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paleoanthropology, genetics and evolution

Peter Turchin

  • Quote: Peter Turchin on the "bugbear" of randomness

    Sun, 2009-08-30 16:18 -- John Hawks

    I'll probably have some more material on quantitative analysis of dispersal in the few days. Here's a quote from Peter Turchin (1998:17-18):

    Of course, we do not know that animals truly move at random, like flipping coins to decide whether to turn right or left. Each individual could be a perfect automaton, rigidly reacting to environmental cues and its internatl states in accordance with some set of behavioral rules. However, even if this were true, we might still choose to model behavior of such animals stochastically, because we would not have the perfect knowledge of all the deterministic rules driving these animals. Even if we did, we might not want to include them all in our dispersal model, since such a model would have an enormous number of parameters and would require a very accurate representation of all environmental "micro-cues." The point is that randomness is a modeling convention. Because it is impractical, and not even helpful, to attempt to model individual movement deterministically, we use a more parsimonious probabilistic model.

    I'm pausing the quote to point out my boldface. It has become computationally feasible in the last few years to model enormously complicated scenarios with individuals acting pseudo-deterministically. The most popular use of such modeling is to try to constrain dispersal models by some geographic conditions, such as local habitat richness, rainfall, or altitude (see also, "One model, hold the extra parameters"). Of course, animals really do disperse in ways that depend on such geographic parameters. The question is whether any datasets are sufficient to test models involving so many parameters.

    This approach is aptly termed behavioral minimalism (Lima and Zollner 1996). In essence, we adopt a thermodynamic approach: the behavior of individuals is erratic, or irregular, but the redistibution process at the population level has many regular features. There is a direct analogy with with thermodynamic theory. The motion of each gas molecule is chaotic and essentially unpredictable, and can only be described probabilistically. When dealing with large numbers of molecules, however, the laws at the aggregate level are for all intents and purposes deterministic. Similarly, the problem of biological dispersal can be treated by starting with a probabilistic description of individual movements (in other words, formulating the problem as a random walk), and then approximating the redistribution process of the ensemble of individuals with a deterministic equation, diffusion.

    The effective scale of stochastic versus deterministic processes is important. I'm chiefly interested in the dispersal of adaptive genes in human populations, for which the deterministic approximation may be considered to have become more and more relevant over time, as the population sizes of regional populations grew. Still, the present pattern in many cases may reflect the stochasticity of populations from earlier time periods, when they were smaller. And formerly important deterministic processes, such as the adoption of agriculture, may no longer be directly observable. So how do we model variance?

    The thermodynamic approach to dispersal does not have to assume that the movement of each "particle" is completely random. The important feature of this approach is that we can control the degree of realism in the model. Environmental factors that have strong effects on movement can be included explicitly in the model, while other factors that have weak effects (or about which we have no information) are included in the stochastic component.

    This would incorporate the geographic modeling approaches mentioned above -- deterministic processes related to spatial variance of habitat or dispersal potential. But then the important step must be to find a minimal deterministic model to account for the data, and then test it with other observations -- such as more extensive genetic sampling, archaeological information, or historical documentation.

    References:

    Turchin P. 1998. Quantitative Analysis of Movement. Sinauer, Sunderland MA.

  • Simulations bubbling like a stew

    Thu, 2009-04-16 11:40 -- John Hawks

    Peter Turchin writes very effectively about quantitative modeling and analytical methods in biology. So every so often I like to post an illuminative quote. Here's his description of maximum likelihood estimation, from Quantitative Analysis of Movement:

    Simpler, more direct analyses may make unwarranted assumptions, but they are better at revealing important patterns in the data, and their results can suggest what variables and functional forms to use in the modeling of data. Eventually, however, direct methods of analysis get beyond the bounds of their competence. The general approach discussed in this section can in principle estimate parameters of any model, given infinite amounts of informative data and infinite computer power.

    The basic approach is to construct a detailed simulation model (better even, a series of models) and fit it to the data using nonlinear estimation techniques. Jon Schnute colorfully describes a detailed simulation as a "stew" of calculations from which observable quantities (to be compared with the actual data) bubble up to the surface (quoted from Hilborn and Mangel 1997). Nonlinear estimation is the process of adjusting the parameters of the stew (adding more or less salt, increasing or decreasing temperature, etc.) until the stuff that bubbles up resembles the actual data the best. The crudest approach is to change parameters in the simulation by the method of trail [sic] and error and to compare the simulation results to data by eye. A more refined approach is to use some quantitative measure of goodness of fit and a nonlinear minimization routine to search for the best fit automatically (Turchin 1998:295).

