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Added DICE Experiments from Climate School.
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‎src/main/java/net/finmath/experiments/ui/DICEAbatementTimeExperimentUI.java‎

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package net.finmath.experiments.ui;
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import java.text.DecimalFormat;
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import java.util.List;
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import java.util.Map;
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import java.util.function.BooleanSupplier;
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import java.util.function.DoubleConsumer;
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import java.util.function.UnaryOperator;
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import java.util.stream.Collectors;
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import net.finmath.climate.models.ClimateModel;
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import net.finmath.climate.models.dice.DICEModel;
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import net.finmath.climateschool.utilities.DICEModelPlots;
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import net.finmath.experiments.ui.parameter.BooleanParameter;
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import net.finmath.experiments.ui.parameter.DoubleParameter;
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import net.finmath.time.TimeDiscretization;
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import net.finmath.time.TimeDiscretizationFromArray;
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public class DICEAbatementTimeExperimentUI extends ExperimentUI {
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private static final double timeStep = 1.0;
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private static final double timeHorizon = 500.0;
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private final DecimalFormat numberDigit3 = new DecimalFormat("#.000");
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private final DecimalFormat numberPercent2 = new DecimalFormat("#.00%");
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private final DICEModelPlots plots = new DICEModelPlots();
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public DICEAbatementTimeExperimentUI() {
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super(List.of(
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new DoubleParameter("Discount Rate", 0.03, 0.01, 0.05),
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new DoubleParameter("Abatement Max Time", 50.0, 10.0, 200.0),
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new BooleanParameter("Show Cost", false)
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));
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}
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public String getTitle() { return "DICE Model - One Parametric Abatement Model - NOT CALIBRATED"; }
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public void runCalculation(BooleanSupplier isCancelled, DoubleConsumer progress) {
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Map<String, Object> currentParameterSet = getExperimentParameters().stream().collect(Collectors.toMap(p -> p.getBindableValue().getName(), p -> p.getBindableValue().getValue()));
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System.out.println("Calculation with Parameters: " + currentParameterSet);
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/*
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* Discount rate
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*/
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final double discountRate = (Double)currentParameterSet.get("Discount Rate");
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/*
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* Parameters for the abatement model (abatement = fraction of industrial CO2 reduction; 1.00 ~ 100 % reduction ~ carbon neutral).
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*/
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final double abatementInitial = 0.03;
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final double abatementMax = 1.00;
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final double abatementMaxTime = (Double)currentParameterSet.get("Abatement Max Time");
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/*
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* Create a time discretization
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*/
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final int numberOfTimeSteps = (int)Math.round(timeHorizon / timeStep);
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final TimeDiscretization timeDiscretization = new TimeDiscretizationFromArray(0.0, numberOfTimeSteps, timeStep);
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/*
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* Create our abatement model: it is a piecewise linear funtion: starting in abatementInitial, then reaching abatementMax in abatementMaxTime years, then staying at abatementMax.
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*/
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final UnaryOperator<Double> abatementFunction = time -> Math.min(abatementInitial + (abatementMax-abatementInitial)/abatementMaxTime * time, abatementMax);
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/*
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* Create our savings rate model: a constant.
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*/
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final UnaryOperator<Double> savingsRateFunction = time -> 0.26;
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/*
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* Create the DICE model from the given parameters.
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*/
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final ClimateModel climateModel = new DICEModel(timeDiscretization, abatementFunction, savingsRateFunction, discountRate);
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/*
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* Plots
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*/
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if(!Thread.currentThread().isInterrupted() && !isCancelled.getAsBoolean()) {
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synchronized(this) {
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String spec = "T(\u03BC=1) =" + numberDigit3.format(abatementMaxTime) + ", r = " + numberPercent2.format(discountRate);
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plots.plot(climateModel, spec);
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boolean showCost = (boolean)currentParameterSet.get("Show Cost");
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if(showCost) {
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plots.plotCost(climateModel, discountRate, spec);
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}
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else {
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plots.closeCost();
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}
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}
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}
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}
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@Override
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protected void onClose() {
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synchronized(this) {
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super.onClose();
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if(plots != null) plots.close();
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}
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}
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}

‎src/main/java/net/finmath/experiments/ui/DICECalibrationExperimentUI.java‎

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package net.finmath.experiments.ui;
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import java.text.DecimalFormat;
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import java.util.Arrays;
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import java.util.List;
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import java.util.Map;
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import java.util.function.BooleanSupplier;
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import java.util.function.DoubleConsumer;
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import java.util.function.UnaryOperator;
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import java.util.stream.Collectors;
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import net.finmath.climate.models.ClimateModel;
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import net.finmath.climate.models.dice.DICEModel;
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import net.finmath.climateschool.utilities.AdamOptimizerUsingFiniteDifferences;
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import net.finmath.climateschool.utilities.AdamOptimizerUsingFiniteDifferences.GradientMethod;
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import net.finmath.climateschool.utilities.DICEModelPlots;
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import net.finmath.experiments.ui.parameter.BooleanParameter;
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import net.finmath.experiments.ui.parameter.DoubleParameter;
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import net.finmath.stochastic.RandomVariable;
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import net.finmath.stochastic.Scalar;
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import net.finmath.time.TimeDiscretization;
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import net.finmath.time.TimeDiscretizationFromArray;
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public class DICECalibrationExperimentUI extends ExperimentUI {
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private static final double timeStep = 1.0;
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private static final double timeHorizon = 500.0;
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private final DecimalFormat numberDigit3 = new DecimalFormat("#.000");
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private final DecimalFormat numberPercent2 = new DecimalFormat("#.00%");
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double[] initialParameters;
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private final DICEModelPlots plots = new DICEModelPlots();
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public DICECalibrationExperