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authorPaul <Paul@Pauls-MacBook-Air.local>2015-05-11 18:45:27 -0400
committerPaul <Paul@Pauls-MacBook-Air.local>2015-05-11 18:45:27 -0400
commitfae05f1565ab895abe78a2daa93ab93b4c3d97d9 (patch)
tree020e1d60201f51080ced76c7ab2e8f6ccb527478
parentf3d8ab85617758f7a465dc9bc8b000d07dcfd884 (diff)
downloadecon2099-fae05f1565ab895abe78a2daa93ab93b4c3d97d9.tar.gz
Some updates.
-rw-r--r--final/main.tex33
1 files changed, 17 insertions, 16 deletions
diff --git a/final/main.tex b/final/main.tex
index ac8f87d..198b8ab 100644
--- a/final/main.tex
+++ b/final/main.tex
@@ -44,6 +44,7 @@
\author{Thibaut Horel \and Paul Tylkin}
\title{Economics 2099 Project}
+\date{Fall 2014}
\begin{document}
\maketitle
@@ -54,12 +55,12 @@ for this project.}
\section{Introduction}
\label{sec:intro}
-\subsection{Problem}
+\subsection{Setup of the Problem}
The exposition in this section draws from \cite{hartlinebayes}. We consider the
problem of selling $m$ heterogeneous items to a single agent with additive
utility. The type $t$ of the agent is drawn from a distribution $F$ over
-$\R_+^m$. For an allocation $x = (x_1,\ldots,x_m)$ where $x_i$, $i\in[m]$,
+$\R_+^m$. We use the notation $[m]$ for $\{1,...,m\}$. For an allocation $x = (x_1,\ldots,x_m)$ where $x_i$, $i\in[m]$,
denotes the probability of being allocated item $i$ and payment $p$, the
utility of an agent with type $t=(t_1,\ldots, t_m)$ is defined by:
\begin{displaymath}
@@ -86,17 +87,17 @@ obtained as the solution to the following optimization problem:
where the first constraint expresses incentive compatibility and the second
constraint expresses individual rationality. As noted in \cite{hartlinebayes},
this formulation can easily be extended to support additional constraints which
-arise in natural scenarios: budget constraints or matroid constraints for
+arise in natural scenarios: budget constraints or matroid constraints, for
example.
If the distribution $F$ is correlated, a result from \cite{hart} shows that the
gap between the revenue of auctions with finite menu size and
a revenue-maximizing auction can be arbitrarily large. When $F$ is a product
-distribution however, there is a hope to obtain simple auctions with constant
-approximation ratios to the optimal revenue. In fact, a result from
-\cite{babaioff} shows that the maximum of \emph{item pricing} where each item
-is priced separately at its monopoly reserve price and \emph{bundle pricing}
-where the grand bundle of all items is priced and sold as a single item
+distribution, however, there is a possibility of obtaining a simple auction with a constant
+approximation ratio to the optimal revenue. In fact, a result from
+\cite{babaioff} shows that the maximum of \emph{item pricing}, where each item
+is priced separately at its monopoly reserve price, and \emph{bundle pricing},
+where the grand bundle of all items is priced and sold as a single item,
achieves a 6-approximation to the optimal revenue.
We propose to extend this result by considering a more general version of
@@ -136,7 +137,7 @@ single-item auction (see \cite{hartline}, Chapter 3).
In particular, if the type of the agent (her value) is drawn from a regular
distribution, the posted price revenue curve is concave and equal to the
-revenue curve. In this case, the optimal mechanism which serves with ex-ante
+revenue curve. In this case, the optimal mechanism which serves the agent with ex-ante
allocation probability $\hat{x}$ is a simple posted-price mechanism with price
$p_{\hat{x}} = F^{-1}(1-\hat{x})$.
@@ -144,20 +145,20 @@ $p_{\hat{x}} = F^{-1}(1-\hat{x})$.
fully understood yet, but the revenue function $R(\hat{x})$ defined above still
seems to play an important role in understanding the revenue optimal auction.
For example, it is noted in \cite{alaei} that $R(\hat{x})$ is a concave
-function of $\hat{x}$. This allows to define a convex optimization program
-(where the objective function is the sum of the revenue curve for all agents)
+function of $\hat{x}$. This allows us to define a convex optimization program
+(where the objective function is the sum of the revenue curves for all agents)
whose optimal value is an upper bound on the expected value of the
-multiple-agent revenue optimal mechanism.
+multiple-agent revenue-optimal mechanism.
Furthermore, in the same paper, Alaei shows that under fairly general
assumptions, given an $\alpha$-approximate mechanism for the single-agent,
-ex-ante allocation constrained problem it is possible to construct in
+ex-ante allocation constrained problem, it is possible to construct in
polynomial time a mechanism for the multi-agent problem which is
a $\gamma\cdot\alpha$ approximation to the revenue-optimal mechanism where
$\gamma$ is a constant which is at least $\frac{1}{2}$.
-\section{A two-part tariff mechanism}
+\section{A Two-Part Tariff Mechanism}
\subsection{Definition of the Mechanism}
@@ -180,8 +181,8 @@ restore individual rationality, we need to have the agent pay only when:
\begin{displaymath}
\sum_{i=1}^m (t_i - p_i)^+ \geq p_0
\end{displaymath}
-where we used the notation $(x)^+\eqdef\max\{x, 0\}$, so that the sum in the
-above inequality could also be written $\sum_{i: t_i\geq p_i} (t_i- p_i)$.
+where we used the notation $(x)^+\eqdef\max\{x, 0\}$, so that the
+above inequality could also be written as $$\sum_{i: t_i\geq p_i} (t_i- p_i)\geq p_0.$$
We can now formally describe the candidate mechanism.