 Okay, great, thank you. So I thought I should begin, first of all, by explaining for those of you in the audience who might not know or might not be familiar with agent-based modeling, what exactly agent-based modeling is. So agent-based modeling is a type of computational model that simulates the behavior of autonomous agents in order to understand a complex system. In a historical setting, these agents can be anything from individuals to households to larger population groups, and they can be designed to behave as complexly or simply as one might desire. In such a model, agents are placed in an artificially constructed environment with rules that control how each agent reacts with that environment and with each other. Each agent has the same portfolio of variables, but the precise combination of variables in each agent is unique. As the agents in the system react, interact, and influence the environment and each other, various social phenomena can be observed to emerge. ABMs are thus concerned with the passage of time and how the system changes as a result of processes, processes that are hopefully analogous to the real world. So why should archeologists model in this way? Well, historians and archeologists create models all the time, sometimes formal, mathematical ones, but most oftentimes these are informal narrative ones that simply explain how or why an event or phenomenon comes to pass. And there are several different reasons an archeologist might create a computational simulation. I've listed four here to testing quantitative methods, hypothesis testing, theory building, or even just as an educational tool. But the one I want to focus on is the third one in my list. And just mostly to point out that the goal of computational agent-based models doesn't have to be about proving a theory or definitively explaining a phenomenon once and for all. Rather, it can just provide us with a means to more robustly interrogate our own understanding how things worked in the past. The act of creating a model, choosing agents, of giving them characteristics, writing their social rules in the form of algorithms, this forces us to clarify our own beliefs about how entities behaved and how various processes worked in the past. And importantly, it forces us to make explicit our own assumptions. In other words, agent-based modeling allows us to test, to critically examine, and to refine our conceptual models. Now, agent-based models have been widely employed in the social sciences for a long time to study complex systems and human behavior. In archeology, ABM has gained a small but steadily increasing flow of adherence as a useful and promising method for understanding particular aspects of complex social systems and networks in the past. One of the most widely known and successful early ABMs in archeology is the artificial Anasazi model, which successfully replicated the archeologically documented settlement patterns of a prehistoric community in a valley in northwest Arizona over a period of 500 years. The model demonstrated that the eventual abandonment of the valley could not be explained by simple environmental factors. And the model also allows researchers, not only the ones who created it, but any researcher to explore the relationship between resource availability, settlement location, and population growth. Since about 2000 or so, interest in ABMs has exploded in archeology, and many agent-based models have been developed to lead insight, sorry, to lend insight into the formation of settlements and their evolution in a diverse range of historical environmental landscapes, ranging from Bronze Age Mesopotamia to hunter-gatherer groups in Japan to the prehistoric settlements in the Peruvian Andes, pictured here. The active modeling, that is, transcending our understanding of how ancient societies worked into code, algorithms, this facilitates cross-cultural comparison of social processes and mechanisms, which makes it particularly appealing for interdisciplinary studies, particularly with regards to process. With regards to the emergence of urbanization and complex social systems, ABMs can be especially useful for disentangling the various external and internal factors that impact settlement structures in order to better theorize settlement, nucleation, and dispersion. This is what made it appealing to me when I started looking more closely at the rise of urbanism in early Egypt. So let me give a quick and dirty, very oversimplified account of the rise of urbanism and social complexity in early Egypt as we know it archeologically. So the archeological record suggests that the domestication of plants and animals took hold in the latter part of the six millennium BC. In the fifth millennium, we see the gradual establishment of what Egyptologists like to refer to as pastoral communities in the Nile Valley, partly nomadic, partly sedentary. And in the latter part of the fifth millennium, we see the emergence of fully sedentary villages in the Western Delta, the part that's labeled Lower Egypt. And this seems to coincide with the gradual drying out of the Western desert. Around the beginning of the fourth millennium, we see fully sedentary village life emerge in the upper Egyptian Nile Valley. And Egyptologists tend to characterize these largely egalitarian, small agri-communities. From that point, there is a gradual process of population growth and transformation of this settlement landscape along the Nile Valley from a system of loosely dispersed settlements along the desert zone, along on the edge of the flood plain, to denser, more nucleated settlements within the flood plain itself. Grave grids from cemeteries, usually placed in the desert fringes, also indicate that there is a growing social complexity and inequality at the same time. So in around 3,500, 3,400, we start to see the