I’m continuing to post implementation notes to accompany A Manual for Cultural Analysis, which I published last year together with two of my anthropologist colleagues at RAND. In my last installment, I provided links and advice for implementing CCA/PCA.

In this blog post I will address how to implement the network modeling method that is discussed in some detail in Chapter 4 of the manual. The central question is this: how do we tell which set of social connections are most important to the transmission of a cultural trait? Note: if a trait doesn’t transmit on some kind of social connection, then it can’t be socially learned, and so by definition it isn’t culture!

OK, but people are connected by ties of friendship, marriage, coworkership, twitter, etc. So how do we decide which of the various ties are most relevant to a diffusing cultural trait? We cover this question in a lot of detail in the manual. I covered it with even more detailed simulations in my paper with my student Rouslan Karimov. We never got a supplement published for that paper, so I’m posting here the code you need to run the most important analysis from the paper – the method that works. First, make sure R is installed. Download the files that are part of this blog post. Extract (unzip) the July12.7z file - I had to zip it to post it here. After it is extracted you should have a file July12.RData. Then if you double click the July12.RData file it should start up R and will already have the simulated data objects you need in the workspace. Type ls() in the R command line to see what is in the workspace. If double clicking doesn’t work, then start R and type load(“diffsimcont.RData”). Make sure you have used the change directory feature in the dropdown to move your active directory to wherever you put July12.RData.

Think of this like a cooking show where some intermediate step is already baked. To create the things in the R workspace you just loaded you would need to simulate networks, simulate trees, then simulate characters diffusing/evolving on them, etc. I’m happy to provide the simulation code to anyone interested. Just email me. The focus of this blog post, however, is not about building simulations but learning to apply dyadic regression with random effects to network/tree datasets.

OK, so then start running my code file called CodeRandEffectsNetworkSims.txt. Simply copying and pasting one line at a time into the R command line is a good way to learn how a piece of R code works. When you get to the loop you would have to paste in the whole loop for it to run; however, I recommend you set i equal to something, like i=1, and then walk through the loop one line at a time as well. That will enable you to inspect what is happening in the loop. One important part is this bit where it defines what you need to run the random effects regression that controls for the repeated identities of the individuals. The individuals are being repeated across each of their network relationships:

names.vector<-1:nrow(sn.adj1)
 
rows<-matrix(rep(names.vector,ncol(sn.adj1)),ncol=ncol(sn.adj1))
cols<-matrix(rep(names.vector,ncol(sn.adj1)),ncol=ncol(sn.adj1),byrow=T)
 
outcome.vector<-as.vector(daisy(as.data.frame(netsims[,,i]),metric="euclidean"))
 
temp.data<-data.frame(outcome.vector,sn.adj1[lower.tri(sn.adj1)],sn.adj2[lower.tri(sn.adj2)],rows[lower.tri(rows)],cols[lower.tri(cols)])
 
colnames(temp.data)<-c("outcome","sn.adj1","sn.adj2","rows","cols")
 
net.mod<-lmer(outcome~sn.adj1+sn.adj2+(1|rows)+(1|cols),data=temp.data)
 
Within the lmer function call the terms (1|rows) and (1|cols) are what is specifying the random effects – which are just the identity of each row and column for each dyadic datapoint. I like lmer in the lme4 package for random effects models (aka mixed hierarchical models) in R, but another option is gls in the nlme package. There are more options besides these as well, including in other statistical packages like SAS, which has some very good random effects modeling routines. I’m not going to discuss fully here why this is the best approach to determining which tree or network most governs the cultural diffusion process for a trait – read the manual or Karimov and Matthews 2017 if you want the answer to that.

In terms of getting to know how lmer works, be sure to run some of the example code provided in the lmer help file. From R you can get to the help for any function by typing ?function.name in the R command line. For example, typing ?lmer will get you the lmer help file.

I will say that I think the simulations in Karimov and Matthews 2017 are more comprehensive than anything anyone else has ever done on this issue. We show that the dyadic regression with random effects is a definitive solution. It works for multiple networks, or networks combined with trees. I’m sure one could create evil combinations of unmeasured confounding and measurement error where the method will fail, but in principle it works across all relevant conditions while I show the other commonly used methods like lnam (sna R package) and MRQAP (aka Mantel test) do not work across all relevant conditions. If you can fit a random effects regression model then you can fit the method I’m recommending based on the simulations I’ve done. You don’t need any particular software package, you don’t need my code, just regress the trait distances and network ties, include random effects for node IDs, and you’re done. I shouldn’t hear anymore at conferences about how we can’t distinguish treelike inheritance from network diffusion, or determine which networks are important. Measure whatever networks or trees you think might matter, put them in the dyadic regression with random effects, and you’re done.

Since my colleagues and  I published A Manual for Cultural Analysis, some people have asked for R code examples of all the things we describe. That’s a fair critique of the manual as we originally published it, although I’ll note that most of the papers and books we referenced already provide implementations or point to them. Regardless, here on my blog I will write a set of posts that will point to everything you need to implement what’s in the manual.

The first part of the manual focuses on using Cultural Consensus Analysis (CCA) and Principal Component Analysis (PCA) as a first pass at understanding cultural data. Read the manual if you want to understand why this is such an appropriate first pass.

PCA has a large associated literature that I can’t overview here. For implementation, I think the best option is the prcomp function in the ‘stats’ R package that should come with any basic R install. The other option is princomp. I prefer prcomp because it uses SVD rather than eigenvalue decomposition, which is supposed to be slightly more accurate numerically. Also, this implementation allows for data structures that have more variables than datapoints, which is a common occurrence in cultural data.

