CAA 2012 [Call for Roundtable]: Models and Simulations in Archaeology: where we are and where we are headed?

Following the previous post, I just wanted to let you know that we (Mark Lake, Bernardo Rondelli, Xavier Rubio and myself) are also proposing a roundtable (details below). This year's (again, academic year) CAA will have two sessions on Simulation ("Archaeological Simulation Modelling as Computational Social Science: Next Steps Forward" and "Artificial Societies in Prehistory and Ancient Times") so it's a good opportunity to discuss "hands-on", having solid basis to start from.... In the last couple of years the number of publications using ABM, and computer simulations in general is growing, so we really need to sit back and think carefully about it...


Here's the details of our proposal:



Models and Simulations in Archaeology: where we are and where we are headed?


Computer simulation is a well-established technique which has long provided substantive insights in the physical sciences and increasingly does so in the life sciences. In addition, the field of social simulation has developed rapidly since the mid 1990s. The archaeological application of computer models dates back to the early 1970s, but despite forty years of activity, the impact of simulation has, with a few exceptions, been relatively slight, and it is largely still viewed as a fringe activity (e.g. Lake 2010). However, the first decade of the new ,millennium has witnessed a resurgence of interest, made manifest in a growing number of publications which provide the potential to change this perspective. In large part this is due to the way that agent-based modelling has captured archaeologists’ imagination, especially given the increased availability of simple software environments (e.g. NetLogo) that do not require advanced knowledge in programming and can easily be run on desktop computers.

Since this increasing “democratisation” of simulation seems likely to lead to more widespread application of the technique to substantive archaeological problems this seems a good moment to take stock and consider whether the appropriate epistemic foundations are in place to support the growth of productive archaeological simulation. It is vitally important to recognise that the availability and accessibility of user-friendly software environments does not solve per se issues arising from the complexity of the model building process and, in particular, the validation and portability of the experiment’s results. Consequently, an epistemological reflection and methodological overview of the use of modelling and simulation in archaeology is very timely. We think that a discussion between specialist and non-specialist, experienced and non-experienced users can stimulate a reflection on where we are, and more importantly, where we are headed in the application of computer simulation to archaeological research questions.

Topics:

Over tthe last decade the archaeological application of computer simulation has taken two distinct directions. On the one hand, a number of models have been developed to test specific hypotheses. This approach quantitatively or semi-quantitatively compares a existing archaeological data with artificial data produced by the simulations. The other direction is the development of abstract models derived from assumptions developed within our discipline or elsewhere (behavioural ecology, evolutionary anthropology, biology etc.), which have been used in to generate new
theories, or to explore the implications of previously formulated ones in a dynamic and computational environment.

The two directions are not mutually exclusive, but each leads to a series of important question: How can we validate abstract models? How can we translate archaeological and anthropological theories in terms computational and/or mathematical algorithms? How do the limits of computational representation affect our model building exercise? How does the scientific audience evaluate extremely complex and realistic models? How do we communicate our models, especially to non- specialists?
By discussing these concrete questions we hope that the roundtable will approach deeper questions, such as: will computer simulation have an impact on our discipline or it will remain a fringe activity? If the former, then will increased use of simulation change mainstream archaeology or it will lead to the emergence of a “new” sub-discipline?

We are particularly interested in the following topics:

• Abstract vs. Realistic Models: An adversative or complemental epistemology? 
• Mathematical Models vs. Agent Based Models: two faces of the same coin or alternative pathways?
• Validation and Verification of Computer Models. 
• Communicating Models: looking for a common protocol or a language? 
• Potential pitfalls and common problems of modelling in archaeology.
• Why computer models are still an outsider?

