37 research outputs found
When Are Welfare Guarantees Robust?
Computational and economic results suggest that social welfare maximization and combinatorial auction design are much easier when bidders\u27 valuations satisfy the "gross substitutes" condition. The goal of this paper is to evaluate rigorously the folklore belief that the main take-aways from these results remain valid in settings where the gross substitutes condition holds only approximately. We show that for valuations that pointwise approximate a gross substitutes valuation (in fact even a linear valuation), optimal social welfare cannot be approximated to within a subpolynomial factor and demand oracles cannot be simulated using a subexponential number of value queries. We then provide several positive results by imposing additional structure on the valuations (beyond gross substitutes), using a more stringent notion of approximation, and/or using more powerful oracle access to the valuations. For example, we prove that the performance of the greedy algorithm degrades gracefully for near-linear valuations with approximately decreasing marginal values; that with demand queries, approximate welfare guarantees for XOS valuations degrade gracefully for valuations that are pointwise close to XOS; and that the performance of the Kelso-Crawford auction degrades gracefully for valuations that are close to various subclasses of gross substitutes valuations
Oblivious Rounding and the Integrality Gap
The following paradigm is often used for handling NP-hard combinatorial optimization problems. One first formulates the problem as an integer program, then one relaxes it to a linear program (LP, or more generally, a convex program), then one solves the LP relaxation in polynomial time, and finally one rounds the optimal LP solution, obtaining a feasible solution to the original problem. Many of the commonly used rounding schemes (such as randomized rounding, threshold rounding and others) are "oblivious" in the sense that the rounding is performed based on the LP solution alone, disregarding the objective function. The goal of our work is to better understand in which cases oblivious rounding suffices in order to obtain approximation ratios that match the integrality gap of the underlying LP. Our study is information theoretic - the rounding is restricted to be oblivious but not restricted to run in polynomial time. In this information theoretic setting we characterize the approximation ratio achievable by oblivious rounding. It turns out to equal the integrality gap of the underlying LP on a problem that is the closure of the original combinatorial optimization problem. We apply our findings to the study of the approximation ratios obtainable by oblivious rounding for the maximum welfare problem, showing that when valuation functions are submodular oblivious rounding can match the integrality gap of the configuration LP (though we do not know what this integrality gap is), but when valuation functions are gross substitutes oblivious rounding cannot match the integrality gap (which is 1)
Interdependent Public Projects
In the interdependent values (IDV) model introduced by Milgrom and Weber
[1982], agents have private signals that capture their information about
different social alternatives, and the valuation of every agent is a function
of all agent signals. While interdependence has been mainly studied for
auctions, it is extremely relevant for a large variety of social choice
settings, including the canonical setting of public projects. The IDV model is
very challenging relative to standard independent private values, and welfare
guarantees have been achieved through two alternative conditions known as {\em
single-crossing} and {\em submodularity over signals (SOS)}. In either case,
the existing theory falls short of solving the public projects setting.
Our contribution is twofold: (i) We give a workable characterization of
truthfulness for IDV public projects for the largest class of valuations for
which such a characterization exists, and term this class \emph{decomposable
valuations}; (ii) We provide possibility and impossibility results for welfare
approximation in public projects with SOS valuations. Our main impossibility
result is that, in contrast to auctions, no universally truthful mechanism
performs better for public projects with SOS valuations than choosing a project
at random. Our main positive result applies to {\em excludable} public projects
with SOS, for which we establish a constant factor approximation similar to
auctions. Our results suggest that exclusion may be a key tool for achieving
welfare guarantees in the IDV model
Bayesian Analysis of Linear Contracts
We provide a justification for the prevalence of linear (commission-based)
contracts in practice under the Bayesian framework. We consider a hidden-action
principal-agent model, in which actions require different amounts of effort,
and the agent's cost per-unit-of-effort is private. We show that linear
contracts are near-optimal whenever there is sufficient uncertainty in the
principal-agent setting
Multi-Channel Bayesian Persuasion
The celebrated Bayesian persuasion model considers strategic communication
between an informed agent (the sender) and uninformed decision makers (the
receivers). The current rapidly-growing literature mostly assumes a dichotomy:
either the sender is powerful enough to communicate separately with each
receiver (a.k.a. private persuasion), or she cannot communicate separately at
all (a.k.a. public persuasion). We study a model that smoothly interpolates
between the two, by considering a natural multi-channel communication structure
in which each receiver observes a subset of the sender's communication
channels. This captures, e.g., receivers on a network, where information
spillover is almost inevitable.
We completely characterize when one communication structure is better for the
sender than another, in the sense of yielding higher optimal expected utility
universally over all prior distributions and utility functions. The
characterization is based on a simple pairwise relation among receivers - one
receiver information-dominates another if he observes at least the same
channels. We prove that a communication structure is (weakly) better than
if and only if every information-dominating pair of receivers in is
also such in . We also provide an additive FPTAS for the optimal sender's
signaling scheme when the number of states is constant and the graph of
information-dominating pairs is a directed forest. Finally, we prove that
finding an optimal signaling scheme under multi-channel persuasion is,
generally, computationally harder than under both public and private
persuasion
Algorithmic Cheap Talk
The literature on strategic communication originated with the influential
cheap talk model, which precedes the Bayesian persuasion model by three
decades. This model describes an interaction between two agents: sender and
receiver. The sender knows some state of the world which the receiver does not
know, and tries to influence the receiver's action by communicating a cheap
talk message to the receiver.
This paper initiates the algorithmic study of cheap talk in a finite
environment (i.e., a finite number of states and receiver's possible actions).
We first prove that approximating the sender-optimal or the welfare-maximizing
cheap talk equilibrium up to a certain additive constant or multiplicative
factor is NP-hard. Fortunately, we identify three naturally-restricted cases
that admit efficient algorithms for finding a sender-optimal equilibrium. These
include a state-independent sender's utility structure, a constant number of
states or a receiver having only two actions