The logic of the herd: A computer simulation of archaeological herd structure

The advent of new techniques of dental ageing has permitted inferences about the age-structures of ancient animal populations on the basis of faunal remains. Unfortunately, inference from archaeological populations of faunal elements to the age-structure of ancient herds is fraught with both sampling problems and logical difficulties. The relationship between a living herd—a dynamic system characterized by growth—and a static archaeological population is more complex than that assumed by current models based on kill-off patterns and survivorship curves. Using a computer simulation, these logical relationships are explored, and the effects of herd growth on the composition of live and death populations are evaluated. By employing the simulation within a deductive framework, constrained by ethnographically derived criteria for reproduction, mortality, and economic viability in the Near East, it is possible to determine whether certain archaeological kill-off patterns could represent viable herding systems. Examples were found to vary widely in terms of both demographic plausibility and economic viability. Some implications for the possible course of ova-caprine domestication in the Near East are discussed.     

Elimination of Source A and B Errors in p-Median Location Problems

The p-median problem is a powerful tool in analyzing facility location options when the goal of the location scheme is to minimize the average distance that demand must traverse to reach its nearest facility. It may be used to determine the number of facilities to site, as well as the actual facility locations. Demand data are frequently aggregated in p-median location problems to reduce the computational complexity of the problem. Demand data aggregation, however, results in the loss of locational information. This loss may lead to suboptimal facility location configurations (optimality errors) and inaccurate measures of the resulting travel distances (cost errors). Hillsman and Rhoda (1978) have identified three error components: Source A, B, and C errors, which may result from demand data aggregation. In this article, a method to measure weighted travel distances in p-median problems which eliminates Source A and B errors is proposed. Test problem results indicate that the proposed measurement scheme yields solutions with lower optimality and cost errors than does the traditional distance measurement scheme.