CCA is a technique from cognitive anthropology, which is a subfield of cultural anthropology. Basically it works by performing PCA on the transpose of the usual individual by variable matrix, thus you are performing PCA on a variable by individual matrix. This procedure results in loadings for the individuals on the components, and scores for the variables, again the reverse of the usual PCA procedure. Exactly why you might do this theoretically and when you might use CCA vs PCA is answered in the manual.
Skipping to implementation, the simplest way to do CCA is to simply use prcomp on the transpose of your data. Like this: prcomp(t(your.data)). The t() is the transpose function in R.

There also are some packages specifically for PCA that can allow you to fit more subtle forms of it, and allow to you ensure the mathematics are being done in more precisely the same way as in prior important articles by folks like Batchelder, Romney, and Handwerker among others (check the manual for refs). One R package option is AnthroTools for R. AnthroTools will implement the classic version of CCA, and it provides some neat data manipulation tools specific to common types of cultural anthropology data, such as free-lists. Another option with more advanced features is CCTpack, which implements both the classic CCA but also more recently developed modifications, such as contexts where there is more than one underlying cultural stance.

That should cover the options, at least in R, for implementing PCA and CCA as we described in A Manual for Cultural Analysis. Note that all R functions have example code that works down at the bottom of the help pages for them. I’ve learned a lot just by running those little examples and comparing the input data they used to the outputs generated by the functions.

Stayed tuned to my personal blog for the next days and weeks because I am going to publish similar posts for the network analysis and phylogenetic analysis chapters. Feel free to leave comments here with questions or email me.

I recently published an article in an applied journal that might be unlikely for anthropology folks to find, so I thought I would add some commentary on it here. The article uses agent-based simulations to assess the statistical performance of several network regression methods. Network regression is a primary way to assess whether cultural diffusion is occurring, and over which social ties the diffusion happens. The social ties are represented by different networks. Although the article focuses on applications to networks of malicious actors, the methods are applicable generally to most human social networks.

The methods I investigated essentially are some of the currently best approaches for implementing tests to determining which of several transmission pathways most govern the spread of a trait. This type of question is core to cultural evolutionary analysis, and in my view core to the original mandate of anthropology. A good example of a running anthropological discussion on this matter would be the analysis of Welsch et al. 1992 and the critiques, counter critiques, and reanalyses that followed. In my view, this entire exchange is some of the best of anthropology.

Of course, post-Boas cultural anthropology tends not to view articles like Welsch et al. 1992 as of foundational interest. That’s because much of American cultural anthropology left behind methods that systematically compared across different sites or cultures in favor of particularist accounts conducted though increasingly humanistic modes of research. Nothing wrong with the latter by the way – I regard it as a completely valid intellectual activity. It’s just not the entirety of what anthropology was founded to do, and the result has left a significant portion of scientific research on culture without a disciplinary home.

Most biologically inclined anthropologists, meanwhile, in recent memory had vigorously adopted the behavioral ecology paradigm (Grafen’s phenotypic gambit) – which is essentially utility maximization theory from microeconomics with reproductive success or its proxies in replacement of money. Studying cultural evolution is difficult to fit into this framework because classical behavioral ecology is intentionally heuristically teleological rather than explicit and mechanical. Behavioral ecologists don’t necessarily think people are intentionally trying to maximize fitness outcomes, although some might, but they think at least that the mechanical operations of the mind result in a being that behaves as if it is maximizing fitness. In the classic approach no attempt is made to understand the causal mechanisms. That’s all fine, it just only gets you so far. Wouldn’t you rather actually know about the mechanisms themselves?

The perhaps unexpected irony is that folks interested in cultural evolutionary models, by articulating proximate mechanisms, arguably are the ones being more reductionist than those sticking within the classical behavioral ecology paradigm. It’s ironic because cultural research has so often been assumed to have something to do with non-reductionist notions. Thornhill and Palmer in their controversial book at one point say people prefer cultural explanations because culture is consistent with the idea of free will – but cultural explanations offered by cultural evolutionists are just as causal as explanations offered by behavioral ecologists. Both explain individuals’ behavior as a mechanistic result of prior events. In fact, I’d argue cultural evolutionists are being more causal in that they usually seek to specify causal mechanisms start to finish, ultimately and proximately, while behavioral ecology in its classical approach leaves proximate mechanisms unspecified. Fincher and Thornhill’s well-known articles on infectious disease stress and cultural traits are good examples – we are left at the end of the papers with only a functional relationship (heuristic teleology) and don’t know if that is mediated by cultural inheritance, individual learning, an innate reaction norm, or genetic variation.

The second irony is that “behavioral economics,” that moves beyond utility maximization and into proximate causes, has become mainstream or nearly so in microeconomics, and microeconomics essentially is where behavioral ecology first coopted its theory.

