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Dr. hab. Jerzy Supernat Institute of Administrative Studies University of Wrocław Decision-making Techniques.

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Presentation on theme: "Dr. hab. Jerzy Supernat Institute of Administrative Studies University of Wrocław Decision-making Techniques."— Presentation transcript:

1 dr. hab. Jerzy Supernat Institute of Administrative Studies University of Wrocław Decision-making Techniques

2 Decision-making Techniques Elements of decision problem  The decision body.  The decision options (courses of action).  The uncontrollable factors.  The consequences. dr. hab. Jerzy Supernat

3 Decision-making Techniques The decision body  single decision maker  multi decision-maker decision body dr. hab. Jerzy Supernat There are very few decisions which can be reached by a single decision maker with total disregard for others’ views. Even when the formal procedures of an organizations dictate that an individual has the responsibility for making the decision, the views of interested parties will usually need to be sought and the tacit agreement or acquiescence of other individuals and groups obtained. Clearly the implication is that all members of the decision body do not have the same degree of influence on a decision.

4 Decision-making Techniques dr. hab. Jerzy Supernat Where collective decisions over matters of common concern have to be taken, the collegium system is traditionally adopted. The system requires that the individual's judgments should be pooled in such a way as to make sure that:  the group always bears in mind its (agreed) objectives  every member of the group participates  all relevant information is made available to every member of the group  a majority vote determines the ultimate choice The approach is designed to ensure that factors other than those contained within the immediate decision situation do not impinge on the choice process. It is not, however, uncommon for historical residues to produce coalitions or antagonisms within decision bodies, leading to choices being made other than on the strict merits of the case.

5 Decision-making Techniques dr. hab. Jerzy Supernat Groupthink Groupthink occurs when a group makes faulty decisions because group pressures lead to a deterioration of „mental efficiency, reality testing, and moral judgment” (Irving L. Janis)

6 Decision-making Techniques dr. hab. Jerzy Supernat Symptoms of groupthink according to Irving L. Janis:  Illusion of invulnerability – Creates excessive optimism that encourages taking extreme risks.  Collective rationalization – Members discount warnings and do not reconsider their assumptions.  Belief in inherent morality – Members believe in the rightness of their cause and therefore ignore the ethical or moral consequences of their decisions.  Stereotyped views of out-groups – Negative views of ‘enemy’ make effective responses to conflict seem unneces-sary.  Direct pressure on dissenters – Members are under pressure not to express arguments against any of the group’s views.  Self-censorship – Doubts and deviations from the perceived group consensus are not expressed.  Illusion of unanimity – The majority view and judg-ments are assumed to be unanimous.  Self-appointed ‘mindguards’ – Members protect the group and the leader from information that is problematic or contradictory to the group’s cohesiveness, view, and/or decisions.

7 Decision-making Techniques dr. hab. Jerzy Supernat The decision options Decision options are the alternative courses of action between which the decision body must choose. Options lie at the heart of decision-making because, unless there is more than one way to proceed, then there is no choice to be made and therefore no decision. The number of options in a decisional problem can be anything between two and infinity (one type of decision where the options are always infinite is the case where the decision variable is continuous).

8 Where to elect there is but one, ‘tis Hobson's choice: take that or none. Thomas Ward ( ) dr. hab. Jerzy Supernat Decision-making Techniques

9 The Hobson behind Hobson's Choice lived in Cam- bridge, England during the late 16th and early 17th cen- turies. Licensed to carry passengers, parcels, and mail between Cambridge and London, Thomas Hobson kept a stable of about forty high quality horses. As a sideline, he also rented out his horses to university students. After students began requesting particular horses again and again, the liveryman realized certain horses were being overworked. That inspired Hobson to come up with a new system of rotating the horses for hire. Hobson gave customers looking for horses the choice of taking the one nearest the stable door or taking none at all. dr. hab. Jerzy Supernat Decision-making Techniques

