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About TESLA

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. Certain information may be regarded as having more influence over the decision that other types of information. In addition, a multitude of different sources may be used to provide the information, which must be recorded.

TESLA is a software tool which aims to support decision makers when faced with such problems. It provides a means to break the decision down into a hierarchical structure, simplifying the problem and presenting it in such a way that information can be easily gathered and categorised. TESLA does not automate the decision-making process but provides valuable support to the decision maker.

Key features are:
  • An easy-to-use graphical interface with a clear indication of the decision components;
  • The storage of information sources within decision trees, providing a full audit trail for the decision;
  • Decisions are presented in a completely transparent manner;
  • The provision of graphical analysis tools, helping the decision-maker to identify problem areas and regions with the greatest effect on the decision outcome.
TESLA provides a framework for a number of decision-making methods. These are described in more detail below.

Evidence Support Logic

Click here for an overview of Evidence Support Logic in TESLA
Evidence Support Logic (ESL) is designed to identify the amount of uncertainty or conflict involved in the decision. The technique involves analysing a hypothesis representing the decision, rather than the decision itself. The hypothesis is broken down into a number of sub-hypotheses, which in turn can be broken down further until a stage is reached where evidence can be gathered for the lowest sub-hypotheses. For example, the decision "Should I take an umbrella?" could be represented by the hypothesis "It is going to rain". This hypothesis can be broken down into sub-hypotheses such as "The weather forecast predicts rain" and "It is already raining". Evidence for these lower-level hypotheses is readily available.

ESL uses 3-value logic. Experts assign values to each bottom-level hypothesis representing the amount of supporting evidence, the amount of refuting evidence and the amount of uncertainty or conflict in the evidence. Conventional 2-value logic does not take uncertainty into account and so cannot identify areas of weakness in the data, unlike 3-value logic. Once these values have been decided upon, the evidence and uncertainty or conflict are propagated up through the tree to the root hypothesis.

  • Visualisation tools help to identify areas of large uncertainty and regions where reducing this will have a large impact on the overall solution;

  • Areas can be highlighted to target research;

  • The optional extra, Advanced Evidence Support Logic (AESL), extends the ESL method by giving the user greater control over decision characteristics such as the quality of evidence, the amount of overlap in evidence, etc.
To learn more about the mathematics behind ESL, please refer to the TESLA User Guide.

The document Evidence Support Logic: A Guide for TESLA Users describes in more detail the process of elicitating evidence and constructing a decision tree.

Multi-Attribute Analysis

The technique of Multi-Attribute Analysis (MAA), or Multi-Criteria Decision Analysis (MCDA), is designed to aid the decision maker in choosing the best-suited option from a number of different options.

The overall decision is broken down into a number of categories. For example, if the decision is choosing the best site for a new development, this can be broken down into sub-categories such as cost, environmental impact, local support and so on. These categories can be broken down even further; cost could, for example, be split into transportation costs and labour costs. For each of the lowest level sub-categories, the options are awarded a score out of 100 by a panel of experts, with the highest score indicating the best-suited option for that sub-category. The scores are then propagated through the decision tree and an overall score is calculated for each option.

  • Categories can be weighted to reflect importance;

  • Scores can be mapped to a qualitative scheme, e.g. "Very Good", "Good" or "Poor";

  • Visualisation tools help to identify the most important categories in the decision;

  • Decision choices (e.g. weighting of categories) that have little bearing on the outcome can be easily identified.
To learn more about the mathematics behind MAA, please refer to the TESLA User Guide.

TESLA-Excel

The ESL component of TESLA is also available as an add-on to Microsoft Excel. Please email us for further details.

Consultancy Services

Decision making doesn't stop with the software. At Quintessa we have a number of experienced consultants who can help you to construct the decision model. Not only do we provide training for the use of the software, but we can also set up and act as facilitators at workshops designed to provide input into the decision.

More Information

For more information about TESLA or the consultancy services provided by Quintessa, please contact tesla@quintessa-online.com.