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Supplement to Epistemic Foundations of Game Theory

Supplement to Epistemic Foundations of Game Theory

1. Proof of Lemma 3.1

Lemma 3.1 Suppose that G=N,{Si,ui}iN is a strategic game. A strategy siSi is strictly dominated (possibly by a mixed strategy) with respect to XSi iff there is no probability measure pΔ(X) such that si is a best response with respect to p.

Proof: We start with some preliminary observations. Let G=S1,S2,u1,u2 be a two-player strategic game. Recall that Δ(S1) and Δ(S2) denote the mixed strategies for players 1 and 2, respectively. For p1Δ(S1), p2Δ(S2), we write U1(p1,p2) (respectively U2(p1,p2)) for the expected utility that player 1 (respectively 2) receives when 1 uses the mixed strategy p1 and 2 uses the mixed strategy p2. We assume that the players’ choices are independent, so we have the following calculation for i=1,2:

Ui(p1,p2)=xS1yS2p1(x)p2(y)ui(x,y)

A two-player game G=S1,S2,u1,u2 is zero-sum provided for each xS1 and yS2, u1(x,y)+u2(x,y)=0. We make use of the following fundamental theorem of von Neumann:

Theorem S2 (von Neumann’s minimax theorem) For every two-player zero- sum game with finite strategy sets S1 and S2, there is a number v, called the value of the game such that:

  1. v=max = \min_{q\in \Delta(S_2)}\max_{p\in\Delta(S_1)} U_1(p,q)
  2. The set of mixed Nash equilibria is nonempty. A mixed strategy profile (p,q) is a Nash equilibrium if and only if

    \begin{align} &p\in \mathrm{argmax}_{p\in\Delta(S_1)}\min_{q\in\Delta(S_2)} U_1(p,q) \\ & q\in \mathrm{argmax}_{q\in\Delta(S_2)}\min_{p\in\Delta(S_1)} U_1(p,q) \end{align}
  3. For all mixed Nash equilibria (p,q), U_1(p,q)=v

Now, we can proceed with the proof of the Lemma. Suppose that G=\langle N, \{S_i, u_i\}_{i\in N}\rangle is a strategic game where each S_i is finite.

Suppose that s_i\in S_i is strictly dominated with respect to X. Then there is a s_i \in S_i such that for all s_{-i}\in X, u_i(s_i',s_{-i}) > u_i(s_i,s_{-i}). Let p\in\Delta(X) be any probability measure. Then for all s_{-i}\in X, 0\le p(s_{-i})\le 1. This means that for all s_{-i}\in X, we have p(s_{-i})\cdot u_i(s_i',s_{-i}) \ge p(s_{-i})\cdot u_i(s_i,s_{-i}), and there is at least one s_{-i}\in S_{-i} such that

p(s_{-i})\cdot u_i(s_i',s_{-i}) > p(s_{-i})\cdot u_i(s_i,s_{-i})

(this follows since p is a probability measure on X, so cannot assign probability 0 to all elements of X). Hence,

\sum_{s_{-i}\in S_{-i}} p(s_{-i})\cdot u_i(s_i',s_{-i})> \sum_{s_{-i}\in S_{-i}}p(s_{-i})\cdot u_i(s_i,s_{-i})

So, EU(s_i',p)> EU(s_i,p), which means s_i is not a best response to p.

For the converse direction, we sketch the proof for two player games. The proof of the more general statement uses the supporting hyperplane theorem from convex analysis. We do not discuss this extension here (note that it is not completely trivial to extend this result to the many agent case as we must allow players to have beliefs about correlated choices of their opponents). Let G=\langle S_1, S_2, u_1, u_2\rangle be a two-player game. Suppose that \alpha\in \Delta(S_1) is not a best response to any p\in\Delta(S_2). This means that for each p\in \Delta(S_2) there is a q\in\Delta(S_1) such that U_1(q,p)>U_1(\alpha,p). We can define a function b:\Delta(S_2)\rightarrow \Delta(S_1) where, for each p\in\Delta(S_2), U_1(b(p),p)>U_1(\alpha,p).

Consider the game (S_1, S_2, \ou_1, \ou_2) where

\ou_1(s_1, s_2)=u_1(s_1,s_2) - U_1(\alpha, s_2)

and \ou_2(s_1,s_2)=-\ou_1(s_1,s_2). This is a 2-person, zero-sum game, and so by the von Neumann minimax theorem, there is a mixed strategy Nash equilibrium (p_1^*, p_2^*). Then, by the minimax theorem, for all m\in\Delta(S_2), we have

\oU(p_1^*, m) \ge \oU_1(p_1^*, p_2^*) \ge \oU_1(b(p_2^*),p_2^*)

We now prove that \oU_1(b(p_2^*),p_2^*)> \oU_1(\alpha,p_2^*):

\begin{align*} \oU_1(b(p_2^*),p_2^*) &= \sum_{x\in S_1}\sum_{y\in S_2} b(p_2^*)(x)p_2^*(y)\ou_1(x,y)\\ &= \sum_{x\in S_1}\sum_{y\in S_2} b(p_2^*)(x)p_2^*(y)[u_1(x,y) - U_1(\alpha, y)]\\ &= \sum_{x\in S_1}\sum_{y\in S_2} b(p_2^*)(x)p_2^*(y)u_1(x,y) \\ &\quad\quad\quad - \sum_{x\in S_1}\sum_{y\in S_2} b(p_2^*)(x)p_2^*(y)U_1(\alpha, y)\\ &= U_1(b(p_2^*), p_2^*) - \sum_{x\in S_1}\sum_{y\in S_2} b(p_2^*)(x)p_2^*(y)U_1(\alpha, y)\\ &> U_1(\alpha,p_2^*) - \sum_{x\in S_1}\sum_{y\in S_2} b(p_2^*)(x)p_2^*(y)U_1(\alpha, y)\\ &> U_1(\alpha,p_2^*) \end{align*}

