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\documentclass[10pt]{article}
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\newcommand{\ex}[2][]{\E_{#1}\left[#2\right]}
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\newtheorem{theorem}{Theorem}
\newtheorem{lemma}{Lemma}
\newtheorem*{claim}{Claim}
\author{Thibaut Horel \and Paul Tylkin}
\title{Economics 2099 Problem Set 2 -- Solutions}
\begin{document}
\maketitle
\section*{Exercise 4.13}
\section*{Exercise 4.14*}\begin{claim} For any constant $\beta$, there is a matroid environment and an i.i.d. non-regular distribution such that the approximation ratio of the optimal mechanism with the surplus maximization with anonymous reserve is at least $\beta$. \begin{proof} \end{proof} \end{claim}
\section*{Exercise 4.19}
\begin{claim} In regular, matroid environments, the revenue of the surplus maximization mechanism with monopoly reserves is a 2-approximation to the optimal mechanism revenue.
\begin{proof} Let REF denote the optimal mechanism and its expected revenue and let APX denote the surplus maximization mechanism with monopoly reserves and its expected revenue. Similarly to the single-item case, we know that REF $\geq$ APX and want to show that REF $\leq$ 2APX. Let $I$ denote the set of agents allocated by the optimal mechanism and let $J$ denote the set of agents allocated by the surplus maximization mechanism with monopoly reserves. Then, $$REF = \E\left[\sum_{i \in I}\phi_i(v_i)\right]$$ and $$APX = \E\left[\sum_{j \in J}\phi_j(v_j)\right].$$ By linearity of expectation, we can write this as $$REF = \sum_{i \in I}\E\left[\phi_i(v_i)\right]$$ and $$APX = \sum_{j \in J}\E\left[\phi_j(v_j)\right].$$
Now, let $\psi: I \rightarrow J$ be the map (bijection) with the following property: $I \backslash \{i\} \cup \{\psi(i)\}\text{ is feasible.}$ This is known as the \textit{matroid basis exchange property} - and says basically that $\psi(i) \in J$ is a possible replacement for $i \in I$.
Now, we can write the expected revenue of the optimal mechanism by conditioning on whether $i = \psi(i)$ for $i \in I$, $\psi(i) \in J$. (i.e. whether agent $i$ is allocated the same item by the optimal mechanism as agent $\psi(i)$ is by the surplus maximization mechanism). So we can write $$REF = \underbrace{\sum_{\substack{i \in I}}\E[\phi_i(v_i) | i = \psi(i)] \Pr[i=\psi(i)]}_{REF_=} + \underbrace{\sum_{\substack{i \in I}}\E[\phi_i(v_i) | i \neq\psi(i)] \Pr[i\neq \psi(i)]}_{REF_{\neq}}.$$ We will prove the desired result by showing both $REF_=$ and $REF_{\neq}$ are bounded from above by APX.
\begin{align}
REF_= &= \sum_{\substack{i \in I}}\E[\phi_i(v_i) | i = {\psi(i)}] \Pr[i={\psi(i)}] \\
&= \sum_{\substack{i \in I}}\E[\phi_{\psi(i)}(v_{\psi(i)}) | i = {\psi(i)}] \Pr[i={\psi(i)}] \\
&\leq \sum_{\substack{i \in I}}\E[\phi_{\psi(i)}(v_{\psi(i)}) | i = {\psi(i)}] \Pr[i={\psi(i)}] + \sum_{\substack{i \in I}}\E[\phi_{\psi(i)}(v_{\psi(i)}) | i \neq {\psi(i)}] \Pr[i\neq {\psi(i)}] \label{42}\\
&= \sum_{i\in I}\E\left[\phi_{\psi(i)}(v_{\psi(i)})\right] \\
&= APX.
\end{align}
Note that Equation $\ref{42}$ follows from Lemma 4.2 (i.e. from the monotonicity of virtual value functions).
Next, we consider $REF_{\neq}$.
\begin{align}
REF_{\neq} &= \sum_{\substack{i \in I}}\E[\phi_i(v_i) | i \neq {\psi(i)}] \Pr[i\neq {\psi(i)}] \\
&\leq \sum_{\substack{i \in I}}\E[v_i | i \neq {\psi(i)}] \Pr[i\neq {\psi(i)}] \\
&\leq \sum_{\substack{i \in I}}\E[\hat{v}_i | i \neq {\psi(i)}] \Pr[i\neq {\psi(i)}] \\
&= \sum_{\substack{i \in I}}\E[v_{\psi(i)} | i \neq {\psi(i)}] \Pr[i\neq {\psi(i)}] \label{428} \\
&\leq \sum_{\substack{i \in I}}\E[v_{\psi(i)} | i = {\psi(i)}] \Pr[i={\psi(i)}] + \sum_{\substack{i \in I}}\E[v_{\psi(i)} | i \neq {\psi(i)}] \Pr[i\neq {\psi(i)}] \label{429} \\
&= APX.
\end{align}
Note that Equation $\ref{428}$ follows from Lemma 4.28 and Equation $\ref{429}$ follows from Theorem 4.29.
\end{proof}
\end{claim}
\section*{Additional Problem}
We prove that $\ex[\mathbf{F}]{\max_i
v_i}\leq\frac{e}{e-1}\ex[\mathbf{F}^I]{\max_i v_i}$. We first prove it for
finite support distributions by reduction to Lemma 4.13 in the lecture notes.