Which brings me back to social network analysis applied to cultural diffusion. Cultural diffusion as a theorized process really sits between proximate and ultimate causation  in the traditional sense because it is 1) a proximate factor in individual’s personal development, 2) has an epidemiological and phylogenetic character to its own as cultural history, and 3) ultimately can’t fully escape biological selection because, at least among any longstanding cultural variants, natural selection will favor directly cultural variants that promote fitness or genes that bias people to prefer such variants. The other thing I like about studying cultural diffusion, inheritance, and related processes is the hypotheses are eminently refutable. I think culture often is important, but whether it is important or not to a particular case is an empirical matter. Folks like me doing this, I think, often find cases in which culture is not the most important process involved. In some of my own research, I’ve proposed that genetic variation accounts for some cross-society differences in personality traits that are usually interpreted as entirely socially learned (Matthews and Butler 2011). My recent paper tries to advance the empiricist and refutable tradition of cultural research by determining which of the currently available methods perform best at discerning cultural diffusion processes.

kFor this blog I want to point to a great perspective piece by some colleagues that also illustrates a peculiar tendency of evolutionary biologists. Evan Maclean and Brian Hare recently wrote a piece for Science about work by Miho Nagasawa and others showing that when we look at dogs it actually taps into the neuro-chemical (oxytocin) pathways used in human bonding.  The same does not appear to happen when humans gaze on wolves, which suggests the effect is a specific product of some kind of evolutionary selection with humans and dogs, rather than an incidental byproduct.  Here and here are the links to the articles.

The research is all very cool. What I wanted to comment on though from a cultural analysis perspective is the way Evan and Brian phrase their title as ‘Dogs Hijack the Human Bonding Pathway’.  What’s with the hijacking? Are dogs up to some nefarious takeover plot at the expense of us humans? Certainly that is what is implied by the natural English reading of the word ‘hijack.’ This reading is in fact emphasized, in my view, because the title is paired with an image of a human looking at a Yorkshire terrier. Certainly that seems to imply the reading that dogs are using us at our expense to their benefit – more so than if the image had paired the human with a more apparently useful dog, like maybe a coonhound or a huskie. Would the title even have made intuitive sense with a black and tan coonhound in the picture?

Of course it can’t be that dogs hijack human bonding in an immediate proximate sense, because hanging out with dogs just feels good. In evolutionary biology we often distinguish between using a word in a proximate or ultimate sense. The difference is timescale. Proximate sense means the word applies to the time scale we experience things in. So, we live, die, mate, and have all sorts of other ecological interactions. Ultimate sense means the word applies to the evolutionary time scale where all the aggregate outcomes of those proximate things add up to result in genetic drift, natural selection, etc. A classic example is you could say a person’s skin tanning in response to sun exposure is an adaptation in a physiological proximate sense, or that its ability to tan is an adaptation in an evolutionary sense (it is an aggregate product of generations of selection for tanning – I understand some other humans have this ability).

So, it can’t be that dogs hijack us in a proximate sense, because that would be like saying US Airways hijacks me each time I fly to DC. A professional pilot took me there quickly, with a cold drink in my hand, and I was actually reasonably comfortable.

So I think the only sense in which hijack could have been meant then was the ultimate sense. That’s where to me the title reflects this tendency in evolutionary biology to always see the glass as morally half empty.  Most evolutionary biologists will admit there is a lot of cooperation in the natural world, but they tend to emphasize selfishness at all turns. Some people won’t like me saying that, and certainly some particular evolutionists aren’t like this, but I do think it’s the tendency. And yes, having spent a year or so watching monkeys steal food from each other, get eaten by predators, etc. I’ll agree there is a lot of rank selfishness in the natural world.

However, we shouldn’t let all that become a knee-jerk reaction of negativity, and especially about dogs! Now, no one has the data needed to test whether living with dogs has been a net fitness benefit or cost to humans, but this is a blog, so I’m going to make my anecdotal qualitative case that dogs are clearly a net fitness positive. And if it’s a net fitness positive, then dogs haven’t hijacked us; rather, dog-human coevolution is a case of evolutionary mutualism.

Dogs have done a lot for us over the past 20 thousand or so years since we domesticated them. First, they have helped us hunt. We still use them that way today, but I reckon it was even more fitness beneficial to have a good dog at your side when hunting without firearms. Dogs have served as draft animals and protected our children from predators. Terriers also had an important function of exterminating disease-bearing rodents. It seems to me that one less person getting the plague in your family probably more than made up in Darwinian fitness for whatever table scraps and occasional grooming that it cost you to raise the terrier.

I’m willing to concede that perhaps in today’s modern environment dogs may well be a net fitness loss, but that’s not relevant to an evolutionary statement about hijacking. And by modern I mean really modern, because until just recently dogs still performed the critical evolutionary functions I just described. And now for the anecdote. It so happens that my mother-in-law grew up in rural India. As a teenage girl she had a German Shepherd Dog as a pet. One day her father (my wife’s grandfather) was taking a nap in the heat of the day, and a cobra came over the threshold into his bedroom. I don’t recall from the telling of the story who saw the cobra, but someone did and shouted at Dad I guess and he woke up and stayed on the bed while the cobra was still on the floor between the bed and the door. So, as all the humans were standing about, basically trying to figure out how to get Dad out of the room with the cobra on the floor, the German Shepherd rushed in, and before anyone else had moved, the dog rushed the snake and snapped it in two with it’s jaws. Amazingly, the dog didn’t get bit by the snake and survived the encounter. Needless to say this dog was subsequently of legendary status in their village.

Look folks, that’s awesome. I can’t properly quantify a selection coefficient from that, but clearly saving a Dad with teenage children from a cobra earns Rover a whole lot of kibble in the Darwinian end-game. So, maybe next time the magazine Science will spare the knee-jerk negativity and go with a title that reflects what has likely been a long evolutionary history of mutually beneficial cooperation. I’d suggest something like, ‘awesome super-canines save their owners through the human bonding pathway’, and no I don’t think super-hero capes in the accompanying picture would be out of line.

I’ve worked with medium, large, and occasionally big data.  What are big data?  One line to demarcate it would be data sets so large in size that they cannot be processed in reasonable time by any computing hardware.  Datasets like that force the analyst to take non-traditional approaches to process them. 

