Decision analysis for management judgment 4th edition pdf


 

Decision Analysis for Management Judgment (4th ed.) Paul Goodwin, George Wright. Management. Research output: Book/Report › Book. Decision Analysis for Management Judgment Third Edition Paul Goodwin The curve shown is known as a probability density function (pdf). 4th bulb. A defective OK defective OK B defective defective defective OK C OK OK defective OK. Decision Analysis for Management Judgment, 4th Edition. Paul Goodwin, George Wright. ISBN: E-UDT-E Jul pages. Select type: E-Book.

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Decision Analysis For Management Judgment 4th Edition Pdf

Solution Manual for Decision Analysis for Management Judgment, 4th Edition - Download as Word Doc .doc), PDF File .pdf), Text File .txt) or read online. By clicking the web link that we offer, you could take guide Decision Analysis For Management. Judgment: Fourth Edition (Wiley) completely. Hook up to net. Decision Analysis for Management. Judgment. Fourth Edition. Paul Goodwin. The Management School, University of Bath. George Wright. Durham Business.

No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except under the terms of the Copyright, Designs and Patents Act or under the terms of a licence issued by the Copyright Licensing Agency Ltd, 90 Tottenham Court Road, London W1T 4LP, UK, without the permission in writing of the Publisher. This publication is designed to provide accurate and authoritative information in regard to the subject matter covered. It is sold on the understanding that the Publisher is not engaged in rendering professional services. If professional advice or other expert assistance is required, the services of a competent professional should be sought. Some content that appears in print may not be available in electronic books. Includes bibliographical references and index. ISBN pbk. Decision making. Wright, George, II. G66 Perhaps decision making is considered as fundamental as breathing: essential for life, a natural and automatic process. Therefore, why study it? In this book, Paul Goodwin and George Wright show why: because research over the past 30 years has revealed numerous ways in which the process of making decisions goes wrong, usually without our knowing it.

Of course, if you failed as a consultant, you might still get another job, but it is unlikely that it would be as well paid as your current post and the loss of self-esteem would be hard to take. You are further discouraged by a colleague when you mention the idea during a coffee break. To be honest, he says, I would think that you have less than a ftyfty chance of being successful.

In our department I know of two people who have done what youre suggesting and given up after a year. If youre fed up here why dont you simply apply for a job elsewhere? In a new job you might even nd time to do a bit of consultancy on the side, if thats what you want.

Who knows? If you built up a big enough list of clients you might, in a few years time, be in a position to become a full-time consultant, but I would certainly counsel you against doing it now. By now you are nding it difcult to think clearly about the decision; there seem to be so many different aspects to consider.

You feel tempted to make a choice purely on emotional grounds why not simply jump in and take the risk? What you need is a method which will enable you to address the complexities of the problem so that you can approach the decision in a considered and dispassionate manner. This is a personal decision problem, but it highlights many of the interrelated features of decision problems in general. Ideally, you would like to maximize your income, maximize your job security, maximize The role of decision analysis your job satisfaction, maximize your freedom and so on, so that the problem involves multiple objectives.

Clearly, no course of action achieves all of these objectives, so you need to consider the trade-offs between the benets offered by the various alternatives. For example, would the increased freedom of being your own boss be worth more to you than the possible short-term loss of income? Second, the problem involves uncertainty. You are uncertain about the income that your consultancy business might generate, about the sort of work that you could get would it be as satisfying as your friend suggests?

Associated with this will be your attitude to risk. Are you a person who naturally prefers to select the least risky alternative in a decision or are you prepared to tolerate some level of risk? Much of your frustration in attempting to understand your decision problem arises from its complex structure. This reects, in part, the number of alternative courses of action from which you can choose should you stay with your present job, change jobs, change jobs and become a part-time consultant, become a full-time consultant, etc.?

For example, if you did decide to set up your own business should you then open an ofce and, if you open an ofce, should you employ a secretary? Equally important, have you considered all the possible options or is it possible to create new alternatives which may be more attractive than the ones you are currently considering? Perhaps your company might allow you to work for them on a part-time basis, allowing you to use your remaining time to develop your consultancy practice.

