Introduction

The archaeological record shows that foragers varied their stone tool raw material preferences, even when several types of stone material were available. The changing use, and co-use of different stone tool raw materials is well known from a wide range of environmental and climatic contexts, time-periods, and ‘cultures' (Andrefsky 1994; Bamforth 1990; Bar-Yosef 1991; Clark 1980; Jelinek 1991; Kuhn 2004, 1991).

S. Oestmo (is) · C.W. Marean

School of Human Evolution and Social Change, Institute of Human Origins,

Arizona State University, 900 S. Cady Mall, Rm. 233, Tempe, AZ 85281, USA e-mail: soestmo@asu.edu

C.W. Marean

e-mail: Curtis.Marean@asu.edu

M.A. Janssen

School of Sustainability, Center for Behavior, Institutions, and the Environment,

Arizona State University, Tempe, USA

e-mail: Marco.Janssen@asu.edu

C.W. Marean

Faculty of Science, Nelson Mandela Metropolitan University,

Port Elizabeth, Eastern Cape 6031, South Africa

© Springer International Publishing Switzerland 2016 175

J.A. Barcelo and F. Del Castillo (eds.), Simulating Prehistoric and Ancient Worlds, Computational Social Sciences, DOI 10.1007/978-3-319-31481-5_4 frequency include climate/environmental change and its co-variability with mobility and procurement strategies (Ambrose and Lorenz 1990; Binford and Stone 1985; Kuhn 2004), selection of certain raw materials for their physical properties (Braun et al. 2009; Gould and Saggers 1985; Minichillo 2006), changes in demography (Clark 1980), the preference for appearance or color (Akerman et al.

Brantingham (2003) challenges these explanations and provides a neutral model that was argued to explain some of the observed patterns in the record of raw material abundance. Brantingham argues that in order to demonstrate the deliberate selection of raw materials, patterning must be shown to be different from the results of the neutral model, which provides a baseline for comparison where archaeolo­gists can be certain that observed raw material patterns is not the result of strategic selection.

We agree with Brantingham's sentiment. However, Brantingham (2003: 505) points out that a “...appropriate criticism of the present model would suggest that a forager “could” never engage in a random-walk foraging strategy and “could” never ignore the differences between stone raw material types.” Here we explore if such a criticism is valid. In addition, we follow Brantingham's suggestion that quantitative development of the observations presented in his study requires calibration of the agent-based model to run in simulated “worlds” built around the known geographic distributions of actual raw material sources. Here we partly address that suggestion by first exploring two major limitations of the neutral model as currently described. First, the raw material sources are distributed randomly without any clustering across the model landscape, which is not the case on most real landscapes where potential raw material source locations are controlled by the underlying geological structure and geophysical processes. An example of such structures and processes, drawn from our research region, are coastal cliffs and embayed beaches that can produce cobble beaches along a stretch of coastline (Thompson and Marean 2008). Source locations thus appear clustered due to the geological structure and geo­physical processes of the landscape.

The second limitation addressed here is the unrealistic assumption that each raw material location in the model represents a unique raw material. Five thousand raw material sources are possible over an extended landscape but not 5000 unique raw materials. It is more likely that a smaller amount of different raw materials, say 1-25, are represented by the 5000 source locations. In addition, the 1-25 unique raw materials are not randomly distributed in isolation away from same type raw materials. As discussed above, not only are source locations clustered due to the underlying geological structure and geophysical processes, several sources of the same material can be available in a cluster, depending on the geological formation.

4.2