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Viewed As a Subset of RK−1, Carathéodory’S Theorem (See, E.G., Theorem 1.1.4 in Schneider (1993)) Implies That Β Is a Convex Combination of at Most K Points InX

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Viewed As a Subset of RK−1, Carathéodory’S Theorem (See, E.G., Theorem 1.1.4 in Schneider (1993)) Implies That Β Is a Convex Combination of at Most K Points In�X Econometrica Supplementary Material SUPPLEMENT TO “REPRESENTING PREFERENCES WITH A UNIQUE SUBJECTIVE STATE SPACE: CORRIGENDUM” (Econometrica, Vol. 75, No. 2, March, 2007, 591–600) BY EDDIE DEKEL,BARTON L. LIPMAN,ALDO RUSTICHINI, AND TODD SARVER IN SECTION S1, WE DEFINE our axioms and the additive expected-utility rep- resentation, and we state the corrected version of Theorem 4.A of Dekel, Lip- man, and Rustichini (2001) (henceforth DLR). In Section S2, we provide a complete and almost entirely self-contained proof of this representation theo- rem. S1. AXIOMS AND ADDITIVE EXPECTED-UTILITY REPRESENTATION Let B ={b1bK} denote the set of pure outcomes. Let ∆(B) denote the set of probability distributions on B. Finally, let denote a preference relation on the set of nonempty subsets of ∆(B), where this space is endowed with the Hausdorff topology. Let dh(x y) denote the Hausdorff distance between x and y.1 DLR (2001) considered the following axioms on : AXIOM 1—Weak Order: The preference relation is asymmetric and nega- tively transitive. AXIOM 2—Continuity: For any x, the strict upper and lower contour sets {y ⊆ ∆(B) | y x} and {y ⊆ ∆(B) | y ≺ x} are open in the Hausdorff topology. For any sets x and y,andanyλ ∈[0 1],define λx + (1 − λ)y ={λβ + (1 − λ)β | β ∈ x and β ∈ y} AXIOM 3—Independence: If x y, then for all z and all λ ∈ (0 1], λx + (1 − λ)z λy + (1 − λ)z AXIOM 4—Monotonicity: If x ⊆ y, then y x. See DLR for discussion of these axioms. In the main text of the corrigendum, we considered the following additional continuity axiom: 1If we let d denote the Euclidean metric on ∆B, then the Hausdorff distance is defined by dh(x y) = max sup inf d(ββ ) sup inf d(ββ ) ∈ ∈ β∈x β y β∈y β x 1 2 E. DEKEL ET AL. ∗ AXIOM 5—L Continuity: There exist menus x and x∗, and N>0 such that for every ε ∈ (0 1/N), for every x and y with dh(x y) ≤ ε, ∗ (1 − Nε)x+ Nεx (1 − Nε)y + Nεx∗ See the corrigendum for discussion of this axiom. The following version of continuity is standard in the literature (see, for example, Fishburn (1970) or Kreps (1988)): AXIOM 6 —von Neumann–Morgenstern (vNM) Continuity: If x y z, then there exist λλ¯ ∈ (0 1) such that λx + (1 − λ)z y λx¯ + (1 − λ)z¯ . We say that a function u : ∆(B) → R is an expected-utility function if u(β) = b∈B u(b)β(b), where we abuse notation by letting b also denote the degen- erate distribution with probability 1 on b. Obviously, such a function is com- pletely defined by specifying the vector in RK that gives the utility of pure out- comes. Hence we also refer to such vectors as expected-utility (EU) functions. DEFINITION S1: An additive EU representation is a measurable space (S Σ), a measurable state-dependent utility function U : ∆(B) × S → R, and a (count- ably additive) signed measure µ on (S Σ) such that (i) the function V :2∆(B) \ {∅} → R, which is defined by (S1) V(x)= sup U(βs)µ(ds) S β∈x represents and (ii) U(·s)is an expected-utility function for each s ∈ S. There are several differences between the definition of an additive EU rep- resentation given here and the original definition given in DLR. We now dis- cuss those differences and their implications. (i) The definition in DLR only requires the measure to be finitely additive. Our necessity arguments do not rely on countable additivity and our sufficiency proof establishes that the measure is countably additive. Hence a representa- tion with a finitely additive measure exists if and only if a representation with a countably additive measure exists. (ii) DLR’s definition required V to be continuous. We show in Lemma S4 that one implication of our definition is the stronger condition that V is Lip- schitz continuous. (iii) DLR’s definition required that every state s ∈ S be “relevant,” which, loosely speaking, implies that none of the states could be dropped from the representation without altering the underlying preference.2 Our definition al- lows for the possibility that some states are not relevant. It is not hard to show 2The interested reader should consult DLR for a precise definition of a relevant state. REPRESENTING PREFERENCES 3 that if we choose