We then extend the result to general distributions using a density argument
(general distributions can be approximated arbitrarily well by finite support
distributions).
\paragraph{Finite support distributions}
Let us first assume that $\mathbf{F}$ has a finite support. We write $A_i$ the
set of atoms of $\mathbf{F}_i$ (the $i$-th marginal of $\mathbf{F}$) so that
both $\mathbf{F}$ and $\mathbf{F}^I$ define a probability distribution over
$A_1\times\ldots\times A_n$. We now define $A$ to be the disjoint union of
$A_1$ to $A_n$:
\begin{displaymath}
A = \coprod_{i=1}^n A_i
= \big\{ (a, i)\, |\, x\in A_i,\; 1\leq i \leq n\big\}
\end{displaymath}
Note that there is a canonical injection from $A_1\times\ldots\times A_n$ to
$\mathcal{P}(A)$:
\begin{displaymath}
(a_1,\ldots, a_n)\mapsto \big\{ (a_1, 1),\ldots, (a_n, n)\big\}
\end{displaymath}
so that $\mathbf{F}$ and $\mathbf{F}^I$ naturally define a probability
distribution over subsets of $A$:
\begin{displaymath}
\Pr^A_{\mathbf{F}}\big( \{(a_1, i_1),\ldots,(a_k, i_k)\}\big) =
\begin{cases}
\Pr_{\mathbf{F}} (a_1,\ldots, a_k)&\text{if } \{i_1,\ldots,i_k\}
= \{1,\ldots, n\}\\
0&\text{otherwise}
\end{cases}
\end{displaymath}
and similarly for $\Pr^A_{\mathbf{F^I}}$. That is, the only subsets of $A$ which
have a non-zero probability are the ones which ``correspond'' to an $n$-tuple in
$A_1\times\ldots\times A_n$ (they are in the image of the canonical injection
given above).
Similarly, the function $\max_i v_i$ defined over $A_1\times\ldots\times A_n$
can be extended to subsets of $A$ by:
\begin{displaymath}
g(\{(a_1, i_1),\ldots, (a_k, i_k)\}) \eqdef \max\{a_1,\ldots, a_k\}
\end{displaymath}
The consequence of this construction\footnote{This construction might appear
a bit technical. What we would naturally do is to consider the set of all
atoms across all coordinates, but this would not work in cases where the
same atom can appear at different coordinates. This is why we have to
consider the disjoin union, with ``independent'' copies of the atoms.} is
the following key property:
\begin{displaymath}
\E_{S\sim\Pr_{\mathbf{F}}^A}[g(S)] = \E_{\mathbf{F}}[\max_i v_i]
\end{displaymath}
and similarly for $\mathbf{F}^I$. Now, we note that $g$ is
a maximum-weight-element set function, where the weight of $(a, i)\in A$ is
simply $a$. Hence we can apply Lemma 4.13 to $g$ for subsets of $A$ and obtain:
\begin{displaymath}
\E_{\mathbf{F}}[\max_i v_i] =
\E_{S\sim\Pr_{\mathbf{F}}^A}[g(S)]
\geq \frac{e}{e-1}
\E_{S\sim\Pr_{\mathbf{F^I}}^A}[g(S)]=
\E_{\mathbf{F^I}}[\max_i v_i]
\end{displaymath}
\paragraph{General distributions} We first look at a continuous distribution
$\mathbf{F}$ with bounded support. On the support of $\mathbf{F}$, it is
possible to approximate its CDF arbitrarily well in the $L^\infty$ norm (hence in
the $L^2$ norm since we are on a bounded set) by simple functions (convex
combination of step functions). But a step function is simply the CDF of
a Dirac distribution. Hence, we can approximate $\mathbf{F}$ arbitrarily well
in the $L^2$ norm with finite support distributions. But convergence in the $L^2$ norm
implies weak convergence by the Cauchy-Schwarz inequality. That is, we have
a sequence $F_n$ of finite support distributions such that:
\begin{displaymath}
\E_{F_n}[\max_i v_i] \xrightarrow[n\rightarrow \infty]{} \E_F[\max_i v_i]
\end{displaymath}
we can apply the previous result for each of the $F_n$ and take the limit to
obtain again:
\begin{displaymath}
\E_{\mathbf{F}}[\max_i v_i] \geq \E_{\mathbf{F^I}}[\max_i v_i]
\end{displaymath}
When $\mathbf{F}$ does not have bounded support, we can truncate $\mathbf{F}$
by considering its restriction to an increasing sequence of bounded rectangles:
$K_n = \prod_{i=1}^n [-n, n]$. We can apply the previous result to each of the
truncated distributions and again take the limit as $n\rightarrow\infty$. The
validity of this last limit will depend on the exact technical assumptions we
make on $\mathbf{F}$ (basically we need $\mathbf{F}$ to be quickly decreasing
outside of bounded intervals).
More generally, the previous approach will apply as long as we are considering
a distribution $\mathbf{F}$ in a space of distributions for which finite
support distributions are dense for the weak topology. There are various
assumptions with various level of technicality which imply such a density. We
can choose to apply one or the other based on the assumptions we have on
$\mathbf{F}$.
\end{document}
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