10 dr. hab. Jerzy Supernat The other major characteristic of decision options concerns how discernible they are at the start of the decision process. Some decision problems have options which are obvious when the problem is defined. In other decision problems, the precise nature of the options is not immediately apparent. In fact, the options within a decision problem can turn out to be a mixture taken from a continuum which goes between totally defined at the beginning of the decision process and completely novel and developed specifically for the decision in question. Henry Mintzberg classifies decision options by whether they are: given – fully developed at the start of the decision process found ready made – fully developed in the environment of the decision and discovered during the decision process modified – ready-made options with some customized features custom made – developed especially for the decision in question

11 Decision-making Techniques dr. hab. Jerzy Supernat The uncontrollable factors Uncontrollable factors are those parts of the decision problem which, although having an influence on the final outcome, cannot be controlled directly by the decision body. They may be treated as alternative states of nature (or scenarios), i.e. states which the environment takes after, and independent of, the decision itself. When there is only one uncontrollable factor, the total possible states of nature will correspond to all states which that particular uncontrollable factor can take. When more than one uncontrollable factor is involved there could be a state of nature corresponding to every possible combination of the levels which the uncontrollable factors can take.

12 Decision-making Techniques dr. hab. Jerzy Supernat When considering the uncontrollable factors within a decision problem, it is useful to take the three following steps:  identify the factors which will influence the final consequence of a decision  identify the states or levels which each uncontrollable factor could take  attempt to predict the likelihood of these states or levels occurring for each of the uncontrollable factors

13 Decision-making Techniques dr. hab. Jerzy Supernat The consequences For each combination of a course of action and the state of nature, there will be a consequence. Thus, if we have N alternative courses of action and M mutually exclusive states of nature there will be N x M possible consequences. Figure in the next slide illustrates this as a matrix in which the two dimensions are the courses of action and the alternative states of nature.

14 Decision matrix Probability p1p1 p2p2...pjpj pmpm F D F1F1 F2F2...FjFj FmFm D1D1 C 11 C 12...C 1j...C 1m D2D2 C 21 C 22...C 2j...C 2m... DiDi C i1 C i2...C ij...C im... DnDn C n1 C n2...C nj...C nm dr. hab. Jerzy Supernat Decision-making Techniques

15 dr. hab. Jerzy Supernat The decision rules (techniques) 1.The pessimistic decision rule. 2.The optimistic decision rule. 3.The regret decision rule. 4.The expected value decision rule. 5.The expected utility decision rule.

16 Decision-making Techniques dr. hab. Jerzy Supernat The pessimistic decision rule In this case each course of action should be analyzed, and the worst possible outcome for that course of action should be identified. Next the decision-maker should select the course of action providing the best of the worst possible outcomes.

17 Decision-making Techniques The pessimistic decision rule dr. hab. Jerzy Supernat

18 Decision-making Techniques dr. hab. Jerzy Supernat The optimistic decision rule In this case each course of action should be considered, and the best possible outcome for that course of action identified. Next the decision-maker should choose the course of action yielding the best of the best possible outcomes.

19 dr. hab. Jerzy Supernat Decision-making Techniques The optimistic decision rule

20 Should a decision-maker always be a total optimist? Total optimism means taking into account only the best outcome for each course of action. Decision-making Techniques

21 A decision-maker who behaves rationally should consider two things:  the best outcomes and  the worst outcomes modifying their weight (meaning) according to his/her optimism (and pessimism). He/she can do it applying the coefficient of optimism. Decision-making Techniques

22 Calculations based on the coefficient of optimism of 0.6 (we often accept 0.5, i.e. the half way point between total pessimism and total optimism) are as follows: dr. hab. Jerzy Supernat Decision-making Techniques

23  the higher the coefficient of optimism the higher the decision-maker’s hope for obtaining the best possible outcome: the coefficient of optimism of 1 leads to the behavior of a total optimist  the lower the coefficient of optimism, the higher the fear of the decision-maker of receiving the worst possible outcome: the coefficient of optimism of 0 leads to behavior of a total pessimist dr. hab. Jerzy Supernat Decision-making Techniques

24 dr. hab. Jerzy Supernat The regret decision rule The regret decision rule is based on a deceptively simple but extremely useful question: If we choose one particular course of action, then, in hindsight, how much we do regret not having chosen what turned out to be the best course of action given a particular set of circumstances?

