Conformal Methods for Quantifying Uncertainty in Spatiotemporal Data: A Survey

Machine learning methods are increasingly widely used in high-risk settings such as healthcare, transportation, and finance. In these settings, it is important that a model produces calibrated uncertainty to reflect its own confidence and avoid failures. In this paper we survey recent works on uncertainty quantification (UQ) for deep learning, in particular distribution-free Conformal Prediction method for its mathematical properties and wide applicability. We will cover the theoretical guarantees of conformal methods, introduce techniques that improve calibration and efficiency for UQ in the context of spatiotemporal data, and discuss the role of UQ in the context of safe decision making

The Italian Electricity Prices in Year 2025: an Agent-Based Simulation

In this paper, we build a realistic large-scale agent-based model of the Italian dayahead-electricity market based on a genetic algorithm and validated over several weeks of 2010, on the basis of exact historical data about supply, demand and network characteristics. A statistical analysis confirms that the simulator well replicates the observed prices. A future scenario for the year 2025 is then simulated, which takes into account market’s evolution and energy vectors’ price dynamics. The future electricity prices are contrasted with the ones that might arise considering also the possible (yet unlikely) construction of new nuclear power (NP) plants. It is shown that future prices will be higher than the actual ones. NP production can reduce the prices and their volatility, but the size of the impact depends on the pattern of the expected demand load, and can be negligible.Electricity market, PUN, Agent-based computational economics, Nuclear power.

What do majority-voting politics say about redistributive taxation of consumption and factor income? Not much.

Tax rates on labor income, capital income and consumption-and the redistributive transfers those taxes finance-differ widely across developed countries. Can majority-voting methods, applied to a calibrated growth model, explain that variation? The answer I fund is yes, and then some. In this paper, I examine a simple growth model, calibrated roughly to U.S. data, in which the political decision is over constant paths of taxes on factor income and consumption, used to finance a lump-sum transfer. I first look at outcomes under probabilistic voting, and find that equilibria are extremely sensitive to the specification of uncertainty. I then consider other ways to restrict the range of majority-rule outcomes, looking at the model's implications for the shape of the Pareto set and the uncovered set, and the existence or non-existence of a Condorcet winner. Solving the model on discrete grid of policy choices, I find that no Condorcet winner exists and that the Pareto and uncovered sets, while small relative to the entire issue space, are large relative to the range of tax policies we see in data for a collection of 20 OECD countries. Taking that data as the issue space, I find that none of the 20 can be ruled out on efficiency grounds, and that 10 of the 20 are in the uncovered set. Those 10 encompass policies as diverse as those of the US, Norway and Austria. One can construct a Condorcet cycle including all 10 countries' tax vectors. ; The key features of the model here, as compared to other models on the endogenous determination of taxes and redistribution, is that the issue space is multidimensional and, at the same time, no one voter type is sufficiently numerous to be decisive. I conclude that the sharp predictions of papers in this literature may not survive an expansion of their issue spaces or the allowance for a slightly less homogeneous electorate.Taxation ; Consumption (Economics) ; Income tax ; Fiscal policy