669 research outputs found
Non-Separable, Quasiconcave Utilities are Easy -- in a Perfect Price Discrimination Market Model
Recent results, establishing evidence of intractability for such restrictive
utility functions as additively separable, piecewise-linear and concave, under
both Fisher and Arrow-Debreu market models, have prompted the question of
whether we have failed to capture some essential elements of real markets,
which seem to do a good job of finding prices that maintain parity between
supply and demand.
The main point of this paper is to show that even non-separable, quasiconcave
utility functions can be handled efficiently in a suitably chosen, though
natural, realistic and useful, market model; our model allows for perfect price
discrimination. Our model supports unique equilibrium prices and, for the
restriction to concave utilities, satisfies both welfare theorems
The Complexity of Non-Monotone Markets
We introduce the notion of non-monotone utilities, which covers a wide
variety of utility functions in economic theory. We then prove that it is
PPAD-hard to compute an approximate Arrow-Debreu market equilibrium in markets
with linear and non-monotone utilities. Building on this result, we settle the
long-standing open problem regarding the computation of an approximate
Arrow-Debreu market equilibrium in markets with CES utility functions, by
proving that it is PPAD-complete when the Constant Elasticity of Substitution
parameter \rho is any constant less than -1
On Computability of Equilibria in Markets with Production
Although production is an integral part of the Arrow-Debreu market model,
most of the work in theoretical computer science has so far concentrated on
markets without production, i.e., the exchange economy. This paper takes a
significant step towards understanding computational aspects of markets with
production.
We first define the notion of separable, piecewise-linear concave (SPLC)
production by analogy with SPLC utility functions. We then obtain a linear
complementarity problem (LCP) formulation that captures exactly the set of
equilibria for Arrow-Debreu markets with SPLC utilities and SPLC production,
and we give a complementary pivot algorithm for finding an equilibrium. This
settles a question asked by Eaves in 1975 of extending his complementary pivot
algorithm to markets with production.
Since this is a path-following algorithm, we obtain a proof of membership of
this problem in PPAD, using Todd, 1976. We also obtain an elementary proof of
existence of equilibrium (i.e., without using a fixed point theorem),
rationality, and oddness of the number of equilibria. We further give a proof
of PPAD-hardness for this problem and also for its restriction to markets with
linear utilities and SPLC production. Experiments show that our algorithm runs
fast on randomly chosen examples, and unlike previous approaches, it does not
suffer from issues of numerical instability. Additionally, it is strongly
polynomial when the number of goods or the number of agents and firms is
constant. This extends the result of Devanur and Kannan (2008) to markets with
production.
Finally, we show that an LCP-based approach cannot be extended to PLC
(non-separable) production, by constructing an example which has only
irrational equilibria.Comment: An extended abstract will appear in SODA 201
Convex-Concave Min-Max Stackelberg Games
Min-max optimization problems (i.e., min-max games) have been attracting a
great deal of attention because of their applicability to a wide range of
machine learning problems. Although significant progress has been made
recently, the literature to date has focused on games with independent strategy
sets; little is known about solving games with dependent strategy sets, which
can be characterized as min-max Stackelberg games. We introduce two first-order
methods that solve a large class of convex-concave min-max Stackelberg games,
and show that our methods converge in polynomial time. Min-max Stackelberg
games were first studied by Wald, under the posthumous name of Wald's maximin
model, a variant of which is the main paradigm used in robust optimization,
which means that our methods can likewise solve many convex robust optimization
problems. We observe that the computation of competitive equilibria in Fisher
markets also comprises a min-max Stackelberg game. Further, we demonstrate the
efficacy and efficiency of our algorithms in practice by computing competitive
equilibria in Fisher markets with varying utility structures. Our experiments
suggest potential ways to extend our theoretical results, by demonstrating how
different smoothness properties can affect the convergence rate of our
algorithms.Comment: 25 pages, 4 tables, 1 figure, Forthcoming in NeurIPS 202
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