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Introduction à la théorie de la décision Ferdinand M. Vieider University of Munich Home: Université Libre de Tunis, April 6 th,

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Decision Theory: studies ensemble of human decision making processes, individual and social It mostly becomes relevant in situations with some complexity (e.g. risk, uncertainty) It is closely related to several other fields: - operations research - linear programming - game theory - experimental economics - behavioral economics - cognitive psychology - social psychology 2 What is Decision Theory? 2

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Cognitive Psychology: the methodology of investigation and topics is very similar; however: rationality concepts borrowed from economics Experimental Economics: DT methodology is very often experimental, however not exclusively so; also historically focus in individual decisions Behavioral economics: comes closest, at least in descriptive aim; however, decision theory also encompasses rationality models, not only deviations from such models 3 Main similarities and differences 3

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All of the disciplines just discussed make extensive use of experiments Experiments allow to reproduce stylized situations of interest Most importantly: one can vary one independent variable at a time This makes it possible to isolate causal relationships (not just correlation) Further distinctions: lab experiments versus field experiments, artificial experiments versus natural experiments 4 Why experiments? 4

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Overview of different approaches: normative, descriptive and positive Origins of decisions theory: expected value theory to deal with risk Introducing subjectivity: expected utility and its behavioral foundations Expected utility's failure as a descriptive theory of choice Descriptive theories of choice: Prospect Theory (and what it can explain) Uncertainty, ambiguity aversion, and other puzzles (Wason, Monty Hall) 5 Lecture Overview 5

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6 Normative, Descriptive, and Prescriptive approaches: From Expected Value to Expected Utility Theory 6

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Different approaches to decision theory: normative, descriptive, and prescriptive Normative theories describe how a perfectly rational and well-informed decision maker should behave Descriptive analysis focuses only on actually observed behavior, and tries to find regularities Prescriptive analysis has the aim of helping real-world decision makers in making better dec. Are normative theories also good descriptive theories? 7 Different Approaches to DT 7

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At the outset, normative theories were taken for descriptive purposes as well However: deviations from models soon emerged (falsification of theory) Sprawling of descriptive theories that try to explain anomalies Several issues that are often confounded: evidence from lab produces focus on cognitive limitations and stability of preferences Real world: problems of awareness (knowledge about knowledge), then information search and processing 8 Descriptive Issues 8

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Prescriptive analysis moves from a limited- information and processing perspective Goal: helping to reach the best decision given the information at hand In experiments normative and prescriptive approach often coincide (complete info) This means that real-world situations are often very different (external validity issue) 9 Prescriptive Analysis 9

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Historically, the concept of probabilities and how to deal with them is rather recent. In the 1600s, Blaise Pascal and Pierre Fermat developed expected value theory According to EVT, a prospect can be represented as its mathematical expectation: 10 The origins of decision theory DT 100 DT 0 p*X + (1-p)*Y= 0.5*100= 50

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Choice between 2 known-probability events: 11 Example: EV normative? DT 10DT 50 DT 0 EV: 0.9*10+0.1*0=9 < 0.2*50+0.8*0=10 According to EVT, you should choose the lottery to the right. Is that your preference? Does your preference change if we increase the amounts *1000, to 10,000 & 50,000 DT?

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Expected value may not be a reasonable theory, even normatively, for large amounts Also, these amounts may not be the same for everybody (wealth situation, preference) To deal with this, we need one subjective parameter: Expected Utility Theory In EUT, the value of a prospect is given again by its mathematical expectation, but instead of using (objective) monetary amounts we now use (subjective) utilities of those amounts 12 From EV to EU 12

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Choice between 2 known-probability events: 13 Example: EV versus EU DT 400DT 200 DT 0 EU: 0.3*u(400)+0.7*0=0.3 ? 0.5*u(200)+0.5*0=? The extreme outcomes can always be normalized to 0 and 1. But how about intermediate outcomes?