25 Calculated values of regret are in brackets. It’s a matter of convention that regret is presented in positive numbers. dr. hab. Jerzy Supernat Decision-making Techniques

26 After calculating the values of regret we are left with the regret table. dr. hab. Jerzy Supernat Decision-making Techniques

27 Now one has to choose the best course of action by applying the pessimistic decision rule to the regret table and choosing the course of action with minimum of maximum regrets. dr. hab. Jerzy Supernat Decision-making Techniques

28 dr. hab. Jerzy Supernat Inconsistency in the regret decision rule The regret decision rule is a powerful and intuitively attractive idea. It attempts to minimize the embarrassment we might feel of making the wrong decision. It is closely related to the economist’s traditional concept of the opportunity cost of a decision: i.e. by choosing one alternative course of action, what opportunity are we forgoing by not choosing another course of action?

29 Unfortunately, as a decision rule the concept has a major disadvantage: if we are choosing the course of action which will give us the least cause for regret when compared with another option, then the degree of regret will depend upon which other options are considered. This can bring about problems of logical inconsistency. In order to illustrate this inconsistency let’s move to the next slide. dr. hab. Jerzy Supernat Decision-making Techniques

30 The analysis of the problem below shows the best course of action from a regret rule viewpoint is process A. Decision-making Techniques dr. hab. Jerzy Supernat

31 Now, let’s assume that an additional process (process C) has been elaborated allowing us to obtain the same result. Analysis of the problem, taking into account the new process, shows that D 2, in this case, is the better option – previously being the worst one! dr. hab. Jerzy Supernat Decision-making Techniques

32 dr. hab. Jerzy Supernat The expected value decision rule This rule weighs each outcome by the probability (or likelihood) of its occurrence. The expected value is the weighted average of the possible results anticipated from a particular course of action where the weights are the probabilities. After calculating the expected value for each option, the decision-maker should choose the course of action with the maximum expected value. It should be emphasized that expected values are, in themselves totally hypothetical figures. In reality, the calculated expected values on slide 76 will never actually occur. The values will be any of the figures illustrated in the table but never the expected figure. The expected values are merely an indication of the value of each option.

33 Probability p 1 =0.1p 2 =0.2p 3 =0.5p 4 =0.2 EV i FDFD F1F1 F2F2 F3F3 F4F4 D1D2D3D4D5D1D2D3D4D dr. hab. Jerzy Supernat Decision-making Techniques Decision matrix

34 Probability p 1 =0.1p 2 =0.2p 3 =0.5p 4 =0.2 EV i FDFD F1F1 F2F2 F3F3 F4F4 D1D2D3D4D5D1D2D3D4D dr. hab. Jerzy Supernat Decision-making Techniques

35 Example The decision maker is deciding whether or not to under- take one of two contracts (A or B) offered to him. Each contract can lead only to three possible outcomes. The probabilities and outcomes are as follows: dr. hab. Jerzy Supernat Decision-making Techniques

36 dr. hab. Jerzy Supernat Decision-making Techniques

37 Our example using a decision tree. Decision-making Techniques dr. hab. Jerzy Supernat

38 In order to reach the optimal decision, one should analyze the decision tree from right to left (the roll-back techni- que). Fundamental rules:  The expected value should be calculated for each outcome branch.  The branch with the higher expected value should be chosen at each decision node (there is only one decision node in our example). dr. hab. Jerzy Supernat Decision-making Techniques

39 Calculations for each outcome branch: EV 1 = 80 x x (-30) x 0.3 = 40 EV 2 = 50 x x (-10) x 0.2 = 32 Calculated expected values can be placed above the relevant outcome nodes. dr. hab. Jerzy Supernat Decision-making Techniques

40 Decision tree with the expected values. dr. hab. Jerzy Supernat Decision-making Techniques

41 Moving from right to left we reach the (starting) decision node where the decision maker must choose one of three courses of action with calculated expected values (for the course of action D 3 – signing neither of the contracts – profit equals zero). Replacing outcome branches with their equivalents in the form of expected values leads to the reduction of the decision tree. dr. hab. Jerzy Supernat Decision-making Techniques

42 Reduced decision tree. dr. hab. Jerzy Supernat Decision-making Techniques

43 Using the expected value decision rule, the decision maker should choose D 1, and cut off D 2 and D 3, as presented below: || dr. hab. Jerzy Supernat Decision-making Techniques

44 When the analyzed decision problem pops up repeatedly in the static decisional situation choosing D 1 (concluding contract A) arises no doubts. Choosing D 1 in each case gives the decision maker – in the long term – the highest outcome. The expected value decision rule is fully justified when the decision process can be repeated many times in the same stable set of circumstances. dr. hab. Jerzy Supernat Decision-making Techniques

45 However, static decision situations are rare. In practical terms dynamic situations are prevalent and, therefore, one-off decisions are not made twice or more times in identical situations. In our example the decision maker might fear a loss of 30 (with probability of 0.3) connected with contract A (choosing the worse option – from the expected value decision rule – contract B, he risks only 10 with probability of 0.2). Decision-making Techniques dr. hab. Jerzy Supernat

46 The expected utility decision rule As the previous slides indicated in the case of one- off decision the proper analysis should not be the one using the expected value decision rule. Rather the analysis taking into account the decision body’s preferences, in other words, the analysis applying the expected utility decision rule. Utility is a relative value of possible outcomes taking into account the preferences of the decision-maker. dr. hab. Jerzy Supernat Decision-making Techniques

47 In descending order, there are seven possible outcomes in our example: 80, 50, 30, 10, 0, -10, -30 (0 corresponds to choosing neither contract). Because the scale of a utility function is discretio- nary, we can define utilities (U) of extreme outcomes as follows: U (80) = 1 iU (-30) = 0 Next it is necessary to determine the utility of the five other possible outcomes. dr. hab. Jerzy Supernat Decision-making Techniques

48 In order to accomplish this, we can ask the decision- maker to make a choice of two possibilities:  the first, being the certain outcome (in sequence 50, 30, 10, 0, -10)  the second: a gamble on outcomes 80 with probability p and -30 with probability 1-p. 80 p 50 (certain) p (a gamble on outcomes 80 with proba- bility p and -30 with probability 1-p) dr. hab. Jerzy Supernat Decision-making Techniques

49 At p = 0 the decision maker will choose 50, but increasing the probability of winning 80 we will reach a spread of probability making both possibilities equally good for the decision-maker. This could happen at probability of 0.9 winning 80 and probability of 0.1 of loosing p (certain) p dr. hab. Jerzy Supernat Decision-making Techniques

50 U (50) = U (80) x U (-30) x 0.1 U (50) = 1 x x 0.1 U (50) = 0.9 Applying the already known utilities, corresponding with the extreme outcomes of the game, and having establish- ed the spread of probability, we can now calculate a utility of 50 (or utility of the game, being the expected value of utilities of game outcomes at the established spread of probability): Decision-making Techniques dr. hab. Jerzy Supernat

51 Repeating the procedure for the remaining outcomes could show that the probability that makes the decision- maker indifferent is for 30 as high as 0.8: 80 p (certain) p U (30) = U (80) x U (-30) x 0.2 = 1 x x 0.2 U (30) = 0.8 for 10 as high as 0.5 for 0 as high as 0.3 for -10 as high as 0.15 dr. hab. Jerzy Supernat Decision-making Techniques

52 OutcomeUtility Utilities of all outcomes: dr. hab. Jerzy Supernat Decision-making Techniques

53 U 1 (contract A) = 1 x ,5 x x 0.3 U 1 = 0.65 U 2 (contract B) = 0.9 x x x 0.2 U 2 = 0.72 U 3 (neither contract A, nor contract B) = 0.3 x 1 U 3 = 0.3 The expected value analysis used earlier can now be repeated, only using utility values instead of monetary outcomes. We calculate the expected utility for each possible course of action (D 1 – make contract A, D 2 – conclude contract B and D 3 – undertake neither contract) as follows: Decision-making Techniques dr. hab. Jerzy Supernat

54 Analysis of the utility of outcomes points to contract B as the optimal decision. Moving from values expressed in money to their utilities (taking into account preferences of the decision-maker) has brought the change of the decision. dr. hab. Jerzy Supernat Decision-making Techniques

55 Concluding Remark dr. hab. Jerzy Supernat Once you make a decision, the universe conspires to make it happen. Ralph Waldo Emerson


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