19,512 research outputs found
An Incentive Compatible Multi-Armed-Bandit Crowdsourcing Mechanism with Quality Assurance
Consider a requester who wishes to crowdsource a series of identical binary
labeling tasks to a pool of workers so as to achieve an assured accuracy for
each task, in a cost optimal way. The workers are heterogeneous with unknown
but fixed qualities and their costs are private. The problem is to select for
each task an optimal subset of workers so that the outcome obtained from the
selected workers guarantees a target accuracy level. The problem is a
challenging one even in a non strategic setting since the accuracy of
aggregated label depends on unknown qualities. We develop a novel multi-armed
bandit (MAB) mechanism for solving this problem. First, we propose a framework,
Assured Accuracy Bandit (AAB), which leads to an MAB algorithm, Constrained
Confidence Bound for a Non Strategic setting (CCB-NS). We derive an upper bound
on the number of time steps the algorithm chooses a sub-optimal set that
depends on the target accuracy level and true qualities. A more challenging
situation arises when the requester not only has to learn the qualities of the
workers but also elicit their true costs. We modify the CCB-NS algorithm to
obtain an adaptive exploration separated algorithm which we call { \em
Constrained Confidence Bound for a Strategic setting (CCB-S)}. CCB-S algorithm
produces an ex-post monotone allocation rule and thus can be transformed into
an ex-post incentive compatible and ex-post individually rational mechanism
that learns the qualities of the workers and guarantees a given target accuracy
level in a cost optimal way. We provide a lower bound on the number of times
any algorithm should select a sub-optimal set and we see that the lower bound
matches our upper bound upto a constant factor. We provide insights on the
practical implementation of this framework through an illustrative example and
we show the efficacy of our algorithms through simulations
Fairs for e-commerce: the benefits of aggregating buyers and sellers
In recent years, many new and interesting models of successful online
business have been developed. Many of these are based on the competition
between users, such as online auctions, where the product price is not fixed
and tends to rise. Other models, including group-buying, are based on
cooperation between users, characterized by a dynamic price of the product that
tends to go down. There is not yet a business model in which both sellers and
buyers are grouped in order to negotiate on a specific product or service. The
present study investigates a new extension of the group-buying model, called
fair, which allows aggregation of demand and supply for price optimization, in
a cooperative manner. Additionally, our system also aggregates products and
destinations for shipping optimization. We introduced the following new
relevant input parameters in order to implement a double-side aggregation: (a)
price-quantity curves provided by the seller; (b) waiting time, that is, the
longer buyers wait, the greater discount they get; (c) payment time, which
determines if the buyer pays before, during or after receiving the product; (d)
the distance between the place where products are available and the place of
shipment, provided in advance by the buyer or dynamically suggested by the
system. To analyze the proposed model we implemented a system prototype and a
simulator that allow to study effects of changing some input parameters. We
analyzed the dynamic price model in fairs having one single seller and a
combination of selected sellers. The results are very encouraging and motivate
further investigation on this topic
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