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How can we elicit the missing utility? 14 Eliciting Utilities 14 ~ p 1-p DT 400 DT 0 We elicit either CE or p such that U(CE)=p*U(400)+(1-p)*U(0)=p Let CE=200 and elicit p (in reality easier for DM to elicit CE!) CE

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Choice between 2 known-probability events: 15 Example reconsidered: 15 ? U(0)=0, U(400)=1; assume p=0.65, then U(200)=0.65 This means that now: 0.5*U(200)+0.5*U(0)=0.325 > 0.3*U(400)+0.7*U(0)=0.3

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Given the non-linearity in the utility function, preferences can change relative to EV EUT: concavity=risk aversion. This is not universally valid! 16 Subjective Utility and Risk 16 EV U() EU

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EV is reasonable for small stakes, however most important decisions deal with large stakes Also, many important decisions deal with non- quantitative decisions such as health states For the latter EV cannot be defined; also: what if you have utility over money plus other things? Expected Utility is thus generally more useful; it is however more complex, especially when combined with unknown probabilities For the moment, we consider only utilities over monetary outcome with known probabilities 17 EV or EU? 17

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Why concave utility? Consider the following example: A bet is proposed to you: a fair coin is flipped until the first head come up; the amount you win at first flip is DT2, then DT4, then DT8, so that if head comes up at the k th flip you get DT2 k How much would you be willing to pay to play this game? The Expected value of the gamble is infinite: 1/2*2+1/4*4+1/8*8... = = This goes to show that EV does not hold empirically when large amounts are at stake 18 The St. Petersburg Paradox 18

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Risk Aversion: a prospect is considered inferior to its expected value Risk Seeking: a prospect is preferred to its expected value Risk Neutrality: a prospect and its expected value are equally valuable ¡Do not confuse risk aversion with concave U! 19 Risk Aversion and Risk seeking 19 p 1-p X Y p*X+(1-p)*Y ?

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Behavioral foundations are properties of behavior (axioms) underlying a theory They are very helpful in that a theory can be decomposed into some intuitive rule E.g., saying that EU holds is equivalent to saying that preferences satisfy: - weak ordering - standard gamble solvability - standard gamble dominance - standard gamble consistency (or the stronger independence condition) 20 Behavioral Foundations of EU 20

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One of the most discussed issues is the following independence of common alternatives: 21 Independence 21 p 1-p y C p x C x y How intuitive do you find this condition?

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Consider the following two choices: 22 Example: Allais (common consequence) 22 5,000,000 1,000, ,000, ,000, B A C D The most common pattern is BC. This violates the independence axiom (rational: AC or BD).

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Consider the following two choices: 23 Example: Compound Prospects /3 1/3 2/3 1/ /6 2/3 ? Which one do you prefer? 1/2

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Under EUT, risk aversion coincides with a concave utility function, and risk seeking with a convex utility function This does not hold generally: shortly we will see risk seeking with a concave utility function! With a concave utility function, the expected utility of a prospect is lower than the utility of the expected value: p*U(x)+(1-p)*U(y)__
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Under EUT, risk aversion coincides with a concave utility function. 25 EUT and Insurance 25 DT U(DT) U U(y) U(x) xy p*x+(1-p)*y U(p*x+(1-p)*y) p*U(x)+(1-p)*U(y) CE What is the risk premium here?

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There is a 5% risk that your house may be flooded, potential damages are – DT100,000 EV = – DT5,000, However, if you are risk averse, the CE is lower, e.g. CE = – DT6000 There is a positive risk premium of DT1000; by the law of large numbers, the insurance will pay DT5000 on average, and can thus make up to DT1000 by ensuring your risk Could you represent this problem in a graph? What changes because of the negative outcome? When is it rational to take out insurance and when not? 26 Insurance example 26

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Nothing changes: implicit reference point problem (previous wealth) 27 Graph Insurance Example 27 DT U(DT) U U(y) U(x) X = – y=0 p*x+(1-p)*y U(p*x+(1-p)*y) p*U(x)+(1-p)*U(y) CE

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We can explain insurance with concave utility under EUT In theory, we can also explain lottery play, but we need convex utility for that However: many people take up insurance and play lottery at the same time. How can this be explained? Under EUT, we would need convex and concave sections of the utility function We would also need these to hold at different levels of wealth 28 Insurance and Lotteries 28

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People are typically risk seeking for small probabilities (± p<0.15): lottery play For larger probabilities, people tend to be risk averse: CE

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30 Descriptive Theories of Choice: Prospect Theory 30