Since p_2^*(y) U_1(\alpha, y) does not depend on x, we have

\begin{align*} \sum_{x\in S_1}\sum_{y\in S_2} b(p_2^*)(x)p_2^*(y)U_1(\alpha, y) &= \sum_{x\in S_1} b(p_2^*)(x)\sum_{y\in S_2}p_2^*(y)U_1(\alpha,y)\\ &= \sum_{x\in S_1}b(p_2^*)(x)U_1(\alpha, p_2^*)\\ &= U_1(\alpha,p_2^*)\sum_{x\in S_1}b(p_2^*)(x)\\ &=U_1(\alpha,p_2^*) \end{align*}

Hence, for all m\in\Delta(S_2) we have \oU_1(s_1^*,m)>0 which implies for all m\in\Delta(S_2), U_1(s_1^*,m) > U_1(\alpha, m), and so \alpha is strictly dominated by p_1^*. QED

2. Proof of Theorem 6.1

Theorem 6.1 (Brandenburger & Keisler 2006: Theorem 5.4) There is no assumption-complete type structure for the set of conjectures that contain the first-order definable subsets.

To prove this theorem, we follow an idea recently discussed in Abramsky & Zvesper (2010). Suppose that \C_A\subseteq\pow(T_A) is a set of conjectures about Ann states (similarly, let \C_B\subseteq\pow(T_B) be a set of conjectures about Bob states). We start with the flowing assumption:

For all X\in \C_A there is a x_0\in T_A such that

  1. \lambda_A(x_0)\ne\emptyset: “in state x_0, Ann has consistent beliefs”

  2. \lambda_A(x_0)\subseteq \{y \mid \lambda_B(y)=X\}: “in state x_0, Ann believes that Bob assumes X

To prove the theorem, we need the following lemma.

Lemma S1 Under the above assumption, for each X\in \C_A there is an x_0 such that x_0\in X\text{ iff there is a \(y\in T_B\) such that \(y\in \lambda_A(x_0)\) and \(x_0\in\lambda_B(y)\)}

Proof of Lemma S1: Suppose that X\in\C_A. Then there is an x_0\in T_A satisfying 1 and 2.

Suppose that x_0\in X. By 1., \lambda_A(x_0)\ne\emptyset so there is a y_0\in T_B such that y_0\in\lambda_A(x_0). We show that x_0\in\lambda_B(y_0). By 2., we have y_0\in\lambda_A(x_0)\subseteq \{y \mid \lambda_B(y)=X\}. Hence, x_0\in X=\lambda_B(y_0), as desired.

Suppose that there is a y_0\in T_B such that y_0\in\lambda_A(x_0) and x_0\in \lambda_B(y_0). By 2., y_0\in\lambda_A(x_0)\subseteq \{y \mid \lambda_B(y)=X\}. Hence, x_0\in\lambda_B(y_0)=X, as desired.

Consider a first-order language \L whose signature contains binary relational symbols R_A(x,y) and R_B(x,y) defining \lambda_A and \lambda_B respectively. The language \L is interpreted over qualitative type structures where the interpretation of R_A is the set \{(t,s) \mid t\in T_A, s\in T_B, \text{ and }s\in\lambda_A(t)\}. QED

Proof of Theorem 6.1. Consider the formula \phi in \L: \phi(x):=\exists y (R_A(x,y) \wedge R_B(y,x))

Then, the negation of \phi is:

\neg\phi(x):=\forall y(R_A(x,y)\rightarrow\neg R_B(y,x)): “all states x where any state y that Ann considers possible is such that Bob does not consider x possible at y.” That is, this formulas says that “Ann believes that Bob’s assumption is wrong.”

Suppose that X\in\C_A is defined by the formula \neg\phi(x).

Suppose that there is a x_0\in T_A such that

  1. \lambda_A(x_0)\ne\emptyset: Ann’s beliefs at x_0 are consistent.

  2. \lambda_A(x_0)\subseteq \{y \mid \lambda_B(y)=X\}: At x_0, Ann believes that Bob assumes X=\{x \mid \phi(x)\} (i.e., Ann believes that Bob assumes that Ann believes that Bob’s assumption is wrong.)

We have

\begin{align} \neg\phi(x_0) \text{ is true } &\text{iff } x_0 \in X &\text{(defn of \(X\))} \\ &\text{iff } \text{there is a } y\in T_B \text{ with } y\in\lambda_A(x_0) \\ &\quad\quad\text{and } x_0\in \lambda_B(y) &\text{(Lemma S1)}\\ &\text{iff } \phi(x_0) \text{ is true}. &\text{(defn of \(\phi(x)\))} \end{align}

QED

Copyright © 2015 by
Eric Pacuit <epacuit@umd.edu>
Olivier Roy <Olivier.Roy@uni-bayreuth.de>

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