Finally, this problem is not yours alone; it also concerns your spouse, so the decision involves multiple stakeholders. Your spouse may view the problem in a very different way.

For example, he or she may have an alternative set of objectives than you. Moreover, he or she may have different views of the chances that you will make a success of the business and be more or less willing than you to take a risk. The role of decision analysis In the face of this complexity, how can decision analysis be of assistance?

The key word is analysis, which refers to the process of breaking something down into its constituent parts. Decision analysis therefore involves the decomposition of a decision problem into a set of smaller and, hopefully, easier to handle problems. After each smaller problem Introduction has been dealt with separately, decision analysis provides a formal mechanism for integrating the results so that a course of action can be provisionally selected.

This has been referred to as the divide and conquer orientation of decision analysis. This ability of decision analysis to provide an audit trail means that it is possible to use the analysis to produce a defensible rationale for choosing a particular option. Clearly, this can be important when decisions have to be justied to senior staff, colleagues, outside agencies, the general public or even oneself.

When there are disagreements between a group of decision makers, decision analysis can lead to a greater understanding of each persons position so that there is a raised consciousness about the issues involved and about the root of any conict.

This enhanced communication and understanding can be particularly valuable when a group of specialists from different elds have to meet to make a decision. Sometimes the analysis can reveal that a disputed issue is not worth debating because a given course of action should still be chosen, whatever stance is taken in relation to that particular issue. Moreover, because decision analysis allows the different stakeholders to participate in the decision process and develop a shared perception of the problem it is more likely that there will be a commitment to the course of action which is eventually chosen.

The insights which are engendered by the decision analysis approach can lead to other benets. Creative thinking may result so that new, and possibly superior, courses of action can be generated. The analysis can also provide guidance on what new information should be gathered before a decision is made.

Should more extensive geological testing be carried out in a potential mineral eld? It should be stressed, however, that over the years the role of decision analysis has changed. No longer is it seen as a method for producing optimal solutions to decision problems. As Keeney1 points out: Decision analysis will not solve a decision problem, nor is it intended to. Its purpose is to produce insight and promote creativity to help decision makers make better decisions.

This changing perception of decision analysis is also emphasized by Phillips:2 Applications of decision analysis. Indeed, in many applications decision analysis may be deliberately used to address only part of the problem. This partial decision analysis can concentrate on those elements of the problem where insight will be most valuable. While we should not expect decision analysis to produce an optimal solution to a problem, the results of an analysis can be regarded as being conditionally prescriptive.

By this we mean that the analysis will show the decision maker what he or she should do, given the judgments which have been elicited from him or her during the course of the analysis. The basic assumption is that of rationality.

If the decision maker is prepared to accept a set of rules or axioms which most people would regard as sensible then, to be rational, he or she should prefer the indicated course of action to its alternatives. Of course, the course of action prescribed by the analysis may well conict with the decision makers intuitive feelings. This conict between the analysis and intuition can then be explored.

Perhaps the judgments put forward by the decision maker represented only partially formed or inconsistent preferences, or perhaps the analysis failed to capture some aspect of the problem. Alternatively, the analysis may enable the decision maker to develop a greater comprehension of the problem so that his or her preference changes towards that prescribed by the analysis.

These attempts to explain why the rational option prescribed by the analysis differs from the decision makers intuitive choice can therefore lead to the insight and understanding which, as we emphasized earlier, is the main motivation for carrying out decision analysis. Applications of decision analysis The following examples illustrate some of the areas where decision analysis has been applied. The analysis has allowed them to take into account the effect on the value of the business of uncertainties such as competitors strategies, market share and market size.

Among the many benets of the approach, managers reported that it enhanced team building by providing a common language for sharing information and debate. It also led to a commitment to action so that the implementation of the selected strategy was likely to be successful.

Structuring decision problems in the International Chernobyl Project5,6 Four years after the accident at the Chernobyl nuclear power plant in the International Chernobyl Project was undertaken at the request of the Soviet authorities. Decision analysis was used in the project to evaluate countermeasure strategies for example, relocation of some of the population, changes in agricultural practice and decontamination of buildings.

