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Migration forecasts are crucial for proactive immigration and integration management. While the demand for accurate migration forecasts continues to grow, the current state of migration forecasting is still unsatisfactory, because they tend to lack precision. We introduce an alternative method to forecast migration movements: prediction markets. While prediction markets are mainly unknown in migration studies, they are established in the political economy of forecasting election outcomes. For its application to a complex phenomenon in a more constrained information environment such as migration movements, we argue that prediction markets allow to balance complementarities of current qualitative and quantitative approaches if they provide solutions to avoid thin trading and integrate expert knowledge into the market. We apply the prediction market to forecast immigration in four West European countries in 2020 and find encouraging results. We discuss the strengths and limitations of prediction markets to migration forecasting, including ethical considerations, and guide its future application. [ABSTRACT FROM AUTHOR]

Abstract Migration forecasts are crucial for proactive immigration and integration management. While the demand for accurate migration forecasts continues to grow, the current state of migration forecasting is still unsatisfactory, because they tend to lack precision. We introduce an alternative method to forecast migration movements: prediction markets. While prediction markets are mainly unknown in migration studies, they are established in the political economy of forecasting election outcomes. For its application to a complex phenomenon in a more constrained information environment such as migration movements, we argue that prediction markets allow to balance complementarities of current qualitative and quantitative approaches if they provide solutions to avoid thin trading and integrate expert knowledge into the market. We apply the prediction market to forecast immigration in four West European countries in 2020 and find encouraging results. We discuss the strengths and limitations of prediction markets to migration forecasting, including ethical considerations, and guide its future application.

We use simplified methods to investigate how uncertainty in geological models affects practical CO 2 storage capacities in large-scale saline aquifers. Our focus is on uncertainties in top-surface elevation, rock properties (porosity, permeability), fault transmissibility, and aquifer conditions (pressure and temperature). To quantify the statistical characteristics of static trapping capacity and dynamic estimates of plume migration, we create hundreds of possible realizations of the geomodel by applying Gaussian-type perturbations to the spatially correlated properties. Two different simplified methods are introduced to reduce the computational cost of simulating migration over thousands of years in all the model realizations, which each spans hundreds of kilometers. First, we use vertical-equilibrium (VE) modelling, which is orders of magnitude faster than solving the 3D flow equations. Second, we introduce a fast look-ahead algorithm that enables us to exit the VE simulation once a pseudo-steady state is reached. This algorithm uses a spill-point analysis of the top-surface's trapping structure to forecast how much CO 2 will eventually become trapped and how much will leak through open boundaries of the formation. This reduces the computational cost significantly, since we seldom need to simulate long-term migration past a few hundred or thousand years. [ABSTRACT FROM AUTHOR]