1,738,821 research outputs found
Policy spillovers in a regional target-setting regime
The present UK government has introduced a decentralised, target-driven framework for the delivery of regional policy in England. This paper analyses the operation of such a regime when there are spatial spillovers about which the government is uninformed. It stresses the simple idea that spillovers in such a setting normally lead to a sub-optimal allocation of policy expenditures. A key result is that the existence of negative spillovers on some policies generates expenditure switching towards those policies. The extent of the expenditure switching is related to a number of factors: the size of the spillovers; the initial policy weights in the government's welfare function; the number of agencies; the extent of their knowledge of spillovers; and their degree of collusion. Such expenditure switching is generally not welfare maximising
Model evaluation of target product profiles of an infant vaccine against respiratory syncytial virus (RSV) in a developed country setting
Respiratory syncytial virus (RSV) is a major cause of lower respiratory tract disease in children worldwide and is a significant cause of hospital admissions in young children in England. No RSV vaccine has been licensed but a number are under development. In this work, we present two structurally distinct mathematical models, parameterized using RSV data from the UK, which have been used to explore the effect of introducing an RSV paediatric vaccine to the National programme. We have explored different vaccine properties, and dosing regimens combined with a range of implementation strategies for RSV control. The results suggest that vaccine properties that confer indirect protection have the greatest effect in reducing the burden of disease in children under 5 years. The findings are reinforced by the concurrence of predictions from the two models with very different epidemiological structure. The approach described has general application in evaluating vaccine target product profiles
Approachability in unknown games: Online learning meets multi-objective optimization
In the standard setting of approachability there are two players and a target
set. The players play repeatedly a known vector-valued game where the first
player wants to have the average vector-valued payoff converge to the target
set which the other player tries to exclude it from this set. We revisit this
setting in the spirit of online learning and do not assume that the first
player knows the game structure: she receives an arbitrary vector-valued reward
vector at every round. She wishes to approach the smallest ("best") possible
set given the observed average payoffs in hindsight. This extension of the
standard setting has implications even when the original target set is not
approachable and when it is not obvious which expansion of it should be
approached instead. We show that it is impossible, in general, to approach the
best target set in hindsight and propose achievable though ambitious
alternative goals. We further propose a concrete strategy to approach these
goals. Our method does not require projection onto a target set and amounts to
switching between scalar regret minimization algorithms that are performed in
episodes. Applications to global cost minimization and to approachability under
sample path constraints are considered
Safety Performance measures Target Setting
In 2016 the FHWA issued new rules that govern performance management for a number of transportation areas. In this session we explore the new rules that apply to the area of traffic safety, in particular the requirements that both state DOTs and metropolitan planning organizations must set performance goals and begin reporting their annual progress toward meeting those targets
Prediction of Search Targets From Fixations in Open-World Settings
Previous work on predicting the target of visual search from human fixations
only considered closed-world settings in which training labels are available
and predictions are performed for a known set of potential targets. In this
work we go beyond the state of the art by studying search target prediction in
an open-world setting in which we no longer assume that we have fixation data
to train for the search targets. We present a dataset containing fixation data
of 18 users searching for natural images from three image categories within
synthesised image collages of about 80 images. In a closed-world baseline
experiment we show that we can predict the correct target image out of a
candidate set of five images. We then present a new problem formulation for
search target prediction in the open-world setting that is based on learning
compatibilities between fixations and potential targets
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