The use of SMART Chapter 3 in decision conferences Chapter 12 enabled groups of people from a wide variety of backgrounds such as ministers, scientists and regional ofcials to meet together to structure the decision problem. They were thus able to clarify and elucidate the key issues associated with the strategies, such as the number of fatal cancers which they would avert, their monetary costs, the extent to which they could reduce stress in the population and their public acceptability.

By using decision analysis it was possible to evaluate the strategies by taking into account all these issues, regardless of whether they were easily quantied or capable of being measured on a monetary scale.

These included the large number of projects 53 on one occasion that were proposed, the sparseness of information and uncertainty associated with these projects and the sequential nature of the decisions.

For example, if a selected project is a technical success should a decision then be made to develop it commercially? Simple and transparent decision analysis models, using decision trees Chapter 6 , were used to Applications of decision analysis provide logic and consistency to the selection process, allowing a variety of criteria, such as risk, speed of development and sales level, to be taken into account. The models were easily able to clarify the sequences of decisions to managers and allowed uncertainties to be explicitly addressed.

Managers judged the process to be superior to the use of intuition or checklists which are often used to select research projects. Petroleum exploration decisions at the Phillips Petroleum Company8 Petroleum exploration is notoriously risky.

Scarce resources are allocated to drilling opportunities with no guarantee that signicant quantities of oil will be found. In the late s and early s the Phillips Petroleum Company was involved in oil and gas exploration along the eastern and southern coasts of the United States. In deciding how to allocate the annual exploration budget between drilling projects the companys managers faced two issues. First, they wanted a consistent measure of risk across projects.

For example, they needed to compare projects offering a high chance of low returns, with those offering a low chance of high returns. Second, they needed to decide their level of participation in joint drilling projects with other companies. For example, the company could adopt a strategy of having a relatively small involvement in a wide range of projects. The use of decision trees Chapter 6 and utility functions Chapter 5 allowed managers to rank investment opportunities consistently and to identify participation levels that conformed with the companys willingness to take on risk.

Managers also gained insights into the nancial risks associated with investment opportunities and their awareness of these risks was increased. Strategic planning in an Australian voluntary organization9 In , the Independent Living Center, an Australian voluntary organization, which provides services to people with both physical and mental disabilities, needed to develop a strategic plan.

This plan had to take into account the different perspectives of members of the Centers management committee, which consisted of volunteers, representatives of health professions and clients.

Options such as maintaining the status quo, forming a lobby group to raise the Centers prole and reorganizing the Center into separate services were identied by members of the committee in a decision conference Chapter They then used SMART Introduction Chapter 3 to identify the criteria that would distinguish between the strategies and to determine which strategy was most appropriate in the light of these criteria. The criteria included the nancial costs and risks of the strategies, the extent to which they enabled the Center to meet all demands from clients and the time that would be required for implementation.

Through decision analysis the group developed a shared understanding of the key issues that would affect the future of the organization and conicting opinions within the committee were resolved. Supporting the systems acquisition process for the US military10 In the past the acquisition process for major military systems in the United States has been subject to much criticism because it did not produce defensible decisions underpinned by sound analyses and a clear rationale.

As a result, decision analysis techniques like SMART Chapter 3 have been increasingly widely used to structure decision making at the various stages of the process. For example, when the US Army air defense community needed to establish the most cost-effective mix of low-altitude air defense weapons, decision analysis was used to help a group, consisting of both technical experts and senior ofcers, to rank alternative weapon mixes. The process enabled a large number of criteria to be identied e.

Where decisions involved several organizations, the decision model was found have a valuable role in depoliticizing issues. Prioritizing projects in a busy UK social services department11 Kent Social Services Department is responsible for the provision of services to the elderly, mentally handicapped, mentally ill, physically handicapped and children and families in south-eastern England.

In the late s managers in the Department were facing an increasing workload with insufcient resources to handle it. The result was resource log-jams, random-seeming displacement of previously understood priorities, foreshortened deadlines, and an overall sense of overload and chaos.

