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diff --git a/problem.tex b/problem.tex index 4ddaab4..a0a4419 100644 --- a/problem.tex +++ b/problem.tex @@ -1,14 +1,15 @@ +\label{sec:prel} \subsection{Experimental Design} -The theory of experimental design \cite{pukelsheim2006optimal,atkinson2007optimum} studies how an experimenter should select the parameters of a set of experiments she is about to conduct. In general, the optimality of a particular design depends on the purpose of the experiment, \emph{i.e.}, the quantity the experimenter is trying to learn or the hypothesis she is trying to validate. Due to their ubiquity in statistical analysis, a large literature on the subject focuses on learning \emph{linear models}, whereby the experimenter wishes to fit a linear map to the data she has collected. +The theory of experimental design \cite{pukelsheim2006optimal,atkinson2007optimum} studies how an experimenter \E\ should select the parameters of a set of experiments she is about to conduct. In general, the optimality of a particular design depends on the purpose of the experiment, \emph{i.e.}, the quantity \E\ is trying to learn or the hypothesis she is trying to validate. Due to their ubiquity in statistical analysis, a large literature on the subject focuses on learning \emph{linear models}, where \E\ wishes to fit a linear map to the data she has collected. -More precisely, putting cost considerations aside, suppose that an experimenter wishes to conduct $k$ among $n$ possible experiments. Each experiment $i\in\mathcal{N}\defeq \{1,\ldots,n\}$ is associated with a set of parameters (or features) $x_i\in \reals^d$, normalized so that $\|x_i\|_2\leq 1$. Denote by $S\subseteq \mathcal{N}$, where $|S|=k$, the set of experiments selected; upon its execution, experiment $i\in S$ reveals an output variable (the ``measurement'') $y_i$, related to the experiment features $x_i$ through a linear function, \emph{i.e.}, +More precisely, putting cost considerations aside, suppose that \E\ wishes to conduct $k$ among $n$ possible experiments. Each experiment $i\in\mathcal{N}\defeq \{1,\ldots,n\}$ is associated with a set of parameters (or features) $x_i\in \reals^d$, normalized so that $\|x_i\|_2\leq 1$. Denote by $S\subseteq \mathcal{N}$, where $|S|=k$, the set of experiments selected; upon its execution, experiment $i\in S$ reveals an output variable (the ``measurement'') $y_i$, related to the experiment features $x_i$ through a linear function, \emph{i.e.}, \begin{align} y_i = \T{\beta} x_i + \varepsilon_i,\quad\forall i\in\mathcal{N},\label{model} \end{align} where $\beta$ a vector in $\reals^d$, commonly referred to as the \emph{model}, and $\varepsilon_i$ (the \emph{measurement noise}) are independent, normally distributed random variables with mean 0 and variance $\sigma^2$. -The purpose of these experiments is to allow the experimenter to estimate the model $\beta$. In particular, under \eqref{model}, the maximum likelihood estimator of $\beta$ is the \emph{least squares} estimator: for $X_S=[x_i]_{i\in S}\in \reals^{|S|\times d}$ the matrix of experiment features and +The purpose of these experiments is to allow \E\ to estimate the model $\beta$. In particular, under \eqref{model}, the maximum likelihood estimator of $\beta$ is the \emph{least squares} estimator: for $X_S=[x_i]_{i\in S}\in \reals^{|S|\times d}$ the matrix of experiment features and $y_S=[y_i]_{i\in S}\in\reals^{|S|}$ the observed measurements, \begin{align} \hat{\beta} &=\max_{\beta\in\reals^d}\prob(y_S;\beta) =\argmin_{\beta\in\reals^d } \sum_{i\in S}(\T{\beta}x_i-y_i)^2 \nonumber\\ & = (\T{X_S}X_S)^{-1}X_S^Ty_S\label{leastsquares}\end{align} @@ -107,16 +108,24 @@ c_{-i})$ implies $i\in f(c_i', c_{-i})$, and (b) %\end{enumerate} \end{lemma} \fussy +<<<<<<< HEAD +Myerson's Theorem +% is particularly useful when designing a budget feasible truthful mechanism, as it +allows us to focus on designing a monotone allocation function. Then, the +mechanism will be truthful as long as we give each agent her threshold payment---the caveat being that the latter need to sum to a value below $B$. +======= Myerson's Theorem is particularly useful when designing a budget feasible truthful mechanism. One can focus on designing a monotone allocation function, and the resulting mechanism will be truthful as long as each agent is given her threshold payment---the caveat being that the latter need to sum to a value below $B$. +>>>>>>> c29302b25adf190f98019eb8ce5f79b10b66d54d \subsection{Budget Feasible Experimental Design} -In this paper, we approach the problem of optimal experimental design from the + +We approach the problem of optimal experimental design from the perspective of a budget feasible reverse auction, as defined above. - In particular, we assume the experimenter has a budget + In particular, we assume the experimenter \E\ has a budget $B\in\reals_+$ and plays the role of the buyer. Each experiment $i\in \mathcal{N}$ corresponds to a strategic agent, whose cost $c_i$ is @@ -131,10 +140,17 @@ etc.). The cost $c_i$ is the amount the subject deems sufficient to incentivize her participation in the study. Note that, in this setup, the feature vectors $x_i$ are public information that the experimenter can consult prior the experiment design. Moreover, though a subject may lie about her true cost $c_i$, she cannot lie about $x_i$ (\emph{i.e.