But most of the hype about big data isn’t about such analytic distinctions.  The excitement over big data is that we now have the ability to gather truly large datasets on phenomenon that previously were measurable only in small datasets.  So, it used to be that you had to survey people to get their opinions about X, Y, or Z, but now we can gather vast amounts of data on people’s opinions from monitoring the twitterverse or blogosphere.

Gathering large data where previously we only had small data was what excited people about Google’s flu prediction system that was based on the search terms people were using.  A recent paper, however, shows convincingly that Google flu predictions seriously overestimated the actual incidence of flu as tracked by the records related to physician office visits for influenza like illness, which is the exact measure Google was trying to predict from search terms.

This commentary by Lazer et al. in the journal Science includes many important reminders of how using big data for statistics doesn’t much change the rules of good statistical practice and inference.  I often tell my colleagues the same thing.  I consider myself one of the relatively few people in a personal position to judge this, since I have conducted studies on data sets as small as six capuchin monkeys and as large as millions of lines of healthcare claims. 

Sometimes big data are a real advantage.  In particular;

1) Large datasets make testing model fit easier,

2) You have more data to burn through corrections for multiple testing if you are searching for a model with little theory to guide you,

3) Big data are useful when you are predicting something very, very rare, like most cancers or terrorism. 

Any particular usefulness to big data pretty much stops there though.  Big data don’t much help you to discover important and general features of social behavior, for example.  Folks, if you can’t find an effect of one variable on another in several hundred or a thousand data points – then probably it isn’t an important or general effect. 

There is something more going on with the Google flu flub, however, than just missteps in statistical best practice.  What the Googlers, Twitterers, and other such conspecifics are used to measuring--and they are great at it--are systems where the outcome of interest is more or less the thing being measured itself. 

If you want to know what is trending about your company on Twitter, then that is by definition what people are tweeting about.  If you want to direct people to the most popular pages from a given search term (what Google excels at) then you by definition want to measure where people click after using the same search term. 

But doing those things are doing math and not doing statistics.  Statistics is an offshoot of mathematics that is specific to the task of making predictions about things you haven’t measured from observations of other things you did measure.  This axiom applies even to basic statistical inference problems such as finding the average height of a population, which doesn’t proceed by measuring everyone in the population.  If you just measure everyone then you have counted, you have used some math by calculating the average, but you didn’t do any statistics.  Statistically inferring the mean height of a population would proceed by measuring only a smaller sample of that population, and from that deriving an inference of what the average of the whole population likely is, and some measure of your confidence in that prediction.  If you have measured everyone, then you have an observation of the population average, not a prediction of it, and you don’t need a measure of confidence.

That’s why estimating the incidence of flu is from search terms is not like Google’s bread and butter work.  The incidence of flu is an out-of-sample prediction from search terms because the flu’s incidence is not itself determined just from what people think and therefore search about it.  Surely flu incidence is partly a social construct.  For example, a group struck by apprehension over the flu will avoid social contact and thereby slow the flu’s spread.  But the flu also has other causes outside our thinking and searching the internet about it.  Ambient temperature and humidity affect flu transmission, and mutation of the genetic material of the flu virus is a function of properties mostly not constructed by our own sociality. 

My contention is the day-to-day work of Google and many tech companies that use big data is not out-of-sample prediction in this way.  Instead, they are able to directly measure the thing of interest to their advertisers because the latter are intrinsically interested in the behavior of people within the self-creating system that is the internet. 

I think when Google set out to predict the incidence of flu from search terms, they may not have realized they were stepping outside the realm of measurement of a self-creating system (like internet searches) and stepping into the realm of predicting unobserved phenomenon from measurements of another phenomenon. 

This realm is that of statistics, and it is well trod by practicing scientists from many fields.  Yes, these travelers of the statistical realm usually have used small data.  Some of them travel accompanied by a cartload of models and information-theoretic Bayesian livestock.  Others are more modest practitioners but highly adept with a particular tried and true beast of burden, such as linear regression or K-means clustering. 

Regardless, Google and others may do well to consult some of these conventional statistical vagabonds the next time they venture into analytics that are truly about predicting things not measured.  Knowing the paths through a landscape can be even more important if you are carrying big data with you, which can make the effects of navigational errors all the larger.  

There is a new paper out by Jamie Tehrani on the evolution of the Little Red Riding Hood fairy tale that is getting some much-deserved attention. 

http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0078871

There are two points about this paper that are just fantastic. 

First, Dr. Tehrani applies phylogenetic methods to identify both inheritance and diffusion processes.  To accomplish this, he supplements his phylogenetic analysis with some network algorithms (neighbornet) and with detailed ethnographic knowledge of these fairy tale variants.  The results are a wonderful illustration of how applying phylogenetic methods does not lock the researcher into the assumption that culture evolves by inheritance rather than by diffusion.  Instead, the phylogenetic results actively support a reasonable ethnographic argument that cultural diffusion of the story elements was extensive in China, while the story elements were conserved and thus inherited in Europe.  Papers like Dr. Tehrani’s move us well beyond the now sterile debate about whether culture is inherited or diffused (folks, sometimes it’s one, and sometimes it’s the other, and sometimes it’s a mix, so deal with it).  I think studies like this one are a clear model for the future growth of quantitative cross-cultural analysis. 