It enabled the many Applications of decision analysis attributes associated with a project, such as benets to the service, monetary costs, workload involved and political pressures, to be assessed and taken into account. However, the key benets were seen to emanate from the process itself. It allowed a problem which had been a fermenting source of unrest [to be] brought to the surface, openly accepted to be a problem and shared. As a result the undercurrent of discontent was replaced by enthusiasm for action.

One problem involved the selection of a wide area network WAN for interconnecting around sites in Britain. Seven alternative proposals needed to be considered. The decision was complicated by the need to involve a range of people in the decision process e. By using decision conferencing Chapter 12 together with SMART Chapter 3 the team were able to agree a choice and recommend it with condence to the companys board.

Planning under a range of futures in a nancial services rm ATM Ltd a pseudonym provides the electromechanical machines that dispense cash outside many of the banks and building societies in the UK.

Auto-teller machines, as they are called, are ATMs main products. However, several of the executives at ATM were concerned that the use of cash might be in swift decline in the European Union, since smart cards cards similar to debit cards but which store electronic cash were being promoted by a competitor in the nancial services sector.

The executives did not feel able to predict the year in which cash transactions would cease to be signicant, nor did they feel able to assess the potential rate of decline. By using scenario planning Chapter 15 , they felt able to identify critical driving forces which would accelerate or decelerate the move away from cash. As a result, they felt better placed to anticipate and cope with an unfavorable future if such a future did begin to unfold. Such decisions are difcult because they involve many issues that are likely to have differing levels of importance.

For example, in the EC decision, issues such as effects on industry, agriculture, national security, the environment and national culture needed to be addressed. The MPs found that the approach enabled them to generate ideas and structure the problems so that irrelevant or insignicant arguments were avoided in their decision making. Automating advice-giving in a building society front ofce Home Counties Building Society a pseudonym took advantage of deregulation in the UK nancial services sector and investigated the possibility of offering tailored nancial products such as pension plans at point-of-sale in their high street branches.

They found that tailoring nancial products to client characteristics, although theoretically straightforward, would not be practicable given the limited expertise of counter staff. One solution was to capture the expertise of the senior pensions adviser and deliver it via an expert system Chapter 17 on a front-ofce desk. A clerk could type in client details and chat while the system matched the best pension plan, printed a hard copy of the details, and explained in plain English the specic advantages of the recommended plan for the particular client.

Allocating funds between competing aims in a shampoo manufacturing company14 The managing director of an operating company which manufactures and markets a well-known brand of shampoo in a particular country had been asked by head ofce to justify his very large advertising budget.

The managers responsible for distribution, advertising and promotion met with support staff and advertising agency representatives in a decision conference Chapter However, the insights revealed by a SMART model transformed their thinking and the problem was then seen as one of improving the allocation of funds between distribution, advertising and promotion in order to achieve the objectives of growth, leadership Overview of the book 11 and prot.

An EQUITY resource allocation model Chapter 13 enabled the participants to evaluate the costs and benets of combinations of strategies from each expenditure area. This led to agreement on an action plan which was implemented within a month.

Overview of the book The book is organized as follows. Chapter 2 discusses the biases that can arise when unaided decision makers face decision problems involving multiple objectives. Chapter 3 then shows how decision analysis can be used to help with these sorts of problems. The focus of this chapter is on problems where there is little or no uncertainty about the outcomes of the different courses of action.

Decision Analysis for Management Judgment

Uncertainty is addressed in Chapter 4, where we show how probability theory can be used to measure uncertainty, and in Chapter 5, where we apply probability to decision problems and show how the decision makers attitude to risk can be incorporated into the analysis.

As we saw at the start of this chapter, many decisions are difcult to handle because of their size and complex structure. In Chapters 6 and 7 we illustrate methods which can help to clarify this complexity, namely decision trees, inuence diagrams and simulation models.

Of course, all decisions depend primarily on judgment. Decision analysis is not designed to replace these judgments but to provide a framework which will help decision makers to clarify and articulate them. The conclusion from this thought experiment is that the human mind has a limited capacity for complex calculations and that technological devices, such as calculators, complement our consciously admitted cognitive limitations.

This assumption underpins all of the decision analysis methods that are covered later in this book, but what happens if decision makers are not aware of their cognitive limitations and make decisions without using these methods?

According to research by psychologists decision makers have a toolbox of available strategies and they are adaptive in that they choose the 16 How people make decisions involving multiple objectives strategy that they think is most appropriate for a particular decision.

Simon1 used the term bounded rationality to refer to the fact that the limitations of the human mind mean that people have to use approximate methods to deal with most decision problems and, as a result, they seek to identify satisfactory, rather than optimal, courses of action. These approximate methods, or rules of thumb, are often referred to as heuristics. Simon, and later Gigerenzer et al.

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For example, suppose a decision maker knows that the best guide to the quality of a university is its research income. Suppose also that this is a far better guide than any other attribute of the university such as quality of sports facilities or teaching quality or any combination of these other attributes.

In this environment a prospective student who chooses a university simply on the basis of its research income is likely to choose well the simple heuristic would be well matched to the decision-making environment.

Quick ways of making decisions like this which people use, especially when time is limited, have been referred to as fast and frugal heuristics by Gigerenzer and his colleagues. We will rst look at the heuristics which can be found in most decision makers toolboxes and then we will consider how people choose heuristics for particular decision problems. Heuristics used for decisions involving multiple objectives When a decision maker has multiple objectives the heuristic used will either be compensatory or non-compensatory.

In a compensatory strategy an options poor performance on one attribute is compensated by good performance on others. For example, a computers reliability and fast processor speed may be judged to compensate for its unattractive price.

This would not be the case in a non-compensatory strategy. Compensatory strategies involve more cognitive effort because the decision maker has the difcult task of making trade-offs between improved performance on some attributes and reduced performance on others. The recognition heuristic The recognition heuristic2 is used where people have to choose between two options. If one is recognized and the other is not, the recognized Heuristics used for decisions involving multiple objectives 17 option is chosen.

For example, suppose that a manager has to choose between two competing products, but he or she has not got the time or motivation to search for all the details relating to the products. If the manager recognizes the name of the manufacturer of one of them, but not the other, then they may simply choose the product whose manufacturer they recognize. This simple heuristic is likely to work well in environments where quality is associated with ease of recognition.

Decision Analysis for Management Judgment (4th ed.)

It may be that a more easily recognized manufacturer is likely to have been trading for longer and be larger. Its long-term survival and size may be evidence of its ability to produce quality products and to maintain its reputation.

Interestingly, the recognition heuristic can reward ignorance. A more knowledgeable person might recognize both manufacturers and therefore be unable to employ the heuristic. If ease of recognition is an excellent predictor of quality then a less knowledgeable person who recognizes only one manufacturer will have the advantage. Of course, the heuristic will not work well when ease of recognition is not associated with how good an option is. The minimalist strategy2 In this heuristic the decision maker rst applies the recognition heuristic, but if neither option is recognized the person will simply guess which is the best option.

In the event of both options being recognized then the person will pick at random one of the attributes of the two options. If this attribute enables the person to discriminate between the two options they will make the decision at this point. If not, then they will pick a second attribute at random, and so on. For example, in choosing between two digital cameras, both of which have manufacturers which are recognized by the decision maker, the attribute possession of movie shooting modes may be selected randomly.

If only one camera has this facility then it will be selected, otherwise a second randomly selected attribute will be considered. Take the last2 This is the same as the minimalist heuristic except that people recall the attribute that enabled them to reach a decision last time when they had a similar choice to make. If this attribute does not allow them to discriminate between the options this time then they will choose the attribute that worked the time before, and so on.

If none of the previously used attributes works, then a random attribute will be tried. However, in some circumstances the decision maker may be able to rank the attributes in order of importance.

For example, in choosing a car, price may be more important than size, which in turn is more important than top speed. In this case the decision maker can employ the lexicographic heuristic. This simply involves identifying the most important attribute and selecting the alternative which is considered to be best on that attribute.

Thus the cheapest car will be downloadd. In the event of a tie on the most important attribute, the decision maker will choose the option which performs best on the second most important attribute size , and so on. This ordering of preferences is analogous to the way in which words are ordered in a dictionary hence the name lexicographic. For example, consider the words bat and ball.

