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Impact of weather factors on migration intention using machine learning algorithms
A growing attention in the empirical literature has been paid to the incidence of climate shocks and change in migration decisions.
Using an interpretable Machine Learning approach to study the drivers of International Migration
a traditional model, and 2) using PDP we are able to gain additional insights on the specific effects of the migration drivers.
Stochastic Constraint Optimization using Propagation on Ordered Binary Decision Diagrams
For the specific case of monotonic distributions, we suggest an alternative method for using CP in SCOP, based on the development of a new propagator; we show that this propagator is linear in the size of the OBDD, and has the potential to be more efficient than the decomposition method, as it maintains domain consistency.
The Inductive Constraint Programming Loop
Constraint programming is used for a variety of real-world optimisation problems, such as planning, scheduling and resource allocation problems.