the state space to be the support of µ, then the relevancy requirement is satisfied. (iv) DLR required that S be nonempty and they used a nontriviality axiom to ensure that this is possible. Our definition allows for a measure that is iden- tically zero, in which case the support, the set of “relevant” states, is empty. We find this more convenient, but it is easy to show that adding DLR’s nontriviality axiom would ensure a nonempty support. (v) DLR required that s s ∈ S, s = s, then U(·s) and U(·s) do not rep- resent the same expected-utility preference. In this sense, there are no “redun- dant” states. It will be obvious from our proof of the representation theorem that this nonredundancy condition could be imposed without affecting the re- sults. We omit the requirement for simplicity. The following is the corrected statement of Theorem 4.A in DLR. THEOREM S1: The preference has an additive EU representation if and only if it satisfies weak order, vNM continuity, L continuity, and independence. Fur- thermore, also satisfies monotonicity if and only if the measure µ is positive. This theorem differs from the result claimed by DLR only in the continu- ity requirements, replacing their continuity axiom with vNM continuity and L continuity. An implication of the representation theorem is that the assump- tions of Theorem S1 imply continuity because the representation constructed is continuous. The proof of Theorem S1 is contained in Section S2. We now note an inter- esting relationship between the axioms. LEMMA S1: If satisfies monotonicity, then it satisfies L continuity. ∗ PROOF:Letx = ∆(B), x∗ ={(1/K1/K)},andN = K,whereK is the number of pure outcomes. Take any ε ∈ (0 1/N) and x y ⊆ ∆(B) such that dh(x y) ≤ ε. We will show that ∗ (1 − Nε)y + Nεx∗ ⊆ (1 − Nε)x+ Nεx which, given monotonicity, will yield the desired result: ∗ (1 − Nε)x+ Nεx (1 − Nε)y + Nεx∗ To shorten notation, let β∗ = (1/K1/K).Takeanyβ ∈ (1 − Nε)y + ∗ Nεx∗,soβ = (1 − Nε)βy + Nεβ for some βy ∈ y. It is clear from the defini- tion of the Hausdorff distance that dh(x y) ≤ ε implies infβ∈x βy − β E ≤ ε, where · E denotes the Euclidean norm. Because ε ∈ (0 1/N),wehave ε/(1 − Nε)>ε, so there exists βx ∈ x such that βy − βx E ≤ ε/(1 − Nε). 4 E. DEKEL ET AL. Define βˆ ∈ RK as 1 − Nε βˆ = β∗ + (β − β ) Nε y x ˆ ∈ ˆ = We claim that β ∆(B). It is obvious that i β(bi) 1, so we need only to ˆ verify that β(bi) ≥ 0foralli. Because N = K, we see that for all i, 1 − Kε |β(bˆ ) − β∗(b )|≤ βˆ − β∗ = β − β i i E Kε y x E 1 − Kε ε 1 ≤ = Kε 1 − Kε K ∗ ˆ For all i, β (bi) = 1/K,sowehave|β(bi) − 1/K|≤1/K, which implies ˆ ˆ β(bi) ≥ 0. Thus β ∈ ∆(B). Finally, ˆ ∗ (1 − Nε)βx + Nεβ = (1 − Nε)βx + Nεβ + (1 − Nε)(βy − βx) ∗ = (1 − Nε)βy + Nεβ = β ∗ so β ∈ (1 − Nε)x + Nεx . Because β ∈ (1 − Nε)y + Nεx∗ was arbitrary, this completes the proof. Q.E.D. In light of Lemma S1, we have the following corollary to Theorem S1. THEOREM S2: The preference has an additive EU representation with a posi- tive measure µ if and only if it satisfies weak order, vNM continuity, independence, and monotonicity. S2. PROOF OF THEOREM S1 S2.1. Preliminaries: Support Functions Define X to be the set of all nonempty, closed, and convex subsets of ∆(B). Then, for all nonempty x ⊆ ∆(B),wehaveconv(cl(x)) ∈ X,wherecl(x) de- notes the closure of x (in the Euclidean topology on ∆(B)) and conv(x) de- notes the convex hull of x. It will sometimes be useful to work with the set X instead of the set of all menus because of a natural relationship that exists between the set of closed and convex sets and a certain class of continuous functions known as the support functions. In this section, we formally define the support functions and discuss some of their properties. K ={ ∈ RK = 2 = } First, let S s : i si 0and i si 1 be the set of normalized (nonconstant) expected-utility functions on ∆(B).Foranyx ∈ X, the support K function σx : S → R of x is defined by σx(s) = maxβ∈x β · s. For a more com- plete introduction to support functions, see Rockafellar (1970) or Schneider REPRESENTING PREFERENCES 5 (1993). Let C(SK) denote the set of continuous real-valued functions on SK . When endowed with the supremum norm · , C(SK) is a Banach space. Define an order ≥ on C(SK) by f ≥ g if f(s) ≥ g(s) for all s ∈ SK .Let K C ={σx ∈ C(S ) : x ∈ X}.Foranyσ ∈ C,let xσ = β ∈ ∆(B) β · s = β(bi)si ≤ σ(s) s∈SK i The following properties of the support functions will be useful. ∈ ∈ = = LEMMA S2: 1. For all x X and σ C, x(σx) x and σ(xσ ) σ.
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