They both tie on the rst letter and also tie on the second letter, but on the third letter ball has precedence. Like the earlier heuristics the lexicographic strategy involves little information processing i. Despite this, like the recognition heuristic it can work well in certain environments for example, when one attribute is considerably more important than any of the others or where information is scarce.

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However, when more information is available, the decision will be based on only a small part of the available data.

In addition, the strategy is non-compensatory. With deeper reection, a decision maker might have preferred an option that performed less well on the most important attribute because of its good performance on other attributes. For example, when you go shopping you might adopt the following semilexicographic decision strategy: If the price difference between brands is less than 50 cents choose the higher quality product, otherwise choose the cheaper brand.

Consider the alternatives below. This implies that you will prefer A to C, but a direct comparison of A and C using the strategy reveals that C is preferred. This set of choices is therefore contradictory. More formally, it violates a fundamental axiom of decision analysis that is known as transitivity which states that if you prefer A to B and B to C then you should also prefer A to C.

Thus a strategy, which on the face of it seemed reasonable, is irrational in that it contains inherent contradictions. Any alternative falling below this point is eliminated. The process continues with the second most important attribute and so on. For example, suppose that you want to download a car and have a list of hundreds of cars that are for sale in the local paper. By continuing in this way you eventually narrow your list to one car and this is the one you choose.

Clearly, EBA is easy to apply, involves no complicated numerical computations and is easy to explain and justify to others. In short, the choice process is well suited to our limited information processing capacity. However, the major aw in EBA is its failure to ensure that the alternatives retained are, in fact, superior to those which are eliminated. This arises because the strategy is non-compensatory. In our example, one of the cars might have been rejected because it was slightly below the cc cutoff value.

Yet its price, service history and mileage were all preferable to the car you downloadd. These strengths would have more than compensated for this one weakness. The decision makers focus is thus on a single attribute at a time rather than possible trade-offs between attributes. Thus, all the cars, in the earlier example, were available at the same time.

In some situations, however, alternatives become available sequentially. For example, if you are looking for a new house you might, over a period of weeks, successively view houses as they become available on the market. Herbert Simon,7 has argued that, in these circumstances, decision makers use an approach called satiscing.

The key aspect of satiscing is the aspiration level of the decision maker which characterizes whether a choice alternative is acceptable or not. Imagine that your aspiration level is a job in a particular geographical location with salary above a particular level and at least three weeks paid holiday per year. Simon argues that you will search for jobs until you nd one that meets your aspiration levels on all these attributes. Once you have found such a job you will take it and, at least for the time being, conclude your job search.

Consider also the decision problem of selling your home. Offers for download are received sequentially and remain active for only a limited period of time. If you do not accept an offer within a short period of it being made then the prospective downloadr may follow up other possibilities. Reconsider also downloading a used car. Cars are on show in many different showrooms scattered across town, and advertisements for private sales appear in the local newspaper every night.

Should you look at each car? How would you solve these decision problems? Simon7 would argue that in the house sale example you would wait until you received a satisfactory offer. Similarly, in the car download example, you would continue looking until you nd a car that is satisfactory to you. To quote, in a satiscing model, search terminates when the best offer exceeds an aspiration level that itself adjusts gradually to the value of the offers so far received. In the job search problem, if you are offered, and accept, a satisfactory job it is still possible that you might have found a better job if you had been willing to make further job applications and go for further interviews.

It is also possible that when you started the job search process your expectations were unreasonably high such that you might, at an early stage, delay accepting, or refuse, what objectively might be an excellent job. A subsequent unsuccessful job search may lower Heuristics used for decisions involving multiple objectives 21 your aspiration level such that you fall back on what is now seen as an acceptable alternative or are forced to accept a job offer that is less desirable than a job you earlier refused as unsatisfactory.

Note also that satiscing is yet another example of a non-compensatory strategy. In the job search example, there were no considerations of how much holiday you would be prepared to give up for a given increase in salary.

The nal choice also depends on the order on which the alternatives present themselves. If you are searching for a car to download, the car you choose will probably be different if you decide to change the order in which you visit the showrooms. Simons satiscing theory is most usefully applied to describe sequential choice between alternatives that become available and indeed may become unavailable as time passes; however, it may also be adopted in situations where, although all the alternatives are in theory available simultaneously, the alternatives are so numerous that it would be impossible in practice to consider them all in detail.

