The following technical report is available from
http://aib.informatik.rwth-aachen.de:
Uncoordinated Two-Sided Markets
Heiner Ackermann, Paul W. Goldberg, Vahab S. Mirrokni, Heiko Röglin, and Berthold Vöcking
AIB 2007-22
Various economic interactions can be modeled as two-sided markets.
A central solution concept to these markets are stable matchings,
introduced by Gale and Shapley. It is well known that stable matchings
can be computed in polynomial time, but many real-life markets lack a
central authority to match agents. In those markets, matchings are formed
by actions of self-interested agents. Knuth introduced uncoordinated
two-sided markets and showed that the uncoordinated better response
dynamics may cycle. However, Roth and Vande Vate showed that the random
better response dynamics converges to a stable matching with probability
one, but did not address the question of convergence time.
In this paper, we give an exponential lower bound for the convergence
time of the random better response dynamics in two-sided markets. We also
extend these results to the best response dynamics, i.e., we present
a cycle of best responses, and prove that the random best response
dynamics converges to a stable matching with probability one, but its
convergence time is exponential. Additionally, we identify the special
class of correlated two-sided markets with real-life applications for
which we prove that the random best response dynamics converges in
expected polynomial time.
The following technical report is available from
http://aib.informatik.rwth-aachen.de:
Three-Valued Abstraction for Probabilistic Systems
Joost-Pieter Katoen, Daniel Klink, Martin Leucker, and Verena Wolf
AIB 2007-20
This paper proposes a novel abstraction technique for fully probabilistic
systems. The models of our study are classical discrete-time and
continuous time Markov chains (DTMCs and CTMCs, for short). A DTMC is
a Kripke structure in which each transition is equipped with a discrete
probability; in a CTMC, in addition, state residence times are governed
by negative exponential distributions. Our abstraction technique fits
within the realm of three-valued abstraction methods that have been
used successfully for traditional model checking. The key ingredients
of our technique are a partitioning of the state space combined with an
abstraction of transition probabilities by intervals. The uncertainty of
intervals is resolved by history-dependent schedulers that may choose
extreme values only. It is shown that this provides a conservative
abstraction for both negative and affirmative verification results for a
three-valued semantics of PCTL (Probabilistic Computation Tree Logic). In
the continuous-time setting, the key idea is to apply abstraction on
uniform CTMCs which are readily obtained from general CTMCs. In a similar
way as for the discrete case, this is shown to yield a conservative
abstraction for a three-valued semantics of CSL (Continuous Stochastic
Logic). The verification of abstract DTMCs is inspired by the standard
MDP (Markov Decision Process) model-checking problem. Abstract CTMCs
can be verified by computing time-bounded reachability probabilities in
continuous-time MDPs. Some experiments on an infinite-state stochastic
Petri net indicate the feasibility of our abstraction technique.