985 research outputs found
Strong Duality for a Multiple-Good Monopolist
We characterize optimal mechanisms for the multiple-good monopoly problem and
provide a framework to find them. We show that a mechanism is optimal if and
only if a measure derived from the buyer's type distribution satisfies
certain stochastic dominance conditions. This measure expresses the marginal
change in the seller's revenue under marginal changes in the rent paid to
subsets of buyer types. As a corollary, we characterize the optimality of
grand-bundling mechanisms, strengthening several results in the literature,
where only sufficient optimality conditions have been derived. As an
application, we show that the optimal mechanism for independent uniform
items each supported on is a grand-bundling mechanism, as long as
is sufficiently large, extending Pavlov's result for items [Pavlov'11]. At
the same time, our characterization also implies that, for all and for all
sufficiently large , the optimal mechanism for independent uniform items
supported on is not a grand bundling mechanism
Bounding the Optimal Revenue of Selling Multiple Goods
Using duality theory techniques we derive simple, closed-form formulas for
bounding the optimal revenue of a monopolist selling many heterogeneous goods,
in the case where the buyer's valuations for the items come i.i.d. from a
uniform distribution and in the case where they follow independent (but not
necessarily identical) exponential distributions. We apply this in order to get
in both these settings specific performance guarantees, as functions of the
number of items , for the simple deterministic selling mechanisms studied by
Hart and Nisan [EC 2012], namely the one that sells the items separately and
the one that offers them all in a single bundle.
We also propose and study the performance of a natural randomized mechanism
for exponential valuations, called Proportional. As an interesting corollary,
for the special case where the exponential distributions are also identical, we
can derive that offering the goods in a single full bundle is the optimal
selling mechanism for any number of items. To our knowledge, this is the first
result of its kind: finding a revenue-maximizing auction in an additive setting
with arbitrarily many goods
Optimal Multi-Unit Mechanisms with Private Demands
In the multi-unit pricing problem, multiple units of a single item are for
sale. A buyer's valuation for units of the item is ,
where the per unit valuation and the capacity are private information
of the buyer. We consider this problem in the Bayesian setting, where the pair
is drawn jointly from a given probability distribution. In the
\emph{unlimited supply} setting, the optimal (revenue maximizing) mechanism is
a pricing problem, i.e., it is a menu of lotteries. In this paper we show that
under a natural regularity condition on the probability distributions, which we
call \emph{decreasing marginal revenue}, the optimal pricing is in fact
\emph{deterministic}. It is a price curve, offering units of the item for a
price of , for every integer . Further, we show that the revenue as a
function of the prices is a \emph{concave} function, which implies that
the optimum price curve can be found in polynomial time. This gives a rare
example of a natural multi-parameter setting where we can show such a clean
characterization of the optimal mechanism. We also give a more detailed
characterization of the optimal prices for the case where there are only two
possible demands
Unit Versus Ad Valorem Taxes : Monopoly In General Equilibrium
We show that if a monopoly sector is imbedded in a general equilibrium framework and profits are taxed at one hundred percent, then unit (specific) taxation and ad valorem taxation are welfare-wise equivalent. This is contrary to all known claims.Ad valorem taxes ; unit taxes ; monopoly
Public Good Menus and Feature Complementarity
The distance metric on the location space for multidimensional public good varieties represents complementarity between the goods features. "Euclidean" feature complementarity has atypical strong properties that lead to a failure of intuition about the optimal-menu design problem. If the population is heterogeneous, increasing the distance between two varieties is welfare-improving in Euclidean space, but not generally. A basic optimal-direction principle always applies: "anticonvex" menu changes increase participation and surplus. A menu replacement is anticonvex if it moves the varieties apart in the common line space. The result extends to some impure public goods with break-even pricing and variety-specic costs. A sufficient condition for menus to be Pareto-optimal is that "personal price" (nominal price plus perceived distance from a variety) is linear in the norm that induces the distance metric.Public Good Menus; complementarity
Multi-dimensional Virtual Values and Second-degree Price Discrimination
We consider a multi-dimensional screening problem of selling a product with
multiple quality levels and design virtual value functions to derive conditions
that imply optimality of only selling highest quality. A challenge of designing
virtual values for multi-dimensional agents is that a mechanism that pointwise
optimizes virtual values resulting from a general application of integration by
parts is not incentive compatible, and no general methodology is known for
selecting the right paths for integration by parts. We resolve this issue by
first uniquely solving for paths that satisfy certain necessary conditions that
the pointwise optimality of the mechanism imposes on virtual values, and then
identifying distributions that ensure the resulting virtual surplus is indeed
pointwise optimized by the mechanism. Our method of solving for virtual values
is general, and as a second application we use it to derive conditions of
optimality for selling only the grand bundle of items to an agent with additive
preferences
Counterfactual Sensitivity and Robustness
Researchers frequently make parametric assumptions about the distribution of
unobservables when formulating structural models. Such assumptions are
typically motived by computational convenience rather than economic theory and
are often untestable. Counterfactuals can be particularly sensitive to such
assumptions, threatening the credibility of structural modeling exercises. To
address this issue, we leverage insights from the literature on ambiguity and
model uncertainty to propose a tractable econometric framework for
characterizing the sensitivity of counterfactuals with respect to a
researcher's assumptions about the distribution of unobservables in a class of
structural models. In particular, we show how to construct the smallest and
largest values of the counterfactual as the distribution of unobservables spans
nonparametric neighborhoods of the researcher's assumed specification while
other `structural' features of the model, e.g. equilibrium conditions, are
maintained. Our methods are computationally simple to implement, with the
nuisance distribution effectively profiled out via a low-dimensional convex
program. Our procedure delivers sharp bounds for the identified set of
counterfactuals (i.e. without parametric assumptions about the distribution of
unobservables) as the neighborhoods become large. Over small neighborhoods, we
relate our procedure to a measure of local sensitivity which is further
characterized using an influence function representation. We provide a suitable
sampling theory for plug-in estimators and apply our procedure to models of
strategic interaction and dynamic discrete choice
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