Explaining Known Past Routes, Underdetermination, and the Use of Multiple Cost Functions

Explaining material traces of movement as proxies for past movement is fundamental for understanding the processes behind why people in the past traversed the landscape in the way that they did. For this, least-cost path analysis and the use of slope-based cost functions for estimating the cost of movement when walking have become commonplace. Despite their prevalence, current approaches misrepresent what these cost functions are, their relationship to the hypotheses that they aim to represent, and their role in explanation. As a result, least-cost paths calculated using single cost functions are liable to spurious results with limited power for explaining known past routes, and by extension the decision-making processes of past people. Using the ideas of multiple model idealisation and robustness analysis, and applied via a tactical simulation, this study demonstrates that similar least-cost paths can be produced from slope-based cost functions representing both the same hypothesis and different hypotheses, suggesting that least-cost path results are robust but underdetermined under the tested environmental settings. The results from this tactical simulation are applied for the explanation of a Roman road in Sardinia. Using probabilistic least-cost paths as an approach for incorporating multiple cost functions representing the same hypothesis and error in the digital elevation model, it is shown that both model outcomes representing the minimisation of time and energy are unable to explain the placement of the Roman road. Rather, it is suggested that the Roman road was influenced by pre-existing routes and settlements.