Reason-based choice Reason-based choice offers an alternative perspective on the way people make decisions. According to Shar et al. Reason-based choice can lead to some unexpected violations of the principles of rational decision making. First it can make the decision maker highly sensitive to the way a decision is framed.

For example, consider the following two candidates, A and B, who have applied for a job as a personal assistant. Their characteristics are described below: Candidate A Average written communication skills Satisfactory absenteeism record Average computer skills Reasonable interpersonal skills Average level of numeracy Average telephone skills Candidate B Excellent written communication skills Very good absenteeism record Excellent computer skills Awkward when dealing with others Poor level of numeracy Poor telephone skills Note that candidate A is average or satisfactory on all characteristics, while in contrast B performs very well on some characteristics, but 22 How people make decisions involving multiple objectives very poorly on others.

Research by Shar9 suggests that, if the decision is framed as which candidate should be selected? A selection decision will cause people to search for reasons for choosing a particular candidate and Bs excellent communication skills, very good absenteeism record and excellent computer skills will provide the required rationale.

If instead the decision is framed as which candidate should be rejected? Hence positive features are weighted more highly when selecting and negative features more highly when rejecting. This violates a basic principle of rational decision making that choice should be invariant to the way the decision is framed. Another principle of rational decision making is that of independence of irrelevant alternatives.

If you prefer a holiday in Mexico to a holiday in France you should still prefer the Mexican to the French holiday, even if a third holiday in Canada becomes available. Reason-based decision making can lead to a violation of this principle.

You have the choice between: a downloading the camera now or b waiting until you can learn more about the cameras that are available. You have no problem in deciding to download the camera you can nd a compelling reason to justify this in the cameras remarkably low price. Option a is clearly preferable to option b. You now have conict between the cheaper Canon and the more expensive, but sophisticated, Nikon. According to research by Tversky and Shar,10 many people would now change their mind and opt to wait in order to nd out more about available cameras.

This is because it is difcult to nd a clear reason to justify one cameras download over the other. The availability of the Nikon camera has caused you to reverse your original preference of downloading the Canon rather than waiting.

A nal interesting consequence of reason-based decision making is that if an option has some features that are only weakly in its favor, or irrelevant, this can actually deter people from selecting that option. For example, in one study,11 people were asked to choose between two brands of cake mix which were very similar in all features, except that the rst carried an offer to download a collectors plate which most people did not want. The offer signicantly lowered the tendency to choose the Summary 23 rst brand.

Apparently this was because it was difcult to nd reasons to justify this worthless bonus and people felt that they might be exposed to criticism if they chose the option that was associated with it. Factors that affect how people make choices How do people select from their toolbox of heuristics? The key factors appear to be: i the time available to make the decision, ii the effort that a given strategy will involve, iii the decision makers knowledge about the environment, iv the importance of making an accurate decision, v whether or not the decision maker has to justify his or her choice to others and vi a desire to minimize conict for example, the conict between the pros and cons of moving to another job.

Payne et al. When a given level of accuracy is desired they attempt to achieve this with the minimum of effort and use one of the simpler heuristics. Where greater weight is placed on making an accurate decision then more effort will be expended.

There is also evidence that people often use a combination of strategies. When faced with a long list of alternatives they use quick, relatively easy methods to eliminate options to obtain a short list.

Then they apply more effortful strategies to select the preferred option from the short list. In addition, a requirement to justify a decision to others is likely to increase the likelihood that reason-based choice will be used.

Summary This chapter has reported studies of how unaided decision makers make choices when they want to achieve several objectives. In these circumstances people tend not to make trade-offs by accepting poorer performance on some attributes in exchange for better performance on others. However, recent research has suggested that peoples decisionmaking skills are not as poor as was once believed.

In particular, the work of Gigerenzer et al. Nazareno Tuttuparupazzescu. Everson Murungweni. Sufian Tan. Fez Research Laboratory.

More From Maria Cabral. Maria Cabral.

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