Simulation of stone raw material use in silico.

"The experimental verification of evolutionary mechanisms is a challenging undertaking for several reasons: most organisms have comparatively long generation times; there are difficulties in determing important parameters, such as mutation rates or fitness values; and the large variances inherenent in evolution lead to poor statistical signficance in averages."

Wilke and Adami (2002) The biology of Digital Organisms Trends in Ecology and Evolution 17: 528-532.

Like geology, paleontology and evolutionary biology, archaeology desires to be an experimental science where hypotheses can be tested and results replicated under controlled conditions.

Social systems, both past and present, are often bewilderingly complex and do not easily submit to experimentation in vivo because, as WIlk and Adami point out, generation times are too long, the number of system components is too large, the interactions between these components are difficult to tease appart, or, in the case of archaeology, the ravages of site preservation or time-averaging have obscured our view. Simulation modeling--experimenation in silico--alows us to abstract individual components of these complex systems and submit them to rigorous testing.

I use agent-based models as a way of bringing experimentation into the heart of archaeological theory building and model testing. Simulation modeling, along with more traditional mathematical techniques, contributes to the growth of archaeology in a number of ways. Most importantly, it requires analytical rigor and therefore forces us to proove the connections between behavioral, ecological and evolutionary theory and archaeological observations.

I favor an approach to modeling that begins with as few variables as possible, adding new complexities only if absolutely necessary. I also favor an approach that uses null, or neutral models to provide a foundation for archaeological inference.

A “neutral” assumption posits equivalence between the elements in a system; for example, we might define different cultural traits as having equal fitness and thus determine that they are equally likely to be adopted when occurring at equal frequencies; or we might assume that two stone raw material types have equivalent fracture properties and thus are equally likely to be procured when encountered.

The importance of using neutral modeling assumptions is not that they lead to simple system dynamics; on the contrary, they can lead to very complex dynamics. Rather, neutral assumptions establish a controlled modeling environment where we can be sure that observed system dynamics are not the product of fitness differences, selection or adaptation. The application of null models in archaeology provides a basis for estimating statistical confidence. In principle, when archaeological patterns deviate from null expectations, then we can be more confident (and even quantify our level of confidence) that observed patterns reflect some form of adaptation.

Current domains of simulation research...

• Paleolithic hunter-gatherer foraging behavior
  
• Stone raw material use
  
• Design and reduction of stone technologies
  
• Costly signalling in multi-agent populations
  
• Criminal foraging behavior

I use a combination of tools for simulation.

Java -- The core object oriented programming libraries.

RePast -- Specialized Java libraries for agent-based modeling.

JMSL -- A Java library of mathematical and statistical objects.

GeoTools -- Java GIS objects.

IntelliJ Idea -- Integrated development environment for Java.