77 research outputs found
5 minutes with Milan Vojnovic
Professor Milan Vojnovic is a Chair in Data Science in the Department of Statistics, London School and Economics & Political Science. With the publication of Contest Theory, we took the opportunity to interview the author to find out more about his book and how it relates to his career
Incentives and Efficiency in Uncertain Collaborative Environments
We consider collaborative systems where users make contributions across
multiple available projects and are rewarded for their contributions in
individual projects according to a local sharing of the value produced. This
serves as a model of online social computing systems such as online Q&A forums
and of credit sharing in scientific co-authorship settings. We show that the
maximum feasible produced value can be well approximated by simple local
sharing rules where users are approximately rewarded in proportion to their
marginal contributions and that this holds even under incomplete information
about the player's abilities and effort constraints. For natural instances we
show almost 95% optimality at equilibrium. When players incur a cost for their
effort, we identify a threshold phenomenon: the efficiency is a constant
fraction of the optimal when the cost is strictly convex and decreases with the
number of players if the cost is linear
Adaptive Matching for Expert Systems with Uncertain Task Types
A matching in a two-sided market often incurs an externality: a matched
resource may become unavailable to the other side of the market, at least for a
while. This is especially an issue in online platforms involving human experts
as the expert resources are often scarce. The efficient utilization of experts
in these platforms is made challenging by the fact that the information
available about the parties involved is usually limited.
To address this challenge, we develop a model of a task-expert matching
system where a task is matched to an expert using not only the prior
information about the task but also the feedback obtained from the past
matches. In our model the tasks arrive online while the experts are fixed and
constrained by a finite service capacity. For this model, we characterize the
maximum task resolution throughput a platform can achieve. We show that the
natural greedy approaches where each expert is assigned a task most suitable to
her skill is suboptimal, as it does not internalize the above externality. We
develop a throughput optimal backpressure algorithm which does so by accounting
for the `congestion' among different task types. Finally, we validate our model
and confirm our theoretical findings with data-driven simulations via logs of
Math.StackExchange, a StackOverflow forum dedicated to mathematics.Comment: A part of it presented at Allerton Conference 2017, 18 page
Parameter estimation for generalized thurstone choice models
We consider the maximum likelihood parameter estimation problem for a generalized Thurstone choice model, where choices are from comparison sets of two or more items. We provide tight characterizations of the mean square error, as well as necessary and sufficient conditions for correct classification when each item belongs to one of two classes. These results provide insights into how the estimation accuracy depends on the choice of a generalized Thurstone choice model and the structure of comparison sets. We find that for a priori unbiased structures of comparisons, e.g., when comparison sets are drawn independently and uniformly at random, the number of observations needed to achieve a prescribed estimation accuracy depends on the choice of a generalized Thurstone choice model. For a broad set of generalized Thurstone choice models, which includes all popular instances used in practice, the estimation error is shown to be largely insensitive to the cardinality of comparison sets. On the other hand, we found that there exist generalized Thurstone choice models for which the estimation error decreases much faster with the cardinality of comparison sets
Dynamics and Inference for Voter Model Processes
We consider a discrete-time voter model process on a set of nodes, each being
in one of two states, either 0 or 1. In each time step, each node adopts the
state of a randomly sampled neighbor according to sampling probabilities,
referred to as node interaction parameters. We study the maximum likelihood
estimation of the node interaction parameters from observed node states for a
given number of realizations of the voter model process. In contrast to
previous work on parameter estimation of network autoregressive processes,
whose long-run behavior is according to a stationary stochastic process, the
voter model is an absorbing stochastic process that eventually reaches a
consensus state. This requires developing a framework for deriving parameter
estimation error bounds from observations consisting of several realizations of
a voter model process. We present parameter estimation error bounds by
interpreting the observation data as being generated according to an extended
voter process that consists of cycles, each corresponding to a realization of
the voter model process until absorption to a consensus state. In order to
obtain these results, consensus time of a voter model process plays an
important role. We present new bounds for all moments and a bound that holds
with any given probability for consensus time, which may be of independent
interest. In contrast to most existing work, our results yield a consensus time
bound that holds with high probability. We also present a sampling complexity
lower bound for parameter estimation within a prescribed error tolerance for
the class of locally stable estimators
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