity
• William H. Durham, Department of Anthropology and Woods Institute for the Environment, Stanford University: Smallholder Responses to Risk and Uncertainty
• Ronald Rindfuss, Carolina Population Center, University of North Carolina and The East-West Center: Population and Environment; Microsimulation
• Amber Wutich, School of Human Evolution and Social Change, Arizona State University, Water Insecurity
• Lori Hunter, Department of Sociology, University of Colorado: Migration and Health
• David Lopez-Carr, Department of Geography, University of California Santa Barbara: Migration and Fertility on the Forest Frontier

A (rather large) printable flier for the workshop can be found here.  It includes information on how to apply.  Hopefully, we will soon have an all official-like webpage through IRiSS as well, which I will point to when it goes live.

# Typologies of Critique

Greg Downey over at Neuroanthropology has a fantastic post on the most recent flare-up of the anthropology-is-it-science-or-is-it-literature wars.  There is an awful lot of wise prose to be found in this post (and some disturbing information about the labor action at Macquarie University), but the thing that tickled me more than anything was his typology of criticism.  I love these sort of typologies as intellectual play-things and have lots of my own (that probably any of my grad students or post-docs would be happy to tell you about over a beer some time).  Greg’s typology of stupid criticisms:

1. Critique for incompleteness, “where the critic points out something tangentially related to the author’s topic or argument and then asserts that this missing element is THE most important consideration, so the argument is hopelessly, fatally flawed.”
2. Critique from creative misunderstanding,  where “the critic latches onto a single term or phrase, intentionally misunderstands it or comes up with an interpretation that could only occur to the most hostile, cranky, ill-disposed reader, and then projects the misunderstanding onto a straw version of the presenter.”
3. Critique from guilt by association, where “the critic sees some sort of link between what the author writes and some deeply loathed intellectual villain, draws some sort of tenuous connection, and then just substitutes the villain’s ideas for the argument, essay or analysis in question.

Awesome.  I will need to get to work thinking of other willfully bone-headed modes of critique. I will think of this post every time I review a paper or grant proposal from now on…

A similar typology that I came up with attending demography talks, first at the Harvard Center for Population and Development and later at the Population Association of America meetings, deals with discussants. The phenomenon of the discussant is still something I find a bit bizarre, as I find having a discussant adds absolutely nothing to the intellectual merit of a talk or panel in the vast majority of cases.  It also chafes a bit at my science-as-meritocracy ethos (why exactly do I need to have the talk I just sat through explained to me by some guy in a suit?).

The different flavors of discussant that I have identified include:

1. The redundant discussant: “Author #1 said this.  Author #2 said this other thing. Author #3 said something else…” Snooze.
2. The bitchy discussant: “The author claimed to use a Mann-Whitney U when he really used Kendall’s tau. It’s not clear why they used Coale-Demeny West 5 when a UN life table would have clearly been preferable. The assumptions of the stable model are not exactly met. And you didn’t cite me!”
3. The pandering discussant: “In brief, this paper will change the course of human affairs.  I feel an extraordinary privilege just being in the same room as this author on this day. Hosanna.”
4. The orthogonal discussant: “Well, we just heard a number of very interesting talks, now let me tell you about my work…”

Very rarely (so much so that it doesn’t really merit a category), a discussant does what he or she is supposed to do: synthesize and provide novel insight about how the papers in a session relate to each other. I have personally experienced all of the forms of discussant except the panderer (at least in its fullest form).  I did witness a friend receive the panderer’s treatment much to her embarrassment and, frankly, that of everyone in the room. I think it’s fair to say that everyone thought she had indeed given a very fine paper, though had not quite changed history. I think I actually prefer the orthogonal discussant to all the others because that way you get to see another talk rather than just hearing a bunch of [redundancy, bitchiness, pandering], which is not the best use of time at academic meetings. As anyone who has ever been to an academic meeting knows the best use of one’s time is, as Greg notes in his post “drink[ing] heavily with my friends, sneak[ing] off repeatedly for Mexican food, and spend[ing] most daylight hours in the publishers’ expo.” Honestly, this is one of the reasons why I’ve decided I actually like the AAAs. True, there is generally very little in the program that actually interests me.  However, there are lots of people who interest me who attend.  I can hang out and have long lunches and long dinners and even longer sessions drinking and talking anthropology with cool people and not feel the slightest bit of guilt at missing all those sessions! What could be better?

# 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 Key to the Survival of the Human Species?

Perhaps it’s just me being a bit groggy from jet-lag, but I just read one of the most bizarre things I think I have ever seen in the New York Times.  There is a generally very interesting article by Sarah Kershaw on so-called “cougars,” older women who have sexual relationships with younger men. It was the first I had ever heard the term – shows what I know. As the article concludes, Kershaw makes the following statement:

The paradox, of course, is that the older-woman relationship makes perfect sense when it comes to life expectancy, with women outliving men by an average of five years. But with men’s fertility far outlasting women’s, biology makes the case for the older-man scenario, and recent research has even suggested that older men having children with younger women is a key to the survival of the human species.

Say what?! Survival of the species??

It’s a pretty strange statement that strangely lacks attribution, particularly given how well referenced all the other scholarly work discussed in the article is.  I wonder if it isn’t a vague allusion to the work of my colleague Shripad Tuljapurkar who has shown that systematic differences in mean age of childbearing would mitigate the so-called “wall of death” predicted by W.D. Hamilton’s famous paper on the evolution of senescence.