    The quote has some relevance to yesterday's discussion of the Neandertal population structure paper. I'm philosophically reluctant to turn to simulations until I exhaust my analytical options. This is a matter of trusting myself -- if I really had a lot of confidence in my ability to choose the right assumptions to underlie my simulations, I might turn to them first. But assumptions are tricky. Analytical models have their own assumptions, but those have the advantage of transparency -- I didn't pick them, they are fundamental to the models.

    Still, in some cases it doesn't take long to exhaust the analytical options. So we let the observable quantities "bubble to the surface" of simulations.

  • Migration and social change

    Wed, 2009-01-07 10:14 -- John Hawks

    Peter Richerson and Robert Boyd are well-known for their studies of processes of culture change. They apply principles from biological evolution to form hypotheses about changes of cultural traits over time.

    In the December 18 issue of Nature, Richerson and Boyd have an essay titled, "Migration: an engine for social change." In the essay, they advance the hypothesis that migration is a "selective" mechanism of social change:

    As cultural evolutionists interested in how societies change over the long term, we have thought a lot about migration, but only recently tumbled to an obvious idea: migration has a profound effect on how societies evolve culturally because it is selective. People move to societies that provide a more attractive way of life and, all other things being equal, this process spreads ideas and institutions that promote economic efficiency, social order and equality.

    This idea draws from an analogy with the evolutionary mechanism of gene flow. Migration of individuals may cause an adaptive gene from one population to enter another population where it had previously been absent. A population that is a net recipient of gene flow from other regions may thereby receive a large store of alleles for selection later to work upon.

    In the context of Premo and Hublin's hypothesis that culture tended to impede migration in the Pleistocene, Richerson and Boyd's model of migration accelerating culture change is an interesting contrast.

    Migration rates between historic hunter-gatherer groups were quite high. Minimal bands are exogamous units -- nearly all marriages occur between these small groups. The larger groups made up of several bands -- sometimes called tribes -- are endogamous, but rates of migration between them are nevertheless high. For example, Joe Birdsell estimated intermarriage rates between tribes of Australian hunter-gatherers at 14 percent (that is, 14 percent of marriages involved members of different tribes).

    Richerson and Boyd mention migration in small-scale societies (they use the example of Papua New Guinea). But more broadly, they want to apply their idea to the recent large movements of people from poor nations into rich ones:

    We believe that immigration generates far more cultural evolution today than does conquest. Flows of migrants are often substantial. Foreign-born people, mainly from Latin America and Asia, compose about 11% of the current US population, a figure close to historical averages. The richer countries of Europe, such as Sweden, Norway and Germany, once the source of streams of immigrants to the United States and elsewhere, are now receiving people from Asia, Africa and poorer European countries such as Poland and the Balkan states.

    Eleven percent migrants would be a high value for historic tribes of hunter-gatherers -- where children are perhaps half the population. Considering adults only, it might not be so unreasonable for small-scale societies. If these people transplant successful cultural elements -- effective technologies or ecological knowledge, for example -- then such migration will be an important way of equalizing the cultural adaptation of different societies. In other words, gene flow as applied to ideas.

    But I can think of at least two weaknesses with the biological analogy.

    1. Gene flow may introduces alleles that would otherwise be absent in a small population. But a very large population would be likely to get those alleles anyway. Likewise, ideas may be subject to diminishing returns: a immigrants might make a large difference to the set of ideas available in a small backwater society, but even a very large store of migrants may make little additional difference to an already-large cosmopolitan society.

    2. Cultural "institutions" include things like economic laws, education systems, dispositions of marriages, births and child care obligations, and political structures. Each is an invention involving the interaction of many ideas, and they work to the extent that ideas and behaviors are shared. A relatively small amount of graft and violence, for example in 1920's Chicago, can subvert a political and legal system that depends on widespread honesty and compliance. If we were to think of a biological analogy for such complicated systems, we would probably tend to think of "coadapted gene complexes" -- instances in which the adaptive value of one gene depends on the presence or absence of many other genes. Coadapted gene complexes are instances in which gene flow may not typically lead to any additional power for selection. At an extreme, gene flow may lead to disruptive selection, or reinforcement of slight genetic barriers may enhance or accelerate speciation.

