29,536 research outputs found
Competitive Equilibria in Decentralized Matching with Incomplete Information
This paper shows that all perfect Bayesian equilibria of a decentralized dynamic matching market with two-sided incomplete information of independent private values variety converge to competitive equilibria. Each buyer wants to purchase a bundle of heterogeneous, indivisible goods and each seller owns one unit of a heterogeneous indivisible good (as in Kelso and Crawford (1982) or Gul and Stacchetti (1999)). Buyer preferences and endowments as well as seller costs are private information. Agents engage in costly search and meet randomly. The terms of trade are determined through bilateral bargaining between buyers and sellers. The paper considers a market in steady state. It is shown that as frictions, i.e., discounting and fixed costs of search become small, all equilibria of the market game converge to perfectly competitive equilibria.Bargaining, Search, Matching
Competitive Equilibria in Decentralized Matching with Incomplete Information
This paper shows that all perfect Bayesian equilibria of a dynamic matching game with two-sided incomplete information of independent private values variety are asymptotically Walrasian. Buyers purchase a bundle of heterogeneous, indivisible goods and sellers own one unit of an indivisible good. Buyer preferences and endowments as well as seller costs are private information. Agents engage in costly search and meet randomly. The terms of trade are determined through a Bayesian mechanism proposal game. The paper considers a market in steady state. As discounting and the fixed cost of search become small, all trade takes place at a Walrasian price. However, a robust example is presented where the limit price vector is a Walrasian price for an economy where only a strict subsets of the goods in the original economy are traded, i.e, markets are missing at the limit. Nevertheless, there exists a sequence of equilibria that converge to a Walrasian equilibria for the whole economy where all markets are open.Conditional CAPM
Matching Theory for Future Wireless Networks: Fundamentals and Applications
The emergence of novel wireless networking paradigms such as small cell and
cognitive radio networks has forever transformed the way in which wireless
systems are operated. In particular, the need for self-organizing solutions to
manage the scarce spectral resources has become a prevalent theme in many
emerging wireless systems. In this paper, the first comprehensive tutorial on
the use of matching theory, a Nobelprize winning framework, for resource
management in wireless networks is developed. To cater for the unique features
of emerging wireless networks, a novel, wireless-oriented classification of
matching theory is proposed. Then, the key solution concepts and algorithmic
implementations of this framework are exposed. Then, the developed concepts are
applied in three important wireless networking areas in order to demonstrate
the usefulness of this analytical tool. Results show how matching theory can
effectively improve the performance of resource allocation in all three
applications discussed
Matching Dynamics with Constraints
We study uncoordinated matching markets with additional local constraints
that capture, e.g., restricted information, visibility, or externalities in
markets. Each agent is a node in a fixed matching network and strives to be
matched to another agent. Each agent has a complete preference list over all
other agents it can be matched with. However, depending on the constraints and
the current state of the game, not all possible partners are available for
matching at all times. For correlated preferences, we propose and study a
general class of hedonic coalition formation games that we call coalition
formation games with constraints. This class includes and extends many recently
studied variants of stable matching, such as locally stable matching, socially
stable matching, or friendship matching. Perhaps surprisingly, we show that all
these variants are encompassed in a class of "consistent" instances that always
allow a polynomial improvement sequence to a stable state. In addition, we show
that for consistent instances there always exists a polynomial sequence to
every reachable state. Our characterization is tight in the sense that we
provide exponential lower bounds when each of the requirements for consistency
is violated. We also analyze matching with uncorrelated preferences, where we
obtain a larger variety of results. While socially stable matching always
allows a polynomial sequence to a stable state, for other classes different
additional assumptions are sufficient to guarantee the same results. For the
problem of reaching a given stable state, we show NP-hardness in almost all
considered classes of matching games.Comment: Conference Version in WINE 201
Stable Matching with Evolving Preferences
We consider the problem of stable matching with dynamic preference lists. At
each time step, the preference list of some player may change by swapping
random adjacent members. The goal of a central agency (algorithm) is to
maintain an approximately stable matching (in terms of number of blocking
pairs) at all times. The changes in the preference lists are not reported to
the algorithm, but must instead be probed explicitly by the algorithm. We
design an algorithm that in expectation and with high probability maintains a
matching that has at most blocking pairs.Comment: 13 page
Locally Stable Marriage with Strict Preferences
We study stable matching problems with locality of information and control.
In our model, each agent is a node in a fixed network and strives to be matched
to another agent. An agent has a complete preference list over all other agents
it can be matched with. Agents can match arbitrarily, and they learn about
possible partners dynamically based on their current neighborhood. We consider
convergence of dynamics to locally stable matchings -- states that are stable
with respect to their imposed information structure in the network. In the
two-sided case of stable marriage in which existence is guaranteed, we show
that the existence of a path to stability becomes NP-hard to decide. This holds
even when the network exists only among one partition of agents. In contrast,
if one partition has no network and agents remember a previous match every
round, a path to stability is guaranteed and random dynamics converge with
probability 1. We characterize this positive result in various ways. For
instance, it holds for random memory and for cache memory with the most recent
partner, but not for cache memory with the best partner. Also, it is crucial
which partition of the agents has memory. Finally, we present results for
centralized computation of locally stable matchings, i.e., computing maximum
locally stable matchings in the two-sided case and deciding existence in the
roommates case.Comment: Conference version in ICALP 2013; to appear in SIAM J. Disc Mat
Backward Unraveling over Time: The Evolution of Strategic Behavior in the Entry-Level British Medical Labor Markets
This paper studies an adaptive artificial agent model using a genetic algorithm to analyze how a population of decision-makers learns to coordinate on the selection of an equilibrium or a social convention in a two-sided matching game. In the contexts of centralized and decentralized entry-level labor markets, evolution and adjustment paths of unraveling are explored using this model in an environment inspired by the Kagel and Roth (Quarterly Journal of Economics, 2000) experimental study. As an interesting result, it is demonstrated that stability need not be required for the success of a matching mechanism under incomplete information in the long run.Genetic algorithms, linear programming matching, stability, two-sided matching, unraveling
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