I would also point out a prior paper by Dr. Tehrani, myself, and others, that showed how phylogenetic methods could also detect other types of cultural diffusion – specifically when different functionally or socially linked blocks of cultural traits move together from one population to another.

http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0014810

To understand the basic point of this paper, think of the way new languages or religions can be adopted wholesale by a population, whether by choice or conquest.  Such events result in cultural diffusion from the viewpoint of the population of people (they adopted a new set of traits) but from the viewpoint of the cultural elements the process is one of inheritance of a ‘cultural core’ because conversion can occur without blending of the elements within the core, i.e. the story elements of a single fairly tale, or the ritual elements of a religious denomination.  This is why whole populations can be converted and languages or religions moved about across the globe, and yet the process of change in characteristics of these same languages or religions can still, at least sometimes, evolve through tree like inheritance.  Our paper provides a method to detect and model such circumstances.

The second fantastic point about Dr. Tehrani’s paper is it shows the power of phylogenetic and network methods to construct empirically rigorous but quantitative models for global-scale cultural phenomenon.  In the past I have often thought of language and religion as the two systems of human culture that are ubiquitous, variable, and ancient, but Dr. Tehrani’s paper makes a strong case that traditions of folklore and fairy tale may also fit these criteria.  With the analytical methods we now have available, it is just a matter of motivation and funding for all of us to have quantitative global maps of the lineages (inheritance pathways) and linkages (diffusion pathways) among languages, religions, and folktales.  Indeed, it is just such a quantified and global model of the landscape of human culture that I believe was the original, pre-Boasian, mandate of anthropology.

Research out last August by Anders Eriksson and Andrea Manica made news in the press for contradicting claims of neanderthal ancestry in contemporary humans.  This research is completely driven by mathematical modeling rather than relying on empirical data, and I think ultimately Neanderthal admixture will be supported.  John Hawks has very capably laid out the scientific issues and the reasons why Neanderthal admixture is likely to be supported when all is said and done.  What I want to address in this blog are the moral lessons that some have tried to extract from the “out of Africa” explanation of human origins.  Considering these past arguments in light of the currently debated genetic evidence for Neanderthal admixture indicates why arguments for the equal treatment of people based on their common origin are dangerous rhetoric passed off as reasoned philosophy.

The history of the argument that we should all treat each other equally because we are all so genetically similar or because we share a common ancestry in Africa dates back at least a dozen years.  In an article that appeared in the New York Times, writer Nicholas Wade quoted Harvard biologist Edward O. Wilson as saying “We need to create a new epic based on the origins of humanity” (Wade 2000).  Dr. Wilson’s comments came from another article in the Wall Street Journal, in which he indicated that the evolutionary history of Homo sapiens could be a new basis for spiritual values that could replace traditional religion.  Mr. Wade’s own commentary from his article was that: “Many of the biologists who are reconstructing the human past certainly believe their work has a value that transcends genetics. Although their lineage trees are based on genetic differences, most of these differences lie in the regions of DNA that do not code for genes and have no effect on the body.”  He then quoted Dr. Peter Underhill, a geneticist who studies human origins as saying, "We are all Africans at the Y chromosome level and we are really all brothers."

Isn’t it convenient when scientific knowledge of the way the world is seems to justify how we think the world ought to be?  In this case people were arguing from evidence of the way biological variation originated in our species (world is) as a reason for why human behavior should be equitable across racial distinctions (world ought to be).  Trouble eventually follows though when people start saying the reason we ought to behave a certain way is because the world is a certain way.  As the out of Africa model gained more empirical support, even more scientists wanted to jump on the band wagon because they thought they had found a home-run secular reason to justify the equal treatment across race lines that had always been argued on theistic grounds from the time of the Abraham Lincoln and the abolitionists to Martin Luther King Jr. and the civil rights movement.  Searching online can find plenty of comments from anthropologists about how human biological variation is only ‘skin deep’ and we are all very recently diverged – as if racism would be more OK if biological differences went deeper than skin level or they diverged more anciently?  By 2010 Richard Dawkins was giving talks to forums for the black community about how “we are all African,” and even selling T-shirts! 

It was Christopher diCarlo, however, who laid out the case most explicitly that we all should treat each other well because of the facts of our origins in 2010 in Free Inquiry.  Dr. diCarlo does an admirable job of laying out the known science of human evolution.  Intriguingly, one of the scientists he covers prominently is Andrea Manica.  He summarizes the state of the science with: “We are all African. With these four words, we see a genetic coalescence of the entire human population. We now know that we descended from inhabitants of Africa who began migrating out of Africa around 60,000 years ago. In this way, it is impossible for us to not all be, in some ways, related.”  He then continues to draw philosophical lessons from this: “With these four words [we are all African], we see that racism is a human invention.  It is a social construct with lingering natural biases—leftover baggage from our mammalian xenophobic tendencies.”

I suppose then the proverbial shoe fell in May 2010 when scientists apparently confirmed that at least all living non-African humans have some Neanderthal ancestry that is not shared by African humans (here I use African in the idiomatic English language meaning rather than the sense of Dr. Dawkin’s linguistic contortion).  Yes, the percentage is small.  The original Neanderthal genome article put the value at 1-4% Neanderthal genes for non-Africans, but more recent studies indicate that number might rise to 8% summed admixture from Neanderthals and Homo erectus for some of us.  So, 8% non-recent African origin is small, but it certainly seems nontrivial.  Does that mean Dr. diCarlo now should conclude that racism is less of a ‘human invention’ or that some racism is more functional than ‘leftover baggage’?  Should we now start making T-shirts for Africans that say things like “Racially pure, no Neanderthal in here” or the Caucasian version “1-4% Neanderthal and loving it.”  If all Dr. Dawkins was doing with his T-shirt was educating the public about science then I suppose these post-neanderthal genome T-shirts are equally valid?  I hope he sends me a note when he starts selling them at his online store.

