Computational Modeling
Archaeology is a theoretical science that requires the use of models (explanations of how variables fit together) to develop expectations that we can compare to archaeological data. A model is a representation of something, plain and simple. A model can specify how variables fit together, how one part of a system connects or affects another, what behaviors produce a certain kind of material signature, etc. It is an abstraction of reality that captures some aspect of reality.
The distinction between an archaeologist who is “using models” and one who is not is a false one: all archaeologists use models. We all use models to make the leap between the static remains we have in front of us and the human behaviors that produced those remains. We are all trying to “model our way out of the problem:” the problem is that we cannot directly observe the people, societies, dynamics, and behaviors we are trying to understand. That's archaeology.
While we all use models of one kind or another, there are choices about what kind of models one chooses to use. Many of the models that archaeologists use are mental models that are based on logic or some intuitive understanding of how things “should” be related. Joshua Epstein (2008) makes a useful distinction between implicit models and explicit models:
“. . . an implicit model in which the assumptions are hidden, their internal consistency is untested, their logical consequences are unknown, and their relation to data is unknown. But, when you close your eyes and imagine an epidemic spreading, or any other social dynamic, you are running some model or other. It is just an implicit model that you haven't written down (see Epstein 2007).”
The paper from which that quote is taken is available from JASSS here. It is short and non-technical.
My approach to understanding the past takes advantage of formal computational models, specifically agent-based models (ABMs). ABMs let you bridge the “scale gap” between the operational scales of behavior that we can understand through ethnographic observation and evolutionary scales of system change that we are often trying to understand through archaeology. Given what we know about all kinds of complex systems, the relationships between those levels are not likely to be simple. The decisions, behaviors, and interactions of individual people and families do not "map up" to the system level in a simple way. If they did, the stock market would be a very easy thing to understand and predict.
In an agent-based model, we can represent the human behaviors that we can document ethnographically (such as mobility, marriage, kinship, and social learning among hunter-gatherers) as “rules” for human-level behavior. We can create a system populated by actors who behave and interact according to these rules. We can create a model "world" that has spatial features and environmental conditions that affect behavior. We can set the system in motion, letting the members of the population interact for whatever period of time we want across whatever span of space we want. We can observe the characteristics of the system that emerge as a result of the human-level interactions that are taking place. We can also design the model to record aspects of system-level behavior that we can compare to archaeological data.
Even if we know what kinds of behaviors we're interested in, designing an ABM is not necessarily a simple thing to do. The art of modeling is the art of making choices about what things to include and how to represent those things. The more detailed a model is, the less generalizable it is: more specific models are generally less useful for representing a wide variety of cases. At the essence of my philosophy for computer modelling (a philosophy which many others share) is KISS: Keep it Simple Stupid. What is the minimum that is required to reproduce the phenomenon of interest? I have to admit, though, that KISS is an easier philosophy to espouse than it is to follow. It is always tempting to add more things in to a model. And it is hard to take them out once they're in there.
The distinction between an archaeologist who is “using models” and one who is not is a false one: all archaeologists use models. We all use models to make the leap between the static remains we have in front of us and the human behaviors that produced those remains. We are all trying to “model our way out of the problem:” the problem is that we cannot directly observe the people, societies, dynamics, and behaviors we are trying to understand. That's archaeology.
While we all use models of one kind or another, there are choices about what kind of models one chooses to use. Many of the models that archaeologists use are mental models that are based on logic or some intuitive understanding of how things “should” be related. Joshua Epstein (2008) makes a useful distinction between implicit models and explicit models:
“. . . an implicit model in which the assumptions are hidden, their internal consistency is untested, their logical consequences are unknown, and their relation to data is unknown. But, when you close your eyes and imagine an epidemic spreading, or any other social dynamic, you are running some model or other. It is just an implicit model that you haven't written down (see Epstein 2007).”
The paper from which that quote is taken is available from JASSS here. It is short and non-technical.
My approach to understanding the past takes advantage of formal computational models, specifically agent-based models (ABMs). ABMs let you bridge the “scale gap” between the operational scales of behavior that we can understand through ethnographic observation and evolutionary scales of system change that we are often trying to understand through archaeology. Given what we know about all kinds of complex systems, the relationships between those levels are not likely to be simple. The decisions, behaviors, and interactions of individual people and families do not "map up" to the system level in a simple way. If they did, the stock market would be a very easy thing to understand and predict.
In an agent-based model, we can represent the human behaviors that we can document ethnographically (such as mobility, marriage, kinship, and social learning among hunter-gatherers) as “rules” for human-level behavior. We can create a system populated by actors who behave and interact according to these rules. We can create a model "world" that has spatial features and environmental conditions that affect behavior. We can set the system in motion, letting the members of the population interact for whatever period of time we want across whatever span of space we want. We can observe the characteristics of the system that emerge as a result of the human-level interactions that are taking place. We can also design the model to record aspects of system-level behavior that we can compare to archaeological data.
Even if we know what kinds of behaviors we're interested in, designing an ABM is not necessarily a simple thing to do. The art of modeling is the art of making choices about what things to include and how to represent those things. The more detailed a model is, the less generalizable it is: more specific models are generally less useful for representing a wide variety of cases. At the essence of my philosophy for computer modelling (a philosophy which many others share) is KISS: Keep it Simple Stupid. What is the minimum that is required to reproduce the phenomenon of interest? I have to admit, though, that KISS is an easier philosophy to espouse than it is to follow. It is always tempting to add more things in to a model. And it is hard to take them out once they're in there.
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ForagerNet3_Demography (Version 3)
The ForagerNet3_Demography model is a non-spatial ABM designed to serve as a platform for exploring several aspects of hunter-gatherer demography:
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