    The biological analogy loses force if these weaknesses apply. I tend to think that if we are looking for causes of "cultural evolution," we should attend carefully to cases where biological evolution may lead to similar conflicts where one force (migration) generates opposing effects (adaptive and maladaptive). Sometimes gene flow has a bad outcome -- at least to the extent that "bad" means "lower mean fitness."

    In his 2003 book, Historical Dynamics,, Peter Turchin develops a model of culture change that emphasizes group solidarity. Following Ibn Khaldun, Turchin refers to the cohesive character of a group of people as asabiya. In his theory, relatively small societies forming in frontier regions are subject to intense selection. Societies that develop common feeling and solidarity tend to persist; those that remain dissolute tend to be absorbed by larger entities. Over time, a few or a single society may come to dominate a large region -- forming an empire. But these large societies slowly lose their common feeling over time, resulting in their eventual loss of political cohesion and overthrow at the hands of upstart peoples with higher asabiya.

    I mention this theory not to explain it in depth, but to point out that it has roughly opposite predictions from the hypothesis Richerson and Boyd propose. In Turchin's model, widespread immigration may follow as a consequence of empire's spread and success, but this immigration is one of several forces that weakens or destabilizes the society. Richerson and Boyd's scenario, in which immigration strengthens a society, would seem relevant in Turchin's model only at times when the society is initially forming.

    But immigrants, and their descendants, adopt some of the ideas and institutions that make their new homes better places to live and raise families. This integration promotes the spread of ideas and institutions that encourage order, justice and economic efficiency.

    I think that integration is a difficult problem that demands its own solutions -- new ideas and institutions to facilitate the interactions of people who don't have the same background or necessarily the same language. Some societies have made this work, others have not, and they have done so using different ideas and institutions. In particular, "economic efficiency" and "justice" have different relations to immigration in the U.S. compared to Saudi Arabia. Or in the U.S. today compared to the U.S. in 1910. And some nations have made effective use of ideas from other cultures without massive immigration. Japan between 1860 and 1930 and Britain between 1700 and 1850 would seem to fall into this category.

    We may say that the Chinese unwillingness to adopt Western ideas, trade, and immigration through the early twentieth century had a bad outcome. But are we measuring the outcome in terms of geopolitical position, in terms of the ability of entrenched elites to maintain power and order, in terms of territory? Or in terms of numbers -- something more akin to "mean fitness?"

    Ultimately, we evaluate the strength of a hypothesis by its ability to predict or explain empirical evidence. Naturally in an essay of this kind Richerson and Boyd did not have the opportunity to draw in examples in a way that clearly support their hypothesis. But those examples they are able to include seem pretty weak:

    Likewise, the growth of ancient empires seems to have owed much to the assimilation of border peoples. Conquering elites, such as the Mongols in China, the Mughals in India and the Goths in Rome, largely adapted to their highly successful host culture rather than the other way around. In every case, these durable systems had institutions — the Confucian merit-based bureaucracy, the Hindu system of self-governing castes, Roman law — that endure today in one form or another.

    These examples support the idea that societies that attract immigrants tend to have ideas and institutions that cause them to be richer, less violent and less exploitative than the societies that supply them. The Goths were fleeing chaos on the steppe. Christianity, with its concern for the poor and humble, grew mainly by voluntary conversion to eventually become the official religion in the Roman Empire. Confucian humanism, with its concern for good government, replaced the predatory and quarrelsome landed elite as the backbone of Chinese society. Hindu tolerance and productive organization of cultural diversity led to one of the world's wealthiest societies in medieval times. Medieval Islam attracted converts spanning from North Africa to southeast Asia because it supported effective statecraft, intellectual advancement and trade on a vast scale.

    This is a fairly Pollyanna-ish view of the growth of Christianity, Islam, and Confucianism. It seems close to claiming that the Goths were a net benefit to Roman cultural institutions.

    Alexander the Great and Genghis Khan were successful conquerors, but they made a less durable impact on the world than, say, Mohammed, Buddha, Christ and the institution builders they inspired such as Constantine and the Umayyad caliphs.

    Maybe there is a case here somewhere, but I'm not sure Constantine is really the example they wanted.