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Using a Prospect offering either 100 or 0 with different probabilities, I asked choices between the prospect and different sure amounts The switching point between the sure amount and the prospect indicates a person's CE The probabilities were 0.05, 0.5, and 0.9 Mean CEs obtained from this classroom experiment in France were: 31 Experimental Data: Typical CEs 31 EV probability CE (mean)CE/EV

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Remember that U(CE)=p Thus: U(11)=0.05; U(46)=0.5; U(68)=0.9, and we can always set U(0)=0, U(100)=1 32 Your (average) utility function 32 X U(X)

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Kahneman & Tversky (1979; Econometrica) brought psychological intuition to economics: Risk attitudes for small amounts are driven by feelings about probability, not money We can thus let probability be the subjective parameter, and assume utility to be linear: PV=w(p)*x+(1-w(p))*y Linear utility seems reasonable for small monetary amounts (but not large!) For large amount, we can combine probability weighting with utility: PU=w(p)*u(x)+(1-w(p))*u(y) 33 Prospect Theory 33

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We have seen that CE=p*U(100); if utility is linear, then p must be transformed Let us thus assume that CE=w(p)*100, where w represents a weighting function From our previous results we get: - w(0.05)=11/100= w(0.5)=46/100= w(0.9)=68/100=0.68 From, this, we can plot a probability weighting function assuming w(0)=0, w(1)=1 34 Probability Weighting: Attitudes to Risk 34

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35 Probability Weighting Function 35

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Notice how this function can explain contemporary insurance and lottery play through overweighting of small probabilities Also, there are jumps at the endpoints: the possibility and certainty effects The latter can explain the Allais paradox (common consequence effect) It also captures common risk attitudes quite well: fourfold pattern of risk attitudes However, with linear utility it may have problems accommodating decisions over large stakes 36 Insurance and Lottery Play 36

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We have assumed linear utility above: however, we have seen that this is not always reasonable (St. Petersburg paradox) Even assuming concave utility, it has problems dealing with mixed gambles Example from Rabin, Matthew (2000). Risk Aversion and Expected-Utility Theory: A Calibration Theorem. Econometrica 68 (5): If a DM turns down (.5:110; -100), then she will turn down a 50:50 of and X for all X 37 Utility: Attitudes towards Outcomes 37

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In PT, the utility function describes attitudes about money only, not probabilities 38 Prospect Theory Utility Function 38 X U(X) concave convex kink

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Concave utility for gains means that even for small probabilities one can be risk averse for very large outcomes (insensitivity) For losses one can be risk seeking for small probabilities for very large outcomes Loss aversion: a loss is felt more than a monetarily equivalent gain Loss aversion has been used to explain the status quo bias, endowment effect, myopic loss aversion (equity premium puzzle), etc. 39 Properties of Utility 39

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Under loss aversion, losses loom larger than gains 0 ~ How high would the gain need to be to make you indifferent between playing and not playing the prospect? 40 Loss Aversion 40 – DT50 DT ? 0.5

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Let us assume that DT 100 was elicited as gain that makes you indifference Let us also assume that utility is linear over gains and losses, but that you are loss averse Then U(X)=X if X0; and U(X)= –λ*X if X<0 u(0)=0.5*u(100)+0.5*U(–50) 0 =0.5* *(–λ)*50 λ*25=50 λ=2 What other assumption underlies this elicitation of the loss aversion parameter λ? 41 Deduction of Loss Aversion 41

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42 Some functional forms 42 A simple form for the utility that has been proposed is: U(X)=X α if X0 U(X)= –λ*X β if X<0 Can you see why the derivation of loss aversion as done before is an approximation? Some popular functional forms for probability weighting functions are: w(p)=p φ /(p φ +(1-p) φ ) 1/φ w(p)= exp(-ξ (-log p) α

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Loss aversion is found to be the strongest phenomenon empirically It stands and falls however on the determination of the reference point Most of the time, the reference point is assumed to be current wealth, or the status quo This means that people are often reluctant to switch from the status quo, no matter what that status quo is This means that changes are perceived as gains and losses relative to status quo, with losses looming larger 43 Reference Point: Status Quo Bias 43

