A simple decision model that introduces the 10 ingredients of good decisions, the grammar of decision making, and a five-step framework for creating decision models.

Understanding the common depth structure of all organisations reveals differences in the complexity of tasks and types of decisions made by managers and their subordinates, whose individual capabilities may or may not match the requirements of their roles. Also, accountability and authority must be clear for effective decision-making.

• Evaluating options of the same type, though differing in the levels of value they create for achieving the objectives, defines Multi Criteria Decision Analysis modelling (MCDA). Scoring and weighting is illustrated with a women’s shampoo case study, while disvalue is illustrated with an analysis of the harms of misusing drugs.

Multi-Criteria Decision Analysis (MCDA)

This third section made clear that structure and content are driven by process. Each of these three blocks includes unique features, but also interacts with the other two as decision-analytic modelling is applied to a practical problem. You might find places in your organisation that could benefit from some combination of the 10 ingredients, which you could deploy by applying the appropriate processes (Figure EP.2).

• Values are numbers that represent the extent to which objectives are achieved by taking a decision. They express our likes and dislikes. There are many ways of identifying objectives and criteria that are the standards against which achievement is judged. We show how the numbers are assessed, and interpret their meaning as values.

• Evaluating options that are of different types to create portfolios of actions that meet all objectives for the available resources requires making trade-off between the types as well as criteria. The added complexity of MCDA is first described for the women’s shampoo case study, then applied to two major projects: prioritising R&D projects; and designing the UK Navy’s Type 45 destroyer.

The many tasks of the facilitator, as distinct from those of a work group’s leader, are defined and developed, including dealing with conflict. Decision analysis works for the client because the model acts as a transitional object, holding and containing the client’s uncertainty as the model is explored, new insights develop, and preferences are constructed.

As these five chapters make clear, the social and technical aspects of creating a decision-analytic model are deeply intertwined. Indeed, a decision conference is itself a socio-technical system and although it is a (fairly) closed system, its impact will permeate the larger system from which its participants were drawn as they return to the workplace aligned with a new sense of common purpose.

• A brief introduction to Bayesian statistics, showing how uncertainty expressed as probabilities should be revised as more information is received, and illustrated with an example of how data revised researchers’ degrees of belief about the effects of medical cannabis on epilepsy in children. This is followed by a major project showing how Bayesian Belief Networks can assist underwriters assess risk premiums.

A brief history of working with groups of experts in a decision conference led to the development of requisite decision models that are sufficient in form and content to resolve the decision maker’s sense of unease. The stages from an initial meeting with the client, preparation, the decision conference itself, to a follow-through are described.

Not everything we know about numbers can be interpreted as true of what they represent, so this chapter explores how far we can go in applying the numbers to real problems, and how we can be sure they are meaningful.