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Appendix: Selected Software for Decision Analysis

J.B. Hardaker CAB International PDF

Appendix:

Selected Software for

Decision Analysis

In developing the examples in this book, we made use of the following software:

@Risk and RiskOptimizer from Palisade Corporation. Available at: http://www.palisade.com

(­ accessed 2 June 2014). data, later replaced with TreeAge Pro from TreeAge Software, Inc. Available at: http://www.treeage. com (accessed 2 June 2014).

GAMS from GAMS Development Corporation. Available at: http://www.gams.com (accessed

2 June 2014).

Logical Decisions from Logical Decisions. Available at: http://www.logicaldecisions.com (accessed

2 June 2014).

Microsoft Excel, a component of Microsoft Office from Microsoft Corporation. Available at: http:// www.microsoft.com/en-au/default.aspx (accessed 2 June 2014).

ModelRisk from Vose Software. Available at: http://www.vosesoftware.com (accessed 2 June 2014).

Solver for Excel from FrontlineSolvers. Available at: http://www.solver.com (accessed 2 June 2014).

WhatsBest! from Lindo Systems Inc. Available at: http://www.lindo.com (accessed 2 June 2014).

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5: Attitudes to Risky Consequences

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5

Attitudes to Risky

Consequences

Introduction

In Chapter 2 we have shown how a simple risky decision problem, such as that faced by the dairy farmer thinking about insuring against foot-and-mouth disease (FMD), can be solved. The key step was to transform the risky consequences of an event fork into the DM’s certainty equivalents (CEs). However, the assessment of CEs can become very tedious if there are many such risky event forks. Moreover, the introspective capacity needed to decide on CEs rises with the number of branches emerging from the fork.

As explained in Chapter 2, the central notion in decision analysis is to break this assessment of consequences into separate assessments of beliefs about the uncertainty to be faced, and of relative preferences for consequences. In Chapters 3 and 4 we dealt with the former of these assessments. Now it is time to look in more detail at how preferences for consequences can be assessed and how those preferences can be encoded.

In Chapter 2 we laid the theoretical foundation for this chapter on utility theory via the presentation of the axioms of the subjective expected utility hypothesis. Readers might find it useful to review the

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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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12: Strategies Decision Makers Can Use to Manage Risk

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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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11: Risky Decision Makingand Time

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11 

Risky Decision Making and Time

The Time Factor in Decision Analysis

In most of the earlier chapters, decision analysis has been set in the framework of a short time horizon.

In reality, however, farm and agribusiness managers make decisions about production, marketing and finance not just for the present but also for the longer run. For these long-term decisions, accounting for the factor ‘time’ is essential. Some ways of dealing with time in risky decision making are addressed in this chapter.

One important effect of accounting for time is that uncertainty generally increases the further into the future we look. Consequently, the need to account for risk is often greater in decision making for the long run. Unfortunately, accounting for time and for the greater uncertainty that is thereby entailed adds greatly to the complexity of analysis. As we shall explain, there are difficulties in extending the methods of decision analysis to long-run planning. Nevertheless, we believe that ways of considering such decisions that embody at least some key elements of the principles and methods outlined in earlier chapters are likely to lead to better decisions than those emerging from analyses that ignore risk or that seek to accommodate it only in some over-simplified way.

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