Like every other commodity, the value of the metals is subject to variation' (Ricardo, 1817). Mineral commodity price uncertainties have always been a significant issue in mining business due to the close relation between global economic conditions and commodity prices, which commonly generates significant and unexpected price fluctuations. Price forecasts have been driven by economic theory, which states that in the long-run market will be balanced and that prices tend to the marginal cost of production. However, this theory has been misunderstood in the mining industry and future prices through time, in real terms, are assumed to be unchanged. This is unrealistic due to regardless of the cycling nature of economy and the omission of uncertainties. Furthermore, using historical data in traditional price forecast approaches does not guarantee realistic predictions as past events will not necessarily be repeated at the same intervals and with the same intensity. Mining, finance and economic sectors have made significant research efforts to predict prices and control uncertainties through classic linear, Gaussian and probabilistic simulation approaches. However, by definition, uncertainties cannot be measured or accurately modelled. Uncertainties can be identified to measure and manage the associated risks; therefore, uncertainties in mining projects have been historically misinterpreted, erroneously measured and poorly controlled. It is widely known that supply and demand functions drive market prices. While technological development noticeably influences supply, demand is strongly driven by psychological factors, mainly related to customer preferences and lifestyle expectations. Thus, human behaviour is one, or if not, the most important variable to determine commodity prices in a competitive market. During the last decades, alternative methodologies have been used to study uncertainties in human, environmental and market behaviours. Chaos theory, fractals, artificial intelligence and econophysics have been widely and successfully used to investigate complex dynamic systems to address uncertainties over time in the fields of neurosciences, meteorology, aviation and market trading. The application of these techniques in the market suggests a more realistic approach to the business environment. Through the combination of the methodologies above a novel long-run price uncertainties simulation approach that emphasise the differences between risks and uncertainties is proposed in this paper. CITATION:Tapia Cortez, C, Saydam, S, Coulton, J and Sammut, C, 2015. Copper price uncertainties - chaos theory to manage risks in mining projects, in Proceedings Third International Future Mining Conference, pp 261-272 (The Australasian Institute of Mining and Metallurgy: Melbourne).