Of course Dr. Dawkins wasn’t just talking about science.  He and Dr. diCarlo were trying, poorly, to justify their deeply held ethical belief that equal treatment of people from different human subpopulations is a moral imperative.  For hardline atheists like these thinkers, the traditional theistic and metaphysical justifications on which abolition and civil rights were based are off the table.  They can’t believe as theists do that we should all treat each other equally because we emulate the God who knows and loves everyone regardless of the particulars of their traits or origins.  They don’t buy into the metaphysical claims of many Enlightenment thinkers that people are endowed with inherent rights that do not arise from natural origins.  Thus Drs. Dawkins, diCarlo and others predicated moral truth on empirical truth of our natural origins.  If they sincerely meant any of what they said, then they have to conclude racial prejudice is now a little more permissible (on the order of at least 1-4% more permissible). 

Alternatively, they could admit what I suspect is the case, that they never actually thought these arguments from peoples’ origins being equal were good justifications for people treating each other equally.  Admitting that however, would be tantamount to admitting that they don’t have a justification for their moral claims.  It would also mean admitting that instead of searching for good justifications for their moral claims, they would rather pass off glib rhetoric as reasons to their audience, apparently confident that their audience wouldn’t see that these are terribly illogical arguments, and therefore dangerous arguments, for equal treatment of human persons.

I suspect the current debate about human origins will land on the conclusion that some living humans exhibit some degree of genetic admixture from Neanderthals.  The result of this debate has many important scientific implications, but for those of us who hold to the reasons our culture has always held for equal treatment there are no ethical implications of this research.  From the many founding fathers of the United States who objected to slavery at our country’s infancy, to Abraham Lincoln and Martin Luther King Jr., our culture has always used some form of metaphysical argument, and usually a theistic one, to justify that people from different ‘races’ should be treated equally.  The theistic justification is a strong one precisely because it does not depend on any of the facts of what our origins, similarities, or differences may be. 

Wade, N. 2000. The human family tree: 10 Adams and 18 Eves. The New York Times. May 2, 2000, Tuesday, Late Edition – Final. Section F; Page 1; Column 1.

I've followed the literature and debates on the evolution of cooperation since the beginning of my graduate schooling.  Central to these debates is the role played by group structure in allowing altruistic cooperation to evolve, and whether it deserves to be called group selection.  What we call it is a semantic issue, but in their take-down of group selection, people like Richard Dawkins and Steven Pinker have misrepresented the evolutionary mechanisms for group selection.  The key misrepresentation is that selection only acts on individuals or genes, and that there is no value added to thinking about groups.

First, we have to be clear what this debate is about.  The only type of cooperation that matters in this debate is classically called 'evolutionary altruism.'  This means a behavior that has a real fitness cost to the actor who performs the behavior.  Doing the behavior itself is costly, such that it would be eliminated by natural selection if not for direct fitness benefits acquired by the recipient.  However, for any behavior to evolve that benefits only the recipient and not the actor, then there has to be a mechanism by which the recipients of the behavior also tend to be those who perform the behavior as well.  Otherwise there will be 'freeriders' who game the system by collecting benefits as recipients, but because they never perform the behavior they will never pay the costs.  Essentially all models for how evolutionary altruism can evolve are just mechanisms that effectively cut these freeriders out of being recipients, such that recipients are more likely to be also performers of the behavior.  The equation that formalizes the rigor with which the mechanism must exclude freeriders is W.D. Hamilton's famous formulation for inclusive fitness, which describes how the relatedness must exceed the ratio of the cost of performing the behavior over the benefit of receiving it.  r > c/b (see Nowak 2006).  r measures recent genealogical relatedness, in this case, and is one mechanism to ensure that the recipient has at least  probability = r of having the genes that cause the individual to perform the altruistic behavior.

That's the mathematical kernal - now enter decades of semantic nutshells around this.  Does altruism evolve because of group selection, or is it because individuals are gaining 'indirect' fitness benefits for themselves, or is it really genes promoting copies of themselves residing in the bodies of other individuals?  People like Dr. Dawkins and Dr. Pinker have been consistent and vociferous in their denial of any group selection going on in the evolution of altruism.  Here are some thought experiments that are troubling for this assertion:

1.  Consider an evolutionarily altruistic behavior that has evolved by kin selection.  For this behaivor, individuals recognize siblings and then perform a fitness-costly behavior that benefits the sibling.  Siblings have an r=0.5, which satisfies Hamilton's equation for this particular behavior, but assume relatedness lower than 0.4 does not.  So far so good.  Now, our individual selectionists will point to the benefits being given to relatives and say 'see - indirect benefits to the actors - ergo, no group selection effects.'  But what happens when a freerider mutant who never performs the behavior is born into an altruist family?  There is no reciprocity in this system, meaning the mutant's siblings just detect that he is their sibling and thus donates the benefits to him.  What happens?  The freerider mutant always will have a higher fitness than his siblings, because he never pays the cost of the behavior and he receives just as many benefits.  If the only thing we need to think about is the relative fitness of individuals, then kin selection cannot evolve altruistic behaviors because freerider mutants always have higher fitness than their altruistic siblings.  How can the gene for the altuistic behavior ever prosper?  Clearly within their own families, altruists always lose to freeriders.
    If you are a scientist whose thinking has been dogmatized against group selection, don't worry, there's hope.  Just each morning keep repeating the freerider (saying selfish is more fun) mutant scenario, and it will help to deprogram you.  This is what I had to go through to really understand this after years of programming.  Just keep saying, but what about the fact that freerider (selfish) mutants always have to have higher fitnesses within their own families?! 
    The reason Hamilton's equation actually works is because freerider mutants go on to have families that are dominated by sets of freeriders.  That's the only way it can work.  Once this happens, the payoff to selfishness drops below altruism because now the selfish individuals get none of the benefits of the behavior (none of their siblings perform the behavior).  Hamilton himself identified this very clearly in his 1975 book chapter in the volume "Biosocial Anthropology".  Try reading it in addition to Hamilton's 1964 (here is the first one of the pair).
    David S. Wilson has argued, and I agree, that the best way to think about and phrase this insight is that evolutionary altruism evolves because of across-group fitness differentials.  The reason Hamilton's equation works is because a sufficient level of recent genealogical inheritance will create enough across group fitness differential to overcome the invasion of freerider mutants.  Should we call this group selection?  Seems reasonable to me, but we could call it other things.  It differs from traits that we could properly say are selected only at the individual level because the fitness differential that causes the gene to increase in frequency only occurs across some sets of individuals in the population (in my scenario these sets are individuals across different kin groups).  Indeed, this is why Hamilton himself suggested in 1975 that it would be clearer to call some inclusive fitness effects as kin-group selection.  I like this term a lot actually.  We could also make the distinction by talking about selection that is within-group and within-population as different from selection that is within-population only.  That might be most pleasing to the likes of Drs. Dawkins and Pinker, as now we would only by implication be saying something happens at a group level.  It's completely correct - kin selected evolutionary altruism is not selectively favored within groups of kin.  Any claim that it is so selected is demonstrably, devastatingly, false.  Hamilton shows this very clearly in his 1975 chapter.  Within-group within-population is a bit wordy though, so we would have to make acronyms out of them and distinguish between WGWP and WP forms of selection.  Seems cumbersome, but again, we humans make up these semantics so we can call it what ever we like.


2.  OK, so after thought experiment 1 a now wavering anti-group-selectionist might be thinking 'well fine, but that scenario didn't change any of the actual predictions of my incorrect individual selection verbiage.  So, even if I was right for the wrong reasons, I still got the right predictions, which is what matters in science.'  That's quite fair, and I agree wholeheartedly that getting the right predictions is what matters in science.  Once we give up on this we become philosophers, and we had those for thousands of years and never figured out anything. 
    But individual selection thinking does get predictions wrong.  One prediction that gets missed every time without thinking about groups is the distinction between an altruistic behavior that by its nature is a public good (perhaps an alarm call to my whole group) from one that relies on kin recognition mechanisms.  In the former, there is no point in evolving kin recognition and the r that matters is the average r of the group.  The fact that the alarm call warns siblings, for example, has to be discounted by the fact that it warns nonrelated individuals, and the discounting comes out to exactly the mean r across the group that hears the alarm call.  This is a serious prediction that in my experience with students and colleagues gets missed all the time and, again, is very well laid out in Hamilton's 1975 paper (and also by the way it is in the 1964 paper).  Sure, you can rephrase how kin selection works on public good in inclusive fitness terms if you want, but the correct predictions do not flow naturally from this logic and I think are frequently missed by empirical researchers.

3.    Let's continue to where things continue to get worse for anti-group-selection views when we think more about gene-eye-views of the world.
    Are genes the unit of selection?  I have no doubt they are a unit of inheritance, but selection?  The only way they can be a unit of selection is if you adopt a very contorted notion of what a gene is.  Let me illustrate.  Imagine two villages of humans that each are involved in reciprocal altruism within the village - you scratch my back and I'll scratch yours.  So, now the individuals stop giving benefits to freeriders after they discover through their interactions who is a freerider.  Let's suppose one village all have the same gene variants (alleles) that cause them to behave with altuistic reciprocity, but the other village is like a UN training camp with people from all over the world.  Just by chance, the UN village has different alleles that have different base pair sequences.  They all equally produce the same altruistic reciprocity behavior, but they are different sequences, and they have different evolutionary origins.  Maybe some of them even differ in functional parts of the allele that contribute to reciprocal altruism, and maybe in fact some people's reciprocity behavior is influence by different sections of DNA that are not even orthologous to the sections that affect another's reciprocity.  Even though they create the same phenotype, would anyone really call these the same 'genes'?  With their different origins and different sequences can they be said to be the same 'entities' selfishly advancing 'their' own replication?  It seems to me they are not, and remember the village with all the same homologous reciprocity inducing alleles with the exact same base pair sequences!  Any natural interpretation of the English language would conclude that there is more successfully selfish genic selection for reciprocity happening in the homogenous village.  I mean, the UN village is benefiting copies of other genes that aren't even remotely related to each other. 
    The point is none of this 'gene's eye view' and 'selfishly replicating genes' stuff matters.  Evolution, remember, is just a set of mundane mechanistic interactions that stack up to produce algorithmic effects over time.  Think of it like a set of billiard balls being hit ever so deterministically on a table, but instead of the balls moving across space the chains of causal collisions are moving through time.  It doesn't matter whether these alleles had the same origin, different origin, or even if they are in the same places on chromosomes or code for the same protein products.  All that matters is they cause the individuals to do this reciprocal behavior with other reciprocal individuals, so all these diverse genes involved in such a system can rise with the other's tides.  I think somewhere Dr. Dawkins tried to redefine individual selfish genes as just this highly abstract entity, such that we would call different nonorthologous stretches of DNA and different nonhomologous alleles all one 'gene' if they were all related to a single phenotype.  But no one actually defines 'a gene' this way because it would make doing human genomes, and biochemistry, and most of biology impossible. 

