I’m a big booster of the idea that human demographic expansion helped drive our recent evolution. So you might expect me to like the new paper by Adam Powell, Stephen Shennan and Mark Thomas, titled, “Late Pleistocene demography and the appearance of modern human behavior.” Yet, I see a lot of weaknesses in the paper. I think the paper tries to sidestep several issues about “modern human behavior” that ought to be tackled head-on. In the end, the model in the paper can’t describe the data the authors want to consider. Maybe they should have adopted a different model; maybe different data.
I’ve taken a lot of notes about this – too many for me to share, but I wanted to review the basic exposition of the paper, including why the authors think demography may determine technological change during the Late Pleistocene. I might post other notes later on the issue of genetic modeling of demography and its relevance for archaeology. The authors describe a model in which the density of a metapopulation determines the rate of increase (or decline) its cultural evolution, using simulations to extend analytical results from Henrich (2004). Follow their assumptions and you arrive at the conclusion that population density can, under certain conditions, constrain the trajectory of cultural change.
The question is whether the model’s assumptions can apply to the real world. Here’s the abstract of the paper:
The origins of modern human behavior are marked by increased symbolic and technological complexity in the archaeological record. In western Eurasia this transition, the Upper Paleolithic, occurred about 45,000 years ago, but many of its features appear transiently in southern Africa about 45,000 years earlier. We show that demography is a major determinant in the maintenance of cultural complexity and that variation in regional subpopulation density and/or migratory activity results in spatial structuring of cultural skill accumulation. Genetic estimates of regional population size over time show that densities in early Upper Paleolithic Europe were similar to those in sub-Saharan Africa when modern behavior first appeared. Demographic factors can thus explain geographic variation in the timing of the first appearance of modern behavior without invoking increased cognitive capacity.
You can always tell what’s supposed to be bad, it’s the thing that you’re not supposed to to “invoke”. You know, like witches and vampires.
In fact, “cognitive capacity”, as a continuous, one-dimensional variable, underlies the model. In a nutshell, the model assumes that people learn behaviors by instantaneously absorbing the “skill” (which I’ll call “mojo”) from the best (highest “mojo”) individual in their population. But they don’t learn perfectly; their mojo ends up varying.
Nevertheless the whole population is choosing one individual to copy, so what happens over time is that the population changes in one direction or the other. If the distribution among individuals includes a few with higher mojo, then the average amount of mojo should increase over time. Imagine if the whole population copied the running style of the best 100 m runner. The world record might reduce over time; and then people copy the new world record holder, and the average speeds up again, ad infinitum. There is stochastic variation from one step to the next – sometimes it will increase more, sometimes less, and sometimes it may shrink a little. But the model is deterministic: depending on the distribution of mojo, it will either trend upward or downward.
I picked the analogy because it points out a weakness of the model. There’s no possibility of reaching an optimum, or a stasis. In fact, the survival value of “mojo” simply isn’t part of the model, nor is the cost of developing mojo.
OK, it’s a simple model – too simple to capture most aspects of reality. What value can it possibly have?
The assumption is that some behaviors take more mojo than others. Some behaviors then will lie near a threshold where the population is just at the border between gaining or losing mojo over time. The fastest runner in the population might still be slower than last year’s champion. If the population models the new winner, they might lose mojo on average.
So the change in mojo doesn’t depend on the current average; it depends on the distribution of the highest-mojo individual. That’s an extreme value, and extreme values depend on the total number of individuals. There’s some chance that the Jamaican national champion will be the Olympic gold medalist – like last year. But on average the world champion is faster than the champion of any single country; the champion of a country is faster than the champion of any average local track club, and so on. Numbers make a difference. Add more individuals, and you have a better chance of a high extreme value – a better chance in the model that mojo will increase.
Again, the analogy shows the model’s deficiencies. Local track clubs don’t vary randomly. There are some local track clubs where the average 100 m time is pretty close to the Olympic champion’s. In part this is because information isn’t shared instantly and universally. There are both explicit dynamics and path-dependence: Jamaica’s running team has been so successful in part because of recent investments in infrastructure, in part because of leadership from a few gifted coaches. And in large part it’s because talent matters. Some people just have more running mojo.
But the model does show that for a limited range of behaviors, population size (in Powell and colleagues’ simulations, local population density) can exert a deterministic effect on the behavior of the population. Outside that range, the behavior will be dominated by non-demographic factors, such as intrinsic qualities of the learners.
Deterministic versus stochastic models
The question is whether the limited range of behaviors that might respond to demography are actually relevant to the archaeology. Unfortunately, there’s no way to predict which behaviors ought to respond to demography in this way. You might find a really clever way to test the hypothesis, even without knowing – that was one of the features of Henrich’s (2004) paper that first presented the model. I think in the current case, we can start here: If the authors’ model were true, then demography would exert a deterministic effect on technology. A larger population would have a higher average “skill” level, which (by the authors’ model) would allow the development of more complex culture.