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The endowment effect was found by artificially establishing a reference point Some people are randomly given one objects and others with a different one (e.g. mugs v. pens) People are then given the opportunity to exchange the object in their possession A large majority of people is found not to exchange their object This holds true for both objects; since they have been randomly assigned, this can however not express true (average) preferences 44 Reference Point: Endowment Effect 44

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45 From known to unknown probabilities: Subjective expected utility and the Ellsberg Paradox 45

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We have so far only considered the case of risk, where objective probabilities are known Good representation of situations such as lottery or well-established medical processes However: most probabilities are unknown: stock market, entrepreneurship, education In this case one can deduce subjective probabilities from observed decisions Savage (1954) put forth some desirable attributes for decision making under uncertainty: Subjective Expected Utility Theory 46 Unknown Probabilities 46

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47 Ambiguity Aversion 47 You are asked to choose between two urns, one 50:50, one unknown proportion of colors First you are asked to choose which color you would like to bet on, then which urn Which color would you rather bet on? And which urn would you prefer to bet on? This phenomenon was discovered by Ellsberg (1961): it violates subjective expected utility theory since probabilities are the same (!) 10 R 10 B 20 R & B in unknown proportion ?20–?

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When asked for a color preference, most people are indifferent: p rr = p rb ; p ar = p ab Most people however have a strict preference for betting on the known-probability urn, no matter what which color: p rr >p ar & p rb > p ab This implies: p rr + p rb = 1 > p ar + p ab ; however, probabilities cannot sum to less than 1, hence the paradox Prospect Theory has recently been adapted to deal with this: Source functions, AER The Ellsberg Paradox 48

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Uncertainty has generally been studied in opposition to risk, not in its own right Also: Ellsberg has created strong focus on prospects However: people react differently to different probability levels (just as for risk) Also, people react differently to different sources of uncertainty (dislike vague probabilities, but may like uncertainties they have expertise in-->betting on football) Applications: home bias in finance; stock market participation puzzle; 49 More realistic decisions under uncertainty 49

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50 Typical Source functions 50

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51 Probability Calculus and Logical Induction: Monty Hall's Doors and the Wason Selection Task 51

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52 Monty Halls Doors 52 There are three doors, one of which hides a car, and two with a goat behind You can choose a door. After you have chosen, the host opens one of the other two and reveals a goat If given the opportunity, should you switch or stay with your original choice?

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53 To switch or not to switch: 1 53 Imagine that the car is behind door 1, and the other two doors hide goats If you have chosen door 1, the host opens either door 2 or 3: In this case, switching loses the prize

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54 To switch or not to switch: 2 54 Imagine again that the car is behind door 1, and the other two doors hide goats If you have chosen door 2, the host opens door 3 for sure: Now, switching gives you the prize

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55 To switch or not to switch: 3 55 Imagine again that the car is behind door 1, and the other two doors hide goats If you have chosen door 2, the host opens door 3 for sure: Now, switching again gives you the prize

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56 Summing it up 56 We have just seen that switching gets you the prize in 2 out of 3 cases Since the structure is symmetric if we assume the prize is behind another door, the probability of winning if switch is 2/3 This is because the door you pick at first gives a 1/3 chance; the other two doors together though give you a 2/3 chance Since the removed door is always one of the other two, you are left with a 2/3 chance by switching

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57 Wason's Abstract Selection Task 57 There a 4 cards, all of which have a letter on one side and a number on the other Two cards show a number (4 and 7), two show a letter (O and G): Which card(s) do we need to turn over to test the logical implication: vowel-->odd (if there is a vowel on one side then odd number on other) 4A7 G

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58 Adding context 58 There are 4 rooms with closed doors and one person in each room You know one is older than 18, one younger, one drinks wine, and one a soda Which door(s) do we need to open to make sure nobody under drinking age drinks alcohol? How would you write the problem down in logical notation? W (wine) <18 >18 S (soda)

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59 Wason Revealed 59 Were your answers to the two questions above equal? Why, or why not? One potential problem lies in the formulation; different formulations of abstract task were only partially effective The most common answer is to turn around only the vowel-->confirmation bias Confirmation biases are very common, also in scientific research (how many white swans do you need to observe to conclude that all swans are white?)

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