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This comprehensive guide introduces the fundamentals of decision analysis, behavioral decision making, game theory, and negotiation analysis. Learn from the four approaches to decision-making, understand PrOACT methodology, types of errors, and how to evaluate consequences and make tradeoffs effectively. Explore the importance of identifying problems, setting objectives, generating creative alternatives, and handling uncertainty and risk. Enhance your decision-making skills with practical advice and valuable insights shared in Raiffa's book.
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Outline • Fundamentals of • Introduction • Decision analysis • Behavioral decision making • Game theory • Negotiation analysis
Introduction • Four approaches to making decisions: • How an individual should and could make decisions (Prescriptive) • Psychology of how individuals actually make decisions (Descriptive) • How individuals should make separate interactive decisions (Normative) • How groups should and could make joint decisions (Prescriptive) • Raiffa’s book is intended to help us make better decisions and takes all these approaches into consideration
Decision Analysis • Generic Decisions • PrOACT • Uncertainty and Risk • Risk Profiles • Ordinal Ranking • EMV (Expected Monetary Value) • EDV (Expected Desirability Value) • Expected Utility Value
Generic Decisions • Choices, Occasions, Situations: • Decide to negotiate • What if the negotiations fail? • Go to court or accept a settlement • Whom do you negotiate with? • Use an arbitrator? • Choose a style of negotiation • Lesson – Learn the theory and practice prescriptive decision making
PrOACT • Identify the Problem • Clarify the Objectives • Generate creative Alternatives • Evaluate the Consequences • Make Tradeoffs • Lesson – as opposed to reactive: waiting for the other party to do something
Types of errors • When someone makes a statement to you, there are four possible results • You accept the statement as true and it is indeed true • You do not accept the statement and it is false • You accept the statement and it is false • You do not accept the statement and it is true • There is no error for the first two cases • In case three you make an error of the second kind, often called a beta error • In case four you make an error of the first kind or alpha error • Some refer to solving the wrong problem as the third kind of error
Some advice about identifying problems • Think about what caused you to realize that you had to make a decision • Consider solving problems other than the immediate one you face • Question constraints (That’s impossible, I can’t do that, she’ll never go for it, etc.) • Get advice
Objectives • Objectives and interests are synonymous • Think about them broadly rather than narrowly • People have a tendency to look at only a few ramification of what they want to get out of a situation • Why do you want this outcome? • What do mean by your stated objective? • Lesson – brainstorm a list, then whittle it down
Creative Alternatives • There are many ways to get to most objectives • The more of them you consider, the more likely you are to achieve your objectives • Here again, brainstorming lists is very good, but don’t forget to reduce them as well • Alternatives are external to negotiations, options are internal
Evaluating consequences • How good is alternative X with regard to objective Y? • Remember that you have multiple objective and a given alternative will have different effects on each • Construct a matrix as on page 19 of Raiffa – this is called conditional analysis • If we see that one alternative is at least as good or better than another with respect to all objectives, we say that it dominates. (PPP dominates QQQ)
Evaluating consequences – Making Tradeoffs • If it is possible to express all objectives with one measure, we call it costing out. This makes a direct comparison possible. Quite frequently, the one measure is money. • Suppose we assign values to the evaluations in the previous table OK = 1, Fair = 2, Good = 3, Great = 4 and a point for each $200K: • This would allow us to choose a clear winner
Evaluating consequences – Making Tradeoffs • Trade off between objectives, as in the example, sometimes prestige is more important than money (or the prestige will eventually lead to money in subsequent situations!)
