We consider a setting where n buyers, with combinatorial preferences over m items, and a seller, running a priority-based allocation mechanism, repeatedly interact. Our goal, from observing limited information about the results of these interactions, is to reconstruct both the preferences of the buyers and the mechanism of the seller. More specifically, we consider an online setting where at each stage, a subset of the buyers arrive and are allocated items, according to some unknown priority that the seller has among the buyers. Our learning algorithm observes only which buyers arrive and the allocation produced (or some function of the allocation, such as just which buyers received positive utility and which did not), and its goal is to predict the outcome for future subsets of buyers. For this task, the learning algorithm needs to reconstruct both the priority among the buyers and the preferences of each buyer. We derive mistake bound algorithms for additive, unit-demand and single-minded buyers. We also consider the case where buyers’ utilities for a fixed bundle can change between stages due to different (observed) prices. Our algorithms are efficient both in computation time and in the maximum number of mistakes (both polynomial in the number of buyers and items).
[1]
P. Samuelson.
A Note on the Pure Theory of Consumer's Behaviour
,
1938
.
[2]
Manfred K. Warmuth,et al.
Learning Nested Differences of Intersection-Closed Concept Classes
,
1989,
COLT '89.
[3]
L. Khachiyan,et al.
On the conductance of order Markov chains
,
1991
.
[4]
Leslie G. Valiant,et al.
Cryptographic limitations on learning Boolean formulae and finite automata
,
1994,
JACM.
[5]
Wolfgang Maass,et al.
How fast can a threshold gate learn
,
1994,
COLT 1994.
[6]
S. Ramanathan.
A unified framework and algorithm for channel assignment in wireless networks
,
1999,
Wirel. Networks.
[7]
Rakesh V. Vohra,et al.
Learning from revealed preference
,
2006,
EC '06.
[8]
H. Varian.
Revealed Preference
,
2006
.
[9]
Morteza Zadimoghaddam,et al.
Efficiently Learning from Revealed Preference
,
2012,
WINE.
[10]
Maria-Florina Balcan,et al.
Learning Economic Parameters from Revealed Preferences
,
2014,
WINE.
[11]
Aaron Roth,et al.
Online Learning and Profit Maximization from Revealed Preferences
,
2014,
AAAI.