    A good cultural conservative would point out that there is no surprise when an effective cultural institution survives for a long time. Effectiveness causes persistence. Random changes that affect the function of a long-lasting institution are likely to be bad. An institution that can tolerate changes in other cultural parameters (one that is modular) will tend to persist in the face of other changes -- like the substitution of an imperial elite for that of a conquering horde. Conquering hordes from the hinterland didn't come fully equipped with cultural mechanisms to govern cities, tax commerce, and ensure agricultural productivity. Of course they would have adopted the trappings of the empires they conquered.

    Like biological adaptations, we should be skeptical that changes would make long-lasting cultural institutions more effective. We can also consider that their continued effectiveness depends on many other shared aspects of culture.

    References:

    Richerson RJ, Boyd R. 2008. Migration: an engine for social change. Nature 456:877. doi:10.1038/456877a

  • The utility of theoretical models

    Tue, 2008-10-21 16:33 -- John Hawks

    I'm reading through Peter Turchin's 1998 book, Quantitative Analysis of Movement, for a project I'm working on. I found that his second chapter gives a very nice introduction to the reasons why biology depends on formal mathematical models. This is a topic I often review in my courses, so I'll quote some of his discussion.

    He lists six objectives for model-building on pp. 33-35, each with some explanatory text. This amounts to a paragraph or so for each reason; I'm only giving one or two sentences of each, with much omitted.

    Formal statement of the problem ...The necessity of stating the assumptions of the model is another benefit. A mathematical description of a problem forces one to be very clear about what the different variables and parameters in the model are, and how they are interrelated.

    Identifying knowledge gaps ...It may turn out that good quantitative data are available to estimate some functions and parameters but not others, immediately suggesting a focus for the empirical program. When there are many gaps, one has to decide which parameters need to be estimated precisely, and for which parameters ``guesstimates'' will do....

    Gaining theoretical insights There is a large class of models that are never intended to be directly confronted with data.... The purpose of such models is to gain insights into possible causal interconnections between various factors and, in general, extend our intuition...

    Quantitative tests of theory ...A qualitative prediction allows one to test the theory that generated it, but it does not provide a very strong test. Because there are only a few possible outcomes in a qualitative situation (e.g., factor X will either increase, stay the same, or decrease), the probability that the ``correct'' outcome will happen by chance is correspondingly high. A quantitative prediction, on the other hand, can be a much stronger test of the theory, because it will not only say that X will increase, but how much...

    Interpreting the data Sometimes an investigator is motivated not by a desire to test general theory, but by the necessity of measuring some specific quantity [that would be impossible to measure directly]...

    Forecasting and prediction ...Forecasting is weaker than prediction, and uses the knowledge of the past behavior of the system to forecast its future state. Forecasting does not necessarily require an in-depth understanding of the system's dynamics, and can be done at the phenomenological level. However, forecasting will most likely fail if the system's dynamics change. I use prediction in its strongest sense: that is, to predict a situation that was not encountered in the past. For example, it may be necessary to generate predictions about how a system's behavior will change as a result of a certain human intervention. Prediction, in general, requires a mechanistic understanding of the system....

    I especially appreciate the point about quantitative tests --- one that has eluded many paleontologists who are content with categorical statements that are essentially untestable, because they only assert that something should happen ``regularly'' or ``more often'' than something else.

    Also, the final point, about forecasting and prediction, is valuable -- although perhaps idiosyncratic, as I have not seen that distinction made elsewhere. Still, it applies far beyond theoretical biology and into historical science generally. If we consider our state of knowledge about climate change in response to human activity, clearly this is an example where the distinction between forecasting and prediction is relevant. We can have confidence in a prediction only if it entails a suitable understanding of the mechanisms of change in the system, whereas forecasting is accurate only to the extent that we can depend on a uniformitarian assumption -- that the conditions observed in the past followed the same mechanistic relations that will be relevant to the future.

    I tend to lecture about genetic models, for which there is a great value in simplicity (point 3), but which may require quite complicated extensions to handle reasonable biological populations (point 2). In that connection, some reasonable people go to extremes of interpretation -- sometimes claiming that the data necessitate some assumption on the basis of a very simplified model, and in other cases claiming that no model can apply to the complex history of the population. It is our task (my task) to determine which factors are important and conceivably affect results, and which will always be too weak to influence the interpretation of the data (point 1). And the end will often be to discover evidence for values in past human populations for which we have no direct means of estimating aside from genetic variation (point 5).

