lecture1

01 499 43 42980 i buy 10000 insurance for 125

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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 ω En 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=e|Si = 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(ω|Si(ω*)) 44 Preference and UNlity •  Preference ! ! ! •  UNlity, u(ω) u( )=10 > u( )=8 > u(...
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