Unformatted text preview: or • Risk Pr( ) Pr( ) • InformaNon Pr(  info) Pr(  info) 40 Uncertainty & Risk, in General ω1 ω2 ω3 ωi ω Ω • Ω: State Space • ω are disjoint exhausNve states of the world • ωj: rain tomorrow & have umbrella & ... • Pr(ω) 41 Uncertainty & Risk, in General E1 Ei E2
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Ej AlternaNvely, • Overlapping events – E1: rain tomorrow – E2: have umbrella • Ω=2n 42 Modeling InformaNon • E: Event of interest • P(E, Si, Sj): Prior distribuNon • Nature draws event outcome and signals • Bayesian agents can form belief P(E=eSi = si) 43 An Economist’s Approach to Modeling InformaNon ω1 ω2 ω3 ωi ω Ω • Ω: state space • Pr(ω) • An agent has a parNNon of the state space* • Nature draws ω* • Agent observes Si(ω*) • Agent forms belief P(ωSi(ω*)) 44 Preference and UNlity • Preference ! ! ! • UNlity, u(ω) u( )=10 > u( )=8 > u(...
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This note was uploaded on 04/11/2013 for the course EE 218 taught by Professor Vanderschaar,mihaela during the Winter '13 term at UCLA.
 Winter '13
 vanderSchaar,Mihaela

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