Uncertainty and Risk • Usually you do not know the consequences of most alternatives precisely • Risk profiles: • Attach a risk profile to each alternative: • Possible outcomes • Probability of each outcome • Resulting consequences • Caution – separate your judgments about uncertainties from judgments about values
Uncertainty and Risk • A risk profile for one alternative, using qualitative assessments: • Adding numerical assessments:
Expected Monetary value • I have included a portion of a second lecture from a course in Statistical Decision Making that you may find useful (See the Course notes or Course Documents (DEN) sections. • Simply put, the expected monetary value or expected value equals • Probability of occurrence x Value • EMV is the mean of the expected distribution of the payoffs • If a lottery distributes 1,000,000 ticket from which it draws a winner and the winner gets $2,000,000, then • Expected Monetary Value = (1/1,000,000) x $2,000,000 = $2 • Another way to look at EMV as a weighted average • Caution – EMV’s assume that increments in money have uniform intrinsic values
Expected Desirability Value • This measure is intended to capture on how strongly or intensely we feel about something • How do you assign these values? • Without extensive consideration – assign a portion to each • Conditionally – comparing desirability on some scale • Monetary value converted to a desirability value • Let’s look at the book’s example:
Expected Desirability Value - continued • The desirability values were derived as follows • The lowest to highest outcomes are 0 and $2M. The objective is to decide how important each increment in money is. Let’s start with: • From $0 to $500,000 = 50 points • From $500,000 to $2,000,000 = 50 points, that is • The judgmental mid-desirability point between 0 an 2M is 500K • Making two more judgements • The judgmental mid-desirability point between 0 an 500K is 200K • The judgmental mid-desirability point between 500K and 2M is 1M • We can now put this into a table and graph on the next slide:
Expected Desirability Value - continued • From this graph we find the individual desirability values
Expected Utility Values • The previous two measures can be quite useful, but do not take into consideration the attitude towards risk • EUV can be a useful summary value • Definition BRLT = Basic Reference Lottery Ticket • Note that this particular ticket has an EMV of $500,000
Expected Utility Values - continued • I have put the following formula in a cell in Excel and when I put an equal sign in front of it, it will determine whether we win or not • How do we use this? • Select L and W from your risk profiles (the book’s example had 0 and $2M) • BRLT’s will be chosen with monotonicity and continuity (as in math analysis) • They are also substitutable – If we don’t care whether we get outcome B or a 0.6 BRLT, we can substitute one for the other
Expected Utility Values - continued • As with desirability midpoints, we establish mid risk points • How much would you be satisfied with if the outcome was certain as compared to a 50 – 50 chance of zero and the max? • In our example • 0 to 2M : 400K • 0 to 400K: 150K • 400K to 2M: 800K
Behavioral Decision Making • How do real people make decisions? • Most people, most of the time, do not follow the advice of theorists • What are the most frequent deviations from rational decisions?
Behavioral Decision Making - Decision traps • Anchoring – Relying on a first impression • Status Quo – stick with the past • Sunk Cost – throwing good money after bad • Confirming Evidence – we tend to pay more attention to evidence that supports our position • Wrong problem – influencing the response with the question – recent story about automobile death rates
Behavioral Decision Making - Prediction Anomalies • Thinking probabilistically – most people don’t bother • likely, probably, maybe, more than likely, rarely, likelyhood. Possibility, good chance • Lesson – be more precise • Conditional ambiguities - P(A|B) vs. P(B|A), Monty Hall • Overconfidence – using confidence intervals that are too tight • Lesson - practice • Conjunction fallacy - P(A and B) > P(A). This must be false, but when you substitute real events, people do make the mistake
Behavioral Decision Making - Prediction Anomalies • Mutually distinct – no union or P(A and B) = 0 • Exhaustive – all possible events are included in the probability space P(A) + P(B) +….+P(X) = 1 • Prior odds – the probabilities of A and B as given • When new information (NI) is added, (and assume that it is distinct) we get posterior probabilities P(A|NI), P(B|NI) • For example: P(A) = 0.3, P(B) = 0.4, P(NI) = 0.2, then P(A|NI) = 0.3/(1-0.2) = 0.375 and P(B(NI) = 0.4/(1-0.8) = 0.5, that is the odds on A and B have increased because part of the space has been taken up by NI • P(A)/P(B) = 0.75, but P(A|NI)/P(B|NI) = 0.75, because we assumed that NI was in our probability space and exclusive
Behavioral Decision Making - Prediction Anomalies • In general, this is not the case and • P(A|NI)/P(B|NI) = P(A)/P(B) x P(NI|A)/P(NI|B) The last term is the likelihood ratio • Bayes’ theorem • Example If 30% of students in a class of 50 got A’s and 35% in A class of 60 got A’s, what is the percentage of A’s? There are 15 in the first class and 21 in the second class for a total of 36 A’s out of 110 students = (15+21)/(50+60) = 0.327 • But let’s ask the question in reverse – If a student got an A, what is the probability that she/he came from the first class? How many A’s in the first class? 15. How many total A’s? 36, therefore (0.3)(50)/[(0.3)(50) + (0.35)(60)] = 0.417
Behavioral Decision Making - Prediction Anomalies • Base rate fallacy – the base is the probability ratio P(A)/P(B) • People confuse the base rate with the likelihood ratio • Underestimating the value of sample evidence • Example in book about green and white bags • Census discussion in Congress in 1990’s • Getting mystical about coincidences I bet you each have a story about a coincidence • Suppose I flip a coin 15 time in a row and get the following result HHTTHHHTHTHTTTH. Note that there are 7 T’s and 8 H’s, not an unusual result. So this particular sequence of events is not particularly unusual. However, if I view it from the perspective that this particular sequence has occurred, the odds are 215 = 32768:1!!! • People resist changing their mind once they have come to a conclusion
Game Theory • “How rational actors ought to behave when their separate choices interact to produce payoffs to each player” • Consider it from the perspective of what it tells us about negotiation because it offers us powerful insight • Helps us consider how the other party will respond to our offers • Basics: • You have to act (doing nothing is considered an act) • Payoffs depend on what each party does • You don’t know what they will do, but know what they could do • They do not know what you will do, but know what you could do
Negotiation Analysis • Joint decision making • Organizing an approach
