\documentclass[10pt]{article} \usepackage{amsmath,amsfonts,amsthm} \usepackage[english]{babel} \usepackage{paralist} \usepackage[utf8x]{inputenc} \usepackage[pagebackref=true,breaklinks=true,colorlinks=true]{hyperref} \usepackage[capitalize, noabbrev]{cleveref} % these are compressed lists to help fit into a 1 page limit \newenvironment{enumerate*}% {\vspace{-2ex} \begin{enumerate} % \setlength{\itemsep}{-1ex} \setlength{\parsep}{0pt}}% {\end{enumerate}} \newenvironment{itemize*}% {\vspace{-2ex} \begin{itemize} % \setlength{\itemsep}{-1ex} \setlength{\parsep}{0pt}}% {\end{itemize}} \newenvironment{description*}% {\vspace{-2ex} \begin{description} % \setlength{\itemsep}{-1ex} \setlength{\parsep}{0pt}}% {\end{description}} \DeclareMathOperator*{\E}{\mathbb{E}} \let\Pr\relax \DeclareMathOperator*{\Pr}{\mathbb{P}} \newcommand{\inprod}[1]{\left\langle #1 \right\rangle} \newcommand{\R}{\mathbb{R}} \newcommand{\eqdef}{\mathbin{\stackrel{\rm def}{=}}} \newcommand{\llbracket}{[\![} \newtheorem{theorem}{Theorem} \newtheorem{lemma}{Lemma} \newtheorem*{goal}{Goal} \author{Thibaut Horel \and Paul Tylkin} \title{Economics 2099 Project} \begin{document} \maketitle \section{Introduction} 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 $x_i$, $i\in[m]$, 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} u(t, x, p) = \left(\sum_{i=1}^m x_i t_i\right) - p \end{displaymath} By the taxation principle, a mechanism for this setting can be described by a pair $x(\cdot), p(\cdot)$ where the allocation rule $x$ and the payment rule $p$ are indexed by the agent's type. For an agent with type $t$, $\big(x(t), p(t)\big)$ is her preferred menu option. The revenue-optimal mechanism for this single-agent problem can be formally obtained as the solution to the following optimization problem: \begin{equation} \label{eq:opt} \begin{split} \max_{(x, p)} & \E_{t\sim F}\big[p(t)\big]\\ \text{s.t.} &\, u(t, x(t), p(t)) \geq u(t', x(t'), p(t')),\quad \forall t, t'\in\R_+^m\\ &\, u(t, x(t), p(t)) \geq 0,\quad\forall t\in\R_+^m\\ &\,x_i(t)\leq 1,\quad \forall t\in\R_+^m,\,\forall i\in[m] \end{split} \end{equation} where the first constraint expresses incentive compatibility and the second constraint expresses individual rationality. Unfortunately, it has been shown in \cite{hart} that even in this simple case and as soon as $m\geq 2$, the optimal mechanism can have a menu size which is exponential in $m$. Therefore it is important to understand to which extent simple mechanisms can approximate the revenue-optimal mechanism. A remarkable result of \cite{babaioff} proves that a simple mechanism provides a constant approximation to Problem~\ref{eq:opt}. More precisely, it is shown 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 together achieves a 6-approximation to the optimal mechanism. We propose to extend this result by considering a more general version of Problem~\ref{eq:opt} where there is an additional \emph{ex-ante allocation constraint}. Formally, we are given a vector $\hat{x} = (\hat{x}_1,\ldots,\hat{x}_m)$ and we add the following constraint to Problem~\ref{eq:opt}: \begin{equation} \label{eq:ea} \E_{t\sim F}\big[x_i(t)] \leq \hat{x}_i,\quad\forall i\in[m] \end{equation} which expresses the assumption that the ex-ante allocation rule is upper-bounded by $\hat{x}$. To provide some more intuition about this, we assume that the buyer is charged a price $p_0$ to participate in the mechanism, and then is offered a menu of goods with prices $p_1,...,p_m$. The buyer's utility over the goods is additive, as above. However, there is an ex-ante constraint of being allocated a given good $i$, given by $\hat{x}_i$. For each good, if the buyer is allocated the good, which he is with probability $x_i \leq \hat{x}_i$, then he pays $p_i$; otherwise, he pays nothing. This is essentially the concept of a two-part tariff, as discussed in \cite{armstrong}. Let us denote by $R(\hat{x})$ the revenue obtained by the optimal mechanism solution to Problem~\ref{eq:opt} with ex-ante allocation constraint \eqref{eq:ea}. \paragraph{Problem.} \emph{Given an ex-ante allocation constraint $\hat{x}$, is there a simple mechanism whose ex-ante allocation rule is upper-bounded by $\hat{x}$ and whose revenue is a constant approximation to $R(\hat{x})$?} \paragraph{} One reason why one might want to consider this problem is that it can be used as a building block for more general mechanisms. Indeed, in \cite{alaei}, 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 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 $\frac{1}{2}$. It is interesting to consider two specific cases for which we already have an answer to our problem: \begin{itemize} \item when $\hat{x} = (1,\ldots,1)$, \eqref{eq:ea} does not further constrain Problem~\ref{eq:opt}, i.e. the constraint is trivially satisfied. In this case, as discussed above, the mechanism described in \cite{babaioff} provides a 6-approximation. \item when $\hat{x} = (\frac{1}{m},\ldots,\frac{1}{m})$, by summing the constraint \eqref{eq:ea} for all $i\in[m]$, we see that the ex-ante allocation constraint implies that in expectation, no more than one-item is sold to the agent. This is exactly the unit-demand case for which TODO:cite provides a 2 approximation. \end{itemize} \section{Related Work} In \cite{babaioff}, the authors describe a setting with a monopolist seller, offering $n$ heterogeneous goods, and a single buyer. \cite{hart} \cite{hartline} \cite{yao} \cite{armstrong} \cite{alaei} \bibliographystyle{abbrv} \bibliography{main} \end{document}