2,897 research outputs found
Decision Taking for Selling Thread Startup
Decision Taking is discussed in the context of the role it may play for a
selling agent in a search market, in particular for agents involved in the sale
of valuable and relatively unique items, such as a dwelling, a second hand car,
or a second hand recreational vessel.
Detailed connections are made between the architecture of decision making
processes and a sample of software technology based concepts including
instruction sequences, multi-threading, and thread algebra.
Ample attention is paid to the initialization or startup of a thread
dedicated to achieving a given objective, and to corresponding decision taking.
As an application, the selling of an item is taken as an objective to be
achieved by running a thread that was designed for that purpose
Dynamic Ad Allocation: Bandits with Budgets
We consider an application of multi-armed bandits to internet advertising
(specifically, to dynamic ad allocation in the pay-per-click model, with
uncertainty on the click probabilities). We focus on an important practical
issue that advertisers are constrained in how much money they can spend on
their ad campaigns. This issue has not been considered in the prior work on
bandit-based approaches for ad allocation, to the best of our knowledge.
We define a simple, stylized model where an algorithm picks one ad to display
in each round, and each ad has a \emph{budget}: the maximal amount of money
that can be spent on this ad. This model admits a natural variant of UCB1, a
well-known algorithm for multi-armed bandits with stochastic rewards. We derive
strong provable guarantees for this algorithm
Optimal Data Acquisition for Statistical Estimation
We consider a data analyst's problem of purchasing data from strategic agents
to compute an unbiased estimate of a statistic of interest. Agents incur
private costs to reveal their data and the costs can be arbitrarily correlated
with their data. Once revealed, data are verifiable. This paper focuses on
linear unbiased estimators. We design an individually rational and incentive
compatible mechanism that optimizes the worst-case mean-squared error of the
estimation, where the worst-case is over the unknown correlation between costs
and data, subject to a budget constraint in expectation. We characterize the
form of the optimal mechanism in closed-form. We further extend our results to
acquiring data for estimating a parameter in regression analysis, where private
costs can correlate with the values of the dependent variable but not with the
values of the independent variables
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Michigan Earn and Learn: An Outcome & Implementation Evaluation of a Transitional Job and Training Program
While the Great Recession introduced unemployment and underemployment to the masses, its significant negative trends aggravated already declining rates of employment in Michigan, particularly among less-educated, young, male, and minority individuals, who were then also hit hardest by the recession. As the nation began to slowly recover after the recession, Michigan continued struggling to find an economic foothold.The State of Michigan, along with private funders, responded with the Michigan Earn and Learn program, with the goal of creating opportunities for people facing barriers to employment to pursue education and occupational training that could help them get ahead. This evaluation report of the Michigan Earn and Learn transitional jobs program was commissioned by The Joyce Foundation on behalf of the State of Michigan
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