}, all features are verifiable upon collection) or $y_i$ (\emph{i.e.}, she cannot falsify her measurement). %\subsection{D-Optimality Criterion} +<<<<<<< HEAD +Ideally, motivated by the $D$-optimality criterion, we would like to design a mechanism that maximizes or approximates \eqref{dcrit} . Since \eqref{dcrit} may take arbitrarily small negative values, to define a meaningful approximation one would consider the (equivalent) maximization of $V(S) = \det\T{X_S}X_S$. %, for some strictly increasing, on-to function $f:\reals_+\to\reals_+$. +However, the following lower bound implies that such an optimization goal cannot be attained under the constraints of truthfulness, budget feasibility, and individual rationality. +\begin{lemma} +For any $M>1$, there is no $M$-approximate, truthful, budget feasible, individionally rational mechanism for a budget feasible reverse auction with $V(S) = \det{\T{X_S}X_S}$. +======= Ideally, motivated by the $D$-optimality criterion, we would like to design a mechanism that maximizes \eqref{dcrit} within a good approximation ratio. As \eqref{dcrit} may take arbitrarily small negative values, to define a meaningful approximation one would consider the (equivalent) maximization of $V(S) = \det\T{X_S}X_S$. %, for some strictly increasing, on-to function $f:\reals_+\to\reals_+$. However, the following lower bound implies that such an optimization goal cannot be attained under the constraints of truthfulness, budget feasibility, and individual rationality. \begin{lemma} For any $M>1$, there is no $M$-approximate, truthful, budget feasible, individually rational mechanism for a budget feasible reverse auction with value function $V(S) = \det{\T{X_S}X_S}$. +>>>>>>> c29302b25adf190f98019eb8ce5f79b10b66d54d \end{lemma} \begin{proof} \input{proof_of_lower_bound1} @@ -151,7 +167,9 @@ This negative result motivates us to study a problem with a modified objective: \end{center} where $I_d\in \reals^{d\times d}$ is the identity matrix. One possible interpretation of \eqref{modified} is that, prior to the auction, the experimenter has free access to $d$ experiments whose features form an orthonormal basis in $\reals^d$. However, \eqref{modified} can also be motivated in the context of \emph{Bayesian experimental design} \cite{chaloner1995bayesian}. In short, the objective \eqref{modified} arises naturally when the experimenter retrieves the model $\beta$ through \emph{ridge regression}, rather than the linear regression \eqref{leastsquares} over the observed data; we explore this connection in Section~\ref{sec:bed}. -Note that maximizing \eqref{modified} is equivalent to maximizing \eqref{dcrit} in the full-information case. In particular, $\det(\T{X_S}X_S)> \det(\T{X_{S'}}X_{S'})$ iff $\det(I_d+\T{X_S}X_S)>\det(I_d+\T{X_{S'}}X_{S'})$. In addition, \eqref{edp}---and the equivalent problem with objective \eqref{dcrit}---are NP-hard; to see this, note that \textsc{Knapsack} reduces to EDP with dimension $d=1$ by mapping the weight of each item $w_i$ to an experiment with $x_i=w_i$. Nevertheless, \eqref{modified} is submodular, monotone and satifies $V(\emptyset) = 0$, allowing us to use the extensive machinery for the optimization of submodular functions, as well as recent results in the context of budget feasible auctions \cite{chen,singer-mechanisms}. +Note that maximizing \eqref{modified} is equivalent to maximizing \eqref{dcrit} in the full-information case. In particular, $\det(\T{X_S}X_S)> \det(\T{X_{S'}}X_{S'})$ iff $\det(I_d+\T{X_S}X_S)>\det(I_d+\T{X_{S'}}X_{S'})$. In addition, \eqref{edp}---and the equivalent problem with objective \eqref{dcrit}---are NP-hard; to see this, note that \textsc{Knapsack} reduces to EDP with dimension $d=1$ by mapping the weight of each item $w_i$ to an experiment with $x_i=w_i$. Nevertheless, \eqref{modified} is submodular, monotone and satifies $V(\emptyset) = 0$. +%, allowing us to use the extensive machinery for the optimization of submodular functions, as well as recent results in the +% context of budget feasible auctions \cite{chen,singer-mechanisms}. %\stratis{A potential problem is that all of the above properties hold also for, \emph{e.g.}, $V(S)=\log(1+\det(\T{X_S}X_S))$\ldots} |