emergence of very clear settlement hierarchy in signs of urbanism in upper Egypt, by which I mean large-scale food production, pottery workshops pointing to the division of labor. Especially in upper Egypt and the region of the Kenabend, there are at least three significant urban centers surrounded by smaller village communities that emerge and grow larger than anything found further north. These are frequently referred to in the literature as proto kingdoms, but they're really just city-states, characterized by differentiated wealth, social stratification, and complex networking with the surrounding villages. How big exactly are we talking? Well, to take one of them, Hierocompolis, it's believed that by around 3,400 or so, it was perhaps 5,000 to 10,000 people over an area of around 32 to 37 hectares. Not huge, but not tiny. And so from around 3,400 to thereabouts onwards, these city-states grow, decline, power waxes, and wanes, and eventually they coalesce, but the mechanisms aren't clear, either through warfare or other processes of elite cooperation and competition. At the same time, they're also expanding influence and control towards the north, such that by around 3,100, 3,050, the whole Nile Valley from the first cataract near Elephantine, all the way to the Mediterranean, is politically unified under the control of a single god king, the pharaohs that everyone knows and loves. But however, what I'm interested in is these three city-states and how the so-called small agri-communities from 4,000 BC transformed by around 3,500 BC into these city-state systems. This is a fairly short period of time. In particular, I'm interested in the beginning of the process, as was mentioned at the very beginning of the session this morning. If indeed the Nile Valley was populated by small, roughly egalitarian farming communities, each with a handful of households, what catalysts, what factor or factors changed this and led to settlement hierarchy, agglomeration and population shifts? Answering this is problematic for a couple of reasons. One is that the archeological record is very poor. Most of our information comes from cemeteries on the desert fringes, not the settlements themselves. There's many of the settlements have been buried under 5,000 years of cultivation. And also the pre-dionistic period, as opposed to the phara-pharaonic period, didn't really attract a lot of really focused scientific exploration until the last few decades. The other reason it's difficult to answer this question is that there's a lot of the explanations that have been put forward for the origin of cities and social complexity, both in the case of Egypt and for other ancient states, just don't hold up. There's no evidence of outside military threat. There's, while trade becomes a factor later, it doesn't seem to have played much of a role initially. It's more involved with the later polity competition. Population pressure, the record suggests that the Nile Valley was significantly underpopulated throughout the pre-dionistic period. What about environmental factors? Well, the Nile River, its associated floodplain and the boarding dry steplands was a very subsistence-friendly environment. With its annual inundation, the Nile River both refreshed and fertilized the fields. And basic basin irrigation is relatively simple. It doesn't require great collaboration. It can be controlled locally. So the grand hydraulic theories of the early 20th century don't really seem to apply here either. And geoarchaeological analysis shows that while the Nile flood did experience variability from year to year in the pre-dionistic, this is not dramatic enough to force large-scale cooperation. Rather, long periods of low Niles becomes a feature of the third millennium. So there's really no evidence that the population numbers in the early fourth millennium ever reached anything that would have put pressure on the abundant natural resources provided by the Nile ecosystem. So as one researcher has put it, there's water, there's wind, and there's sun in abundance. So the environment appears more to be a passive rather than a driving factor. And thus we need to look for internal social factors within the communities themselves. Barry Camp put forward a theoretical reconstruction of the process in his influential book from 1989, I believe it is, and you find the theory further in the second edition in 2006. And he likened the whole thing to a monopoly game. The way he paints the picture, you have an agricultural landscape of unlimited potential and players initially are all on equal footing. They compete somewhat unconsciously, maybe more unconsciously, and the initially egalitarian state doesn't last. The advantage changes from player to player through a combination of chance and personal decisions. Eventually the advantage, the advantageous position of one or more players reinforces itself. Certain players rise above in a more or less entrenched ways, others drop out from boredom or fatigue. So in Camp's version, the factors leading to growth and social complexity in early Egypt then are the landscape, chance, and personal decisions. The human portion of this being driven by an attachment to land and an innate need to compete. So in collaboration with the Computer Science Department at the University of Cape Town, we're constructing an agent-based model in an attempt to explore Camp's conceptual model in computational form, in order to explore how a combination of human decision-making, environmental factors, and chance interconnect to result in the settlement system and rise of social complexity that is witnessed in the archeological record. Especially we are interested in the challenge of modeling decision-making. Traditionally in agent-based situations in archeology, agents are imbued with a rationality that's based on biological