One of the biggest burdens carried by archaeologists is the ubiquity of uncertainty in most aspects of the discipline. Chronometry, sampling biases and the indirect inference of past human behaviour are perhaps the most relevant examples. While some theoretical discussion on the implication of these problems has been addressed in the past, practical solutions have been relatively few. Uncertainty is in fact often measured and acknowledged, but a direct integration of its implications in the analytical domain has been rare.

The treatment of uncertainty can be generally distinguished in two main stages. The first part involves its quantification, often involving the adoption of a probabilistic description of the empirical data. The second stage is characterised by the formal and quantitative integration of such knowledge into the analytical workflow, which will ideally translate into outputs where quantified patterns can be classified according to degrees of uncertainty.  Such outcome could then be an integral part of the archaeological narrative, providing a framework for distinguishing what we know for sure from what we are less certain about.

This paper will tackle the second aspect, focusing on case studies of spatial and temporal analysis. It will explore how probabilistic data obtained from a wide variety of methods (e.g. radiocarbon dating, aoristic analysis, probabilistic categorisation etc.) can be integrated to analytical workflows that are traditionally not designed to deal with such type of input. The proposed solution is to fully exploit the available information by using Monte-Carlo Simulation methods. Results of such technique will offer a distribution of possible alternative histories that can be inferred from the current state of knowledge. Statistical summary of such output will then provide the likelihood of occurrence for each alternative event.

Applications of the proposed method will be shown for three different case studies: 1) inferences on populations dynamics based on pithouse counts; 2) spatio-temporal variation in settlement density; and 3) analysis of the settlement rank size distribution.