When it comes to individual artifacts, demography’s effect is stochastic. The development of technology has been path-dependent, with different populations following different paths. Sometimes those paths have included similar features, sometimes not. The same idea that spreads in some populations may fail to spread in others, despite the same demographic conditions.
For example, the Aurignacian split-based bone point is an intrinsically unlikely artifact. Most people in the world did not produce them, even though bone points were fairly common, especially in groups who used small-projectiles. Carved ivory figurines, on the other hand, are not nearly so unlikely; many peoples in the world have produced them. But some populations did so at very low population sizes and densities, while others have made carved ivory figurines only after reaching very large population sizes with highly specialized division of labor. Large populations make it more likely that we’ll see carved ivory figurines, among other things, but they do not determine that such figurines will be present. In other words, population size is one factor affecting the stochastic appearance of these artifacts.
OK, but what if we try to generalize beyond individual artifacts or traditions and consider “modern human behavior” as a whole? Isn’t there some general and abstract factor that might change deterministically with demography? To test that hypothesis, we need to (a) develop some accurate measure of the abstract factor, and (b) observe it to be deterministically influenced by demography.
Here’s an example: For our work on the acceleration of recent adaptive evolution, our hypothesis was that a deterministic model based on recent demographic expansion could describe the number of new selected mutations in human populations. We tested the hypothesis by developing a measure for selection, and by showing that the numbers of variants matched the predictions of the deterministic model. This global conclusion about the number of variants holds despite the fact that any particular case of selection on a gene depends on many stochastic factors, including the occurrence of a favorable mutation, its escape from genetic drift when rare, and the function of the gene relative to recent human ecological changes. In the limit of large numbers, these random processes do not obscure the deterministic effect of population size.
Now, for archaeological observations, we could in principle follow the same procedure. If there is an abstract factor of “modern behavior”, we might develop an accurate measure of it by understanding the relationship of the abstract factor and particular artifact types. That’s the reason why archaeologists have devoted such extensive effort to defining “modern human behavior.” The entire goal of defining “modern human behavior” is to make archaeology an instrument for measuring the cognitive advancement of prehistoric groups.
Yes, there’s some irony here. Many archaeologists don’t want to “invoke” cognitive capacity, even as they define “modern behavior” as a proxy for it. Artifacts certainly change stochastically. If we wanted to test a stochastic model of change, we might as well use artifacts directly. But that might not allow us to test whether the demographic factor was more important than other factors, such as developmental or ecological ones. Can we expect some combination of artifacts to behave deterministically?
The current paper chooses a simple threshold definition for the abstract factor: the Blombos incised ochre artifacts and pierced shells define the same level of “modern behavior” as the early Aurignacian of Europe. Why those two populations? Why those two behaviors? Why ignore much earlier engraved lines from other places, or pierced artifacts made by Neandertals? The paper doesn’t make any serious effort to defend this measure of an abstract factor underlying “modern behavior”.
I think at a minimum, the authors need to show that their measure of “modern behavior” is replicable and predictive outside the context of these two populations. If engraved lines can be a threshold measure of “skill”, then they should reliably appear in some contexts and not others. If pierced shells can stand in for other elements of behavior, like small game exploitation or projectile use, then show the strength of the correlation. If they can’t stand in reliably for their abstract factor, then they need to find some combination of observations that can. If there is no combination of observations that proves reliable, then their model cannot validly apply.
The second necessary element for testing the deterministic model is to show whether the measure is deterministically affected by demography. On this score, the paper is much more convincing: Their demographic model cannot explain the distribution of their measure of “modernity”.
Oh, I know, the conclusion of the paper says the opposite. But look at the data: The model predicts that southern Asia should have Upper Paleolithic-like industries beginning long before they appeared in Europe, and that southern Africa should have retained Upper Paleolithic-like behaviors throughout the last 90,000 years or more. Neither of those predictions holds up. The authors don’t consider the mtDNA evidence for population growth in the New World (where art and ornamentation are rare among Paleoindians) or Australia (which underwent substantial complexification during the Holocene). The comparison of Europe and South Africa is an assumption of their measure, not a prediction or conclusion.
The model really only gets one prediction correct: The West Asian record undergoes an Upper Paleolithic transition at around the same time as Europe. And even on that score, one may quibble: was the Levantine initial Upper Paleolithic earlier than Europe or later? Does the European mtDNA expansion, which mainly consists of mtDNA lineages derived from West Asia, record European demography or West Asian demography?
They’re left making a variety of ad hoc arguments to explain why the model doesn’t fit the demography: maybe the mtDNA samples don’t represent Late Pleisocene populations exactly; maybe the population really shrank in post-Howieson’s Poort South Africa even though the mtDNA (and a lot of archaeology) say it didn’t; maybe there were recurrent bottlenecks and expansions not covered by the mtDNA demographic models. When ad hoc hypotheses add up so quickly, there’s often much more parsimonious option: maybe the model is wrong.