After these thought experiments hopefully you have loosened up to see the key to evolutionary altruism is just to find any mechanism where the benefits of altruistic action keep getting to other altruists and freeriders are excluded.  Any mechanism that reliably establishes a correlation between being an altruist and receiving benefits from other altruists will do - it doesn't have to achieve this at every grouping level of society, and it doesn't have to do it through homologous genes that selfishly replicate themselves.  In fact, it doesn't need genes at all, just inherited stuff.  Now the predictions really start to diverge from typical gene and individual selectionist theory.  Because if you just need inherited stuff, then evolution can use cultural variants as well to create evolutionarily altruistic adaptations.  That's a topic for another blog (here is my recent paper on it), and I hate to say this one more time, but yes, W.D. Hamilton already identified how cultural evolution would work equally well for all this in his 1975 chapter.   

I think my experience is not unusual among anthropologists that I am often asked what anthropology is?  This question usually implies what makes anthropology a distinct discipline; that is, what makes it different from evolutionary biology or sociology?

Through teaching and multiple interactions with colleagues, I've found the best answer to this question of what anthropology is involves understanding a little about how anthropology came about.  Anthropology is essentially a natural science discipline.  It came about at a time when many natural history type disciplines arose.  As we discovered the incredible diversity of natural life in the 18th and 19th centuries, scientists began to specialize on particular taxonomic groups of related organisms.  Thus we started to have mammalogists and herpetologists and so forth.  After Darwin we had a fully viable mechanism for how all the diversity of life could be linked together in a single unbroken and absolutely continuous history.  Anthropology was born of the realization that this same unbroken character of evolutionary history must apply to humans; that all the incredible biological and cultural diversity of our species arose from a less diverse origin, and that it came about through evolutionary processes.  Since we are humans, it seemed reasonable that there should be a natural history science about ourselves.  Hence anthropology.

Because anthropology was born of the mindset of naturalists pursuing science, it made sense to early anthropologists like Edward Tylor and Louis H. Morgan that anthropology would pursue both a survey of extant cultural diversity and would investigate the archeological and fossil record of human existence as part of one discipline.  This is, after all, exactly how a natural historian of the time would attempt to understand the evolutionary diversification of a related group of fish or rodents.  You would want to know the existing diversity, about which you can of course have much more detailed information, but then also be linking that as much as possible to the direct evidence of past evolutionary change from the fossil record.  Comparison and comparative methods have always been key to the study of evolution in any set of organisms.  Indeed, comparison across many geographic scales, comparison among extant species, and comparison to the fossil record were all key sources of evidence for Darwin's insights on natural selection and descent with modification of species.

Given the role of language in human social life, and it's magnificent inheritance properties, it was sensible that linguistics would be brought into anthropological science at least in part.  The addition of primatology to the field was also a logical broadening of the comparative basis for understanding our species' evolution.

This is what anthropology was founded to do: to be the natural history science of humankind.  Such an endeavor does not encompass the study of all of human life, and the sciences of sociology and psychology had very different origins.  I will touch briefly on sociology, which is often the most difficult to disambiguate from anthropology.  In contrast to anthropology, sociology was not founded with the fundamental goal of understanding how human social life diversified to what it is today from a series of past mechanistic causes (evolution).  Sociology established itself as strictly the science of social causes for human social life and behavior.  Thus, sociology studied a type of causation that exists at a particular emergent level, just as chemists study the causation of interactions among atoms, and community ecologists study causal interactions among species, etc.  Sociology even today tends to model human social interactions as analogous to particle interactions of physics and with little interest in reducing causal sequences to psychology, biology or physics through a chain of causation; rather, the social causes themselves are of primary interest.  Sociology was always a level of analysis type science, and in that sense more similar to much of modern science (E. O. Wilson has written well on the contrast of natural history science and science practiced at a single emergent level). 

So, that is why anthropology made sense as a distinct discipline.  Does this characterize anthropology today?  Not really.  There are anthropologists (like me) who still are motivated principally by this vision of anthropology as the natural history science of humanity.  I think there has always been at least a small core of anthropologists with this view throughout its 100-150 year existence as a distinct discipline.  However, ever since Franz Boas, many and perhaps most anthropologists have not seen anthropology in this way.  It was Boas who first made popular within anthropology the idea that cultural diversity just springs spontaneously from peoples' heads, and that this construction of culture by ourselves had scarcely anything to do with our biological heritage.  Once anthropologists accepted that, the linkage of fossil diggers, archeologists, and ethnographers in one discipline started to seem incoherent.  This was exacerbated by the increasing popularity of nonscientific methods of cultural analysis that rejected any reconstruction of historical diversification and rejected quantitative methods.  Most of Boas' highly influential students, like Margaret Meade, helped push the discipline in this direction, further splintering anthropology.

What will happen now to anthropology?  I'm not sure, but I am sure that the comparative naturalist science of humanity will be conducted, whether by anthropologists, or some other group of researchers like cultural neuroscientists or psychologists.  This is because there is a real academic discipline at the heart of anthropology.  As we discover more and more about how genes affect our behavior, and even which genetic changes are responsible for our impressive cultural capabilities, the Boasian wall of separation between biological and cultural evolution will become more and more obviously false.  So, someone will take up the effort, because there is a lot of science still to do.