26 research outputs found
Computing large market equilibria using abstractions
Computing market equilibria is an important practical problem for market
design (e.g. fair division, item allocation). However, computing equilibria
requires large amounts of information (e.g. all valuations for all buyers for
all items) and compute power. We consider ameliorating these issues by applying
a method used for solving complex games: constructing a coarsened abstraction
of a given market, solving for the equilibrium in the abstraction, and lifting
the prices and allocations back to the original market. We show how to bound
important quantities such as regret, envy, Nash social welfare, Pareto
optimality, and maximin share when the abstracted prices and allocations are
used in place of the real equilibrium. We then study two abstraction methods of
interest for practitioners: 1) filling in unknown valuations using techniques
from matrix completion, 2) reducing the problem size by aggregating groups of
buyers/items into smaller numbers of representative buyers/items and solving
for equilibrium in this coarsened market. We find that in real data
allocations/prices that are relatively close to equilibria can be computed from
even very coarse abstractions
Pacing Equilibrium in First-Price Auction Markets
In the isolated auction of a single item, second price often dominates first
price in properties of theoretical interest. But, single items are rarely sold
in true isolation, so considering the broader context is critical when adopting
a pricing strategy. In this paper, we study a model centrally relevant to
Internet advertising and show that when items (ad impressions) are individually
auctioned within the context of a larger system that is managing budgets,
theory offers surprising endorsement for using a first price auction to sell
each individual item. In particular, first price auctions offer theoretical
guarantees of equilibrium uniqueness, monotonicity, and other desirable
properties, as well as efficient computability as the solution to the
well-studied Eisenberg-Gale convex program. We also use simulations to
demonstrate that a bidder's incentive to deviate vanishes in thick markets
The Cost of Moral Hazard and Limited Liability in the Principal-Agent Problem
Abstract. In the classical principal-agent problem, a principal hires an agent to perform a task. The principal cares about the task's output but has no control over it. The agent can perform the task at different effort intensities, and that choice affects the task's output. To provide an incentive to the agent to work hard and since his effort intensity cannot be observed, the principal ties the agent's compensation to the task's output. If both the principal and the agent are risk-neutral and no further constraints are imposed, it is well-known that the outcome of the game maximizes social welfare. In this paper we quantify the potential social-welfare loss due to the existence of limited liability, which takes the form of a minimum wage constraint. To do so we rely on the worst-case welfare loss-commonly referred to as the Price of Anarchy-which quantifies the (in)efficiency of a system when its players act selfishly (i.e., they play a Nash equilibrium) versus choosing a socially-optimal solution. Our main result establishes that under the monotone likelihood-ratio property and limited liability constraints, the worst-case welfare loss in the principal-agent model is exactly equal to the number of efforts available
A Note on the Precedence-Constrained Class Sequencing Problem
We give a short proof of a result of Tovey [5] on the inapproximability of a scheduling problem known as precedence constrained class sequencing. In addition we present an approximation algorithm with performance guarantee (c + 1)/2, where c is the number of colors. This improves upon Tovey's c--approximation