Subsistence Strategies, Uncertainty and Cycles of Cultural Diversity

Our (Mark Lake's and mine) paper on "The Cultural Evolution of Adaptive-Trait Diversity when Resources are Uncertain and Finite" have been accepted for a special issue of Advances in Complex Systems!
We basically extended the work we've done for the conference on Cultural Evolution in Spatially Structured Populations (see blog entry), focusing more on the dynamics of cultural evolution for traits which are: 1) adaptive (instead of being neutral) and hence determining changes in the reproductive rate; 2) characterised by negative frequency dependence (we've actually explored initially both positive and negative frequency dependence and a combination of the two, but that's another story/paper); and 3) produces stochastic yields. 
In practice, we developed an ABM (written in R) where agents forage based on a specific trait they possess. The yield of the foraging activity is associated to some degree of uncertainty and is restricted by two types of frequency dependence. In the S-mode model we've explored scenarios where different traits represents different technology or behaviour which are adopted for harvesting a shared resource, while in the I-mode model we've explored scenarios where each trait harvests a separate and independent resource (e.g. different preys). We then allowed agents to reproduce, die, innovate and learn (with frequency z)  using a model (payoff) -biased transmission following the model proposed by Shennan (2001), and measured the diversity of traits using  Simpson's diversity index. The model showed many interesting properties, here are some which I thought were particularly notable:

• High values of z (frequency of social learning) have negative impacts in both I-mode and S-mode models if some degree of stochasticity in the payoff. 
• When traits share the same resource, the highest rate of cultural evolution occurs with values of z which determines a limit cycle between moments of low and high diversity. 
• When traits are harvesting independent resources, the highest rate of cultural evolution occurs with values of z which determines the adoption of largest number of different traits (highest richness) with patterns similar to the Ideal Free Distribution.  When the frequency of social learning is too high, novel traits are lost by the innovators before this is transmitted to the rest of the population.

The negative impact of high reliance on social learning is perhaps the most interesting outcome and relates to what is known as the survivorship bias. Suppose a population of n individuals adopting the same trait A, which determines a normally distributed payoff (with mean μA and standard deviation σA).  At a given point in time an individual innovates and adopt a novel trait B, with a payoff which is on average higher than A (thus μA > μB). With a frequency z some individuals will copy the most successful (thus the individual with the highest payoff) individual among k randomly sampled individuals (with k being our sample window of observation for each agent). If the  σA=σB=0 the payoff will be always the same, and thus trait B will always produce a higher yield than A. This means that the novel trait will be adopted by approximately zn agents, and that the innovator will stuck to B (which will be always higher than A).   However if σA > 0 < σB (or in other words if the payoff have some degree of uncertainty) something different will happen. Since the number of individuals adopting trait A is by definition higher than the number of individuals adopting B, and since the payoff is stochastic, some lucky individuals with trait A are likely to have a payoff higher than μB. If these individuals are among the k sampled individuals of the innovator, the innovator will switch back, erroneously underestimating his own new trait. If z is low, the innovator is unlikely going to do this (it won't rely on social learning) while a proportion zn will have some chance to adopt trait B. If the number of individuals adopting this trait exceeds a certain number this will unlikely got lost, and hence can spread and invade trait A.  The survivorship bias tells a similar story. Suppose you are a businessman and decide to adopt a specific market strategy because you've read on forbes that some guy was successful on this. You have a model (the guy on forbes) which is successful and you explain this based on the strategy he used. However forbes won't mention you that maybe there are 10,000 other businessman who adopted the same market strategy but actually failed. The same applies for music industry. You see people making a lot of money, and so you decide to learn and give a try. And you ignore that hundreds of thousands of people did the same, and failed. The pattern is probably stronger here, because the success rate is not normally distributed, but much more skewed (in fact its likely to be a power law, see here). The small tail of very successful individuals are much more visible than the other (majority) of people. In our model the  shape of the distribution is different, but nonetheless the few successful people are regarded as a representative of a trait in a model biassed transmission. 

So what's the moral in all of this? If there is any, although it's an obvious, a bit cheesy, over-mentioned advice, is to "believe in yourself and not rely to much on copying successful individuals". They're in most case just lucky, and you might have something bigger in your hands.