Summary: Collaborate With Strangers to Find Own Preferences #
Baruch Awerbuch + Yossi Azar # Zvi Lotker § Boaz PattShamir ¶ Mark R. Tuttle #
March 15, 2006
We consider a model with n players and m objects. Each player has a ``preference vector'' of
length m, that models his grades for all objects. The grades are initially unknown to the players.
A player can learn his grade for an object by probing that object, but performing a probe incurs
cost. The goal of a player is to learn his preference vector with minimal cost, by adopting the
results of probes performed by other players. To facilitate communication, we assume that players
collaborate by posting their grades for objects on a shared billboard: reading from the billboard
is free. We consider players whose preference vectors are popular, i.e., players whose preferences
are common to many other players. We present a sequential and a parallel algorithm to solve the
problem with logarithmic cost overhead.
# An extended abstract of this work appeared in the 17th Ann. ACM Symp. on Parallelism in Algorithms and
Architecture, Las Vegas, Nevada, July 2005.
+ Dept. of Computer Science, Johns Hopkins University. Email: firstname.lastname@example.org. Supported by NSF grant
ANIR0240551 and NSF grant CCR0311795.
# School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel. Email: email@example.com. Research sup
ported in part by the GermanIsraeli Foundation and by the Israel Science Foundation.
§ Kruislaan 413 P.O. Box 94079, CWI, 1090 GB Amsterdam, The Netherlands. Email: firstname.lastname@example.org.