Weights representing trade-offs show how a little more on one criterion can be equated to a little less on another. Methods for assessing the weights are described, including properly assessing criteria weights in hierarchical models.

The six types of decision problem for which decision modelling is appropriate are previewed, each illustrated with a real case study. A key discriminator is whether the problem is mainly about multiple objectives, or uncertainty, or a mixture of both.

In decision analysis, probabilities are defined as representing the assessor’s uncertainty about the outcome of an event. We explore the rules of probability, explain how to assess good probabilities, and suggest instances when proper scoring rules can assist.

Facilitated decision modelling with groups of experts enables many heads to be better than one if experts are chosen for their diversity of views and ability to perform at the highest level. The enabling conditions for groups to be effective are summarised from laboratory research and observational studies, including the characteristics of high-performing teams.

• Three types of consultancy require different relationships with clients, and Edgar Schein’s 10 principles of process consultancy skills are reinterpreted as appropriate for the practising decision analyst. Developing trustworthiness and a helping relationship are the keys to effective decision support.

Event trees show decisions to take now that can mitigate subsequent unfavourable events defining the risk of a situation, whereas fault trees assume an unfavourable event has occurred, with decisions to be taken that reduce the undesirable consequences and their likelihood of occurring if a fault occurs in a complex system. Two case studies provide examples. Scenario analysis provides a way to understand deep uncertainty.

• MCDA can assist negotiators as they attempt to find which of the stands on the controversial issues are acceptable to both parties. A simple example is given to demonstrate how asymmetries of interests can be quantified, revealing mutually acceptable solutions. Several real-life applications by the US Government are followed by a report on how oil spillage by tankers at sea benefited from this type of model.

The people who started decision theory, how they identified the key quantifiable ingredients of good decisions: values, trade-offs and probabilities, then turned this mathematical beginning into the applied discipline of decision analysis.

• Thinking strategically means considering what and why before deciding how and when, shaped by context, the organisation’s mission and vision, focused by strategic intent, and made practical by intermediate goals or challenges. A case study shows how this approach helped an umbrella organisation to reshape its future to better serve its members in providing health care. A final case study integrated three model types enabling the US response to unrest in the Middle East.

Decision trees show sequences of decision and event outcome nodes, and can describe consequences with multiple objectives or criteria. Case studies describe a choice problem of a junior engineer, and a board-level decision about a possible new product. Structural relationships between the act-event-outcome ingredients are emphasised by relevance diagrams.

In the 1970s, two senior underwriters from a major UK insurance company attended an executive course on decision analysis I had been teaching at Brunel University. At the end of the course, they told me that decision analysis makes explicit all the things an underwriter does implicitly when assessing a risk. Since then, I’ve heard the same thing from experts in many areas, which reinforces my view that the 10 ingredients of good decisions make intuitive sense as well as being derived from a theoretical analysis of coherent preferences. Thus, they provide the elements of a framework for helping a decision maker in almost any field do a better job of creating the future.