    References:

    Turchin P. 1998. Quantitative analysis of movement. Sinauer Associates, Sunderland MA.

  • Cooperation, phenotypic vectors, energy

    Wed, 2006-04-26 08:53 -- John Hawks

    Burtsev and Turchin (2006) present the results of simulations of cooperative behavior in self-interested agents. This is a well-established subject, and their contribution is that their strategies are "evolved" from basic behavioral elements within their simulations, instead of being assumed a priori.

    In our model, agents are endowed with a limited set of receptors, a set of elementary actions and a neural net in between. Behavioural strategies are not predetermined; instead, the process of evolution constructs and reconstructs them from elementary actions. Two new strategies of cooperative attack and defence emerge in simulations, as well as the well-known dove, hawk and bourgeois strategies. Our results indicate that cooperative strategies can evolve even under such minimalist assumptions, provided that agents are capable of perceiving heritable external markers of other agents.

    This to me is one of the most interesting aspects of the model: behavioral traits gain random associations with recognizable phenotypes, and individuals shape their behavior according to the phenotypes that they detect around them.

    Each agent has external phenotype that is coded by a vector of integer values (markers). The markers do not influence behaviour but function only as indicators of similarity....All of our simulations were started with an initial population of agents that were unaware of markers (the matrix coefficients connecting input from markers to actions were preset to zero). Thus, the use of markers in a population had to evolve from a blank slate. Because markers and behaviours are not linked (apart from both being inherited from the ancestors), agents can lose cooperative behaviours by mutation while retaining 'in-group' markers. Thus, the structure of the model allows free-riders to arise.

    This "phenotypic association" vector is suggestive. Of course, for real animals it would probably be more effective to recognize the behaviors themselves as signs. But this depends on multiple opportunities -- you have to see somebody else's behavior at least once to judge it. If there were external manifestations associated with behaviors, it would give the opportunity to decide before an interaction what the other individual's likely strategy would be.

    But then, selection would favor mimicry -- free-riders with the phenotypes of cooperators, for example. This force will tend to limit the degree of association between observable traits and behaviors...

    except...

    That observable traits that indicate relatedness will also tend to indicate similarity in cooperator phenotypes. In other words, you can figure that your relatives will tend to act like you, and also tend to look like you. And as a bonus, if you are sharing with a relative, you are increasing your inclusive fitness.

    They find that the evolution of different strategies depends on carrying capacity, and some new strategies emerged. The comparison of these is worth reading, but a bit too long and involved to quote at length. This part is important:

    Our results have important implications for the evolution of territoriality in animals (and private property in humans). With a few exceptions, theorists have paid little attention to the role that cooperation may have in the evolution of territoriality. Our study suggests that cooperative defence of territory can radically change the course of evolution in resource-rich (C > C2) environments. When the amount of resource becomes large enough to support more than one agent, and too large for a single agent to monopolize, solitary bourgeois are replaced by cooperative starlings, provided that agents can recognize in-group members. The starling strategy does not take over completely, however, but coexists with other strategies in a complex dynamical way.

    The "starling" strategy is a mobbing strategy, in which small animals cooperate to attack and drive away large solitary predators.

    One limitation of these kinds of simulations is that they don't include reproductive boundaries. For this reason, they don't really distinguish models of within-species cooperation from between-species mutualism. Different strategies like "hawk" and "dove" might really represent predators and prey species, or they might represent contrasts of competitive behavior within a species.

    So the appearance of stable strategies at any given level of possible complexity might be a constraint on natural communities, but the level of that constraint may not be immediately obvious. To that end, this study has a very large possible set of strategies (more than 101000 combinations), which means it is sampling a richer set of behaviors than most simple game-theoretic models.

    References:

    Burtsev M, Turchin P. 2006. Evolution of cooperative strategies from first principles. Nature 440:1041-1044. DOI link

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Neandertals

For years, I've worked on their bones. Now I'm working on their genes. Read more about the science studying these ancient people.

Denisova

From a finger bone of an ancient human came the record of a completely unexpected population. My lab is working on the science of the Denisova genome.

Acceleration

The advent of agriculture caused natural selection to speed up greatly in humans. We're uncovering some of the ways that populations have rapidly changed during the last 10,000 years.

Malapa

Just outside Johannesburg, the Malapa site is producing some of the most exciting finds in human evolution. This site is the headquarters of the Malapa Soft Tissue Project.