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7: Decision Analysis with Preferences Unknown

J.B. Hardaker CAB International PDF

7 

Decision Analysis with

Preferences Unknown

Efficiency Criteria

A difficulty often encountered in applying the SEU model lies in the elicitation of the DM’s utility function. The problem may be lack of access to the relevant person, inadequate introspective capacity on that person’s part, or the fact that more than one person may be involved. Similarly, in agriculture, it may be necessary to develop recommendations for a particular target group of farmers numbering perhaps some hundreds or even thousands. Efficiency criteria have been devised to allow some ranking of risky alternatives when the specific utility function (or functions) is not available.

Efficiency analysis depends on making some assumptions about preferences or, equivalently, about the nature of the utility function. Often bounds are placed on the level of risk aversion. Then, for all DMs to whom the assumptions apply, the various actions can be divided into an efficient set and an inefficient set. The inefficient set contains those actions that are dominated by (preferred less than) actions in the efficient set. The efficient set contains those actions that are not dominated. The optimal action for any individual will lie among the alternatives in the efficient set, provided that:

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12: Strategies Decision Makers Can Use to Manage Risk

J.B. Hardaker CAB International PDF

Strategies Decision Makers

Can Use to Manage Risk

12 

Introduction

We have emphasized that risk is everywhere and is substantially unavoidable. It follows that

­management of risk is not something different from management of other aspects of a farm, since every farm management decision has risk implications. There are, however, some types of farm management decisions that bear strongly on the riskiness of farming, and some of these are reviewed in this chapter. The treatment is general because, as we have shown, every decision should be considered in the context of the particular circumstances, notably the beliefs and preferences of the DM. Therefore, specific prescriptions about strategies to manage risk are seldom possible. Instead, we canvass some of the main areas where DMs can act to manage risk and indicate how choices in some of these areas might be analysed.

As outlined in Chapter 1, there are two reasons why risk in agriculture matters: risk aversion and downside risk. Moreover, we have argued that, at least in capitalist agriculture, the latter will often be at least as important as the former since extreme risk aversion by relatively wealthy DMs is irrational and unlikely to exist, at least for important risky choices. In the light of this view, it might seem natural to draw a distinction between management strategies that deal with risk aversion and management strategies that deal with downside risk. That, however, does not work well because effective strategies to manage downside risk will also have benefits in terms of increased utility for risk-averse DMs.

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6: Integrating Beliefs and Preferences for Decision Analysis

J.B. Hardaker CAB International PDF

6 

Integrating Beliefs and

Preferences for Decision Analysis

Decision Trees Revisited

In Chapter 2 we introduced the notion of a decision tree to represent a risky decision. Recall that decision problems are shown with two different kinds of forks, one kind representing decisions and the other representing sources of uncertainty. We represented decision forks, where a choice must be made, by a small square at the node, and we represented event forks, the branches of which represent alternative events or states, by a small circle at the node. We showed how a decision tree can be resolved working from right to left, replacing event forks by their certainty equivalents (CEs) and selecting the optimal branch at each decision fork.

We now return to the simple example relating to insurance against losses from foot-and-mouth disease (FMD) to show how probabilities and utilities are integrated into the analysis. For convenience, the original decision tree developed in Chapter 2 (Fig. 2.2) is repeated here as Fig. 6.1. Note that the uncertainty about the future incidence of the disease is represented in the tree by the event fork with branches for ‘No outbreak’ and ‘Outbreak’. To measure the uncertainty here we need to ask the farmer for subjective probabilities for these two events. Suppose that, as explained in Chapter 3, the farmer assigns a probability of 0.94 to there being no outbreak and a complementary probability of 0.06 to an outbreak occurring. Similarly, the farmer is uncertain about what policy for control of the disease might be implemented if an outbreak occurs, as shown by the event forks further to the right in Fig. 6.1. Again, the farmer is able to assign some subjective values to these conditional probabilities of 0.5 and 0.5

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2: Decision Analysis: Outline and Basic Assumptions

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Decision Analysis: Outline and Basic Assumptions

2

Basic Concepts

As explained in Chapter 1, decision analysis is the name given to the family of methods used to r­ ationalize and assist choice in an uncertain world. In this chapter we focus on the concepts and methods of decision analysis. Figure 2.1 provides an outline of the typical steps in decision analysis of a risky choice. These stages are discussed in turn below.

Establish the context

This first step is concerned with setting the scene and identifying the parameters within which risky choice is to be analysed. Particularly in a large organization, it may be important to take note of the level in the organizational structure at which the choice will be made. For example, different sorts of decisions may be made at different levels, perhaps with important strategic issues decided upon at board level, with key tactical decisions made by senior management and with a range of more routine choices made at the operational level. Identifying the level may lead to identifying the decision maker (DM) or makers – a critical need for proper conduct of the steps to follow. Similarly, it will be important to identify the stakeholders – those who will be affected by the outcomes of the decision. For a family farm, the principal stakeholders are farm family members who typically will be concerned with their standard of living and the continued survival of the family business.

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10: Decision Analysis with Multiple Objectives

J.B. Hardaker CAB International PDF

10 

Decision Analysis with

Multiple Objectives

Introduction with Some Examples

In earlier chapters we have defined utility functions that indirectly embody an important trade-off:

­expected monetary return versus variance. Such a utility function represents a preference model for choice that captures the DM’s attitude to expected return and variance. Obtaining high returns and reducing exposure to variability are usually two conflicting objectives in decision making. We have shown in

Chapters 5 and 7 how to model the preference trade-off between these objectives. In many situations, however, the action chosen depends on how each possible choice meets several objectives, as the following examples show.

A dairy farmer has become concerned about some long-term negative impacts of the current system of milk production on the farm and is therefore considering changing this system. The current production system is a high-input/high-output system. Large amounts of resources are used per cow to produce a high milk yield. In the short term, this system gives the farmer a good income and a high status in the local community. However, because of its intensive nature, it may cause some environmental problems in the future, as well as some problems with cow health and welfare. In thinking about changing the production system, the dairy farmer might consider diverse possible objectives such as the following: (i) maximizing current farm income; (ii) maximizing farm income in the future; (iii) minimizing environmental damage; (iv) maximizing animal health and welfare;

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