or modern economic theories. With the understanding that the definition of rational behavior is doing what is right or makes sense from the point of view of the information possessed. This can be criticized in that rationality and subsequently decision-making are affected not just by knowledge, but also emotion, personality, character, physiology. Such traits are typically have not been incorporated into models. Feeding into a criticism that these models are unrealistic with their homogenous agents. This is put very aptly in a 1991 article where the researcher referred to a model as a cybernetic wasteland, full of agents that behave artificially with a single mindset. For example, it seems logically untenable that all farmers would farm the land to the best of their ability and all make similar decisions. What about the tired or lazy farmer or the frustrated farmer, the one who gives up? The farmer that only thinks for the short term versus the one who thinks for the long term. So recent archeological modelers have pushed back on this investigating how we can incorporate physiological and psychological motivation in order to create a diverse and more believable population. And this is very much a goal-driving R model. So I'm gonna have to rescue or skip through this, but I'm happy to answer questions later on about exactly how we designed R model. We use a program called NetLogo. It's used by many social scientists to create an abstracted version of the Nialotic Flood Plan. Our agents are households. They simply engage in farming on a yearly basis, collecting grain, consuming grain, storing grain. The variables are all the same for all the agents except for three. Where they're placed, where their settlement is placed, that's randomly set at the beginning, and then they're assigned an ambition and competence value. This is randomly set at the beginning of the run and it changes every 10 to 15 years to reflect a generational changeover, right? That kind of shifts in, it shifts in the head of the household, if you will. So just as in conceptual model, the whole run starts out with everyone having equal resources at the beginning. As I said, I think I covered this in the last one, but here's a picture of what the model would look like. This is kind of what happens step-by-step each year. The Nial Floods, we use an equation to simulate the Nial variation from year to year. Using this equation, patches of land are given a fertility value. Then depending on the agent's ambition and competence, ambition and competence affect their decisions in terms of which lands they decide to harvest, which ones they decide to claim, how successful they are at the harvest, and the likelihood that they will then repopulate and gain more population members. Households that do not make enough, harvest enough grain, they lose population numbers. Those that do and maintain a surplus, they have a greater chance of gaining population numbers. And this is to simulate the idea of the emergence of a labor force that can move around to different places. Just to show very quickly some of the results from a certain set of experiments, we ran basically three experiments. I'm just gonna step away from the microphone and point it out. So in experiment one, all of the households were all given optimal competence and ambition. In the second one, they were given slightly variation in ambition and competence, but it's only a very slight variation between them. In the third experiment, the potential variation between the households is very, very large. Not surprisingly, this is exactly what we expected. That you start to see a divergence of the population relatively quickly and it gets more entrenched and bigger as time goes on. We were a bit more surprised to notice that even in the one where all the agents are completely the same, they all have optimum ambition and competence. They're the best they can get. They can make the most of the land. It's still, you still have differentiation emerging after around 250 years, which seems to be a factor of where the settlement's located and the initial choices made in terms of planning land. And each line obviously represents a different settlement. And this is just the same data in table form and I just wanted to point out again that the numerical difference in the one where they're more or less equal, what happens is that after 500 years, the largest settlement is about twice the size of the smallest settlement. In the experiment where they have a lot of divergence and ambition and competence, the largest settlement after 500 years is 10 times the size of the smallest settlements. And this is my final slide. And the whole point of this and what we're trying to do initially, and I should point out that this is, we're at the very, very beginning stages of our project, is that we wanted just to explore and show how very small changes in agent behavior can have long-term significant outcomes. And to show how even in a landscape that is uniform for all individuals, how this can play a role in showing and in playing out what we actually see in the archeological record. Right now, we can kind of make camps theory work. And I should say we can make it work also in terms of wealth differentiation. That's one settlement on the left. You can see for each individual household, each line is a different household. You can see the wealth going up and down for different households over time. Moving forward, this is the range of possible additions, factors and parameters that we're gonna add to make the model more realistic, more complex, and hopefully more helpful. The idea being both that we can make better use of the evidence, the very poor evidence of upper Egypt and the pre-dynastic, but also push forward the intellectual discussion of the rise of urbanism in antiquity. Thank you.