These activities build on the foundation of relevant data and its interpretation, and a comprehensive understanding of the breadth of issues upon which the decisions depend.

Open and transparent decisions need to be based on evidence. However, in the evaluation of complex situations there are often multiple and sometimes conflicting sources of evidence in addition to uncertainty. To address these issues, Quintessa has developed an approach founded on ESL (Evidence Support Logic) and supported by our TESLA software. The aim is to assist decision makers to document the logic and justification that underpins their reasoning, to identify and visualise potential sources of bias and their implications, and to provide a well-documented audit trail back to the raw data. Complex decisions or considered judgements are typically informed by a wide range of factors, drawing on multiple information sources. There may be a large amount of information available, but it needs to be structured in a useful way before it can become part of the decision-making process.

ESL is a tool that supports decision makers when faced with such problems. It provides a means to break a decision down into a hierarchical structure, simplifying the problem and presenting it in such a way that information can be easily gathered and categorised and enabling the optimisation of data gathering based on what is most important for the decision at hand.

ESL has been used to help solve problems in several different industries, in particular oil and gas, carbon capture and sequestration, and radioactive waste management activities; in the latter case, a particular focus has been on helping decisions on high-hazard legacy facilities, the management of which is subject to significant uncertainty. It has also been used to underpin portfolio selection for major investment decisions within these industries. Underpinned by our unique TESLA decision visualisation software, the rationale for the decision can be clearly articulated and linked back to the underpinning evidence and judgements.

Further information about ESL can be found in Evidence Support Logic: A Guide for TESLA Users.