1 Utility Examples Scott Matthews Courses: /

and Utility Functions  We might care about utility function for wealth (earning money). Are typically:  Upward sloping - want more.  Concave (opens downward) - preferences for wealth are limited by your concern for risk.  Not constant across all decisions!  Risk-neutral (what is relation to EMV?)  Risk-averse  Risk-seeking

and Certainty Equivalent (CE)  Amount of money you would trade equally in exchange for an uncertain lottery  What can we infer in terms of CE about our stock investor?  EU(low-risk) - his most preferred option maps to what on his utility function? Thus his CE must be what?  EU(high-risk) -> what is his CE?  We could use CE to rank his decision orders and get the exact same results.

and Risk Premium  Is difference between EMV and CE.  The risk premium is the amount you are willing to pay to avoid the risk (like an opportunity cost).  Risk averse: Risk Premium >0  Risk-seeking: Premium < 0 (would have to pay them to give it up!)  Risk-neutral: = 0.

and Utility Function Assessment  Basically, requires comparison of lotteries with risk-less payoffs  Different people -> different risk attitudes - > willing to accept different level of risk.  Is a matter of subjective judgment, just like assessing subjective probability.

and Utility Function Assessment  Two utility-Assessment approaches:  Assessment using Certainty Equivalents  Requires the decision maker to assess several certainty equivalents  Assessment using Probabilities  This approach use the probability-equivalent (PE) for assessment technique  Exponential Utility Function:  U(x) = 1-e -x/R  R is called risk tolerance

and Exponential Utility - What is R?  Consider the following lottery:  Pr(Win $Y) = 0.5  Pr(Lose $Y/2) = 0.5  R = largest value of $Y where you try the lottery (versus not try it and get $0).  Sample the class - what are your R values?  Again, corporate risk values can/will be higher

8 We all need a break. Deal or No Deal

and Show online game - quickly  Then play it in front of class a few times  With index cards

and Appeal of the Game  DOND is a constant tradeoff game:  Certainty equivalent (banker’s offer)  Expected value / utility of deal  Attitude towards risk!  Recent example from pop culture  To accept deal (for risk neutral), CE < offer  How does banker make offers? Not pure EV!

and Deal or No Deal - Decision Tree  Decision node that has 2 options:  Banker’s offer to stop the game OR  Chance node (1/N equal probabilities) with all remaining case values as possible outcomes

and Let’s focus on a specific outcome  You’ve been lucky, and have the game down to 2 cases: $1 and $1,000,000  What does your “decision tree” look like?  How much would you have to be offered to stop playing?  What are we asking when we say this?  What if banker offers (offer increasingly bigger from about $100k).

and And what if your utility looks like.. Utility(Y) Money ($) $0 $1,000, EMV = $500, $220k CE - why? Typical risk-averse Risk Prem Risk Prem = EMV - CE

and  The banker offers you $380,000  Who would take the offer? Who wouldn’t?  Would the person on the previous slide take it? Why?

and And what if your utility looks like.. Utility(Y) Money ($) $0 $1,000, EMV = $500, CE - why? Typical risk-averse Risk Prem? Risk Prem = EMV - CE

and And what if your utility looks like.. Utility(Y) Money ($) $0 $1,000, EMV = $500, CE - why? Typical risk-seeking Risk Prem < 0! Risk Prem = EMV - CE ~0.15

and The banker’s utility function, and decision problem  Minimizing loss!  Banker however “is” playing repeated games with many chances to recover loss

Play the Game Twice and

19 Friedman-Savage Utility Or.. Why Scott doesn’t buy lottery tickets until the jackpots get big?

Is Risk Aversion constant?  Doesn’t seem to be from trials of game  Seems to vary by situation (and timing)  Assumptions of expected value or utility miss the context of the decisions! and

and    13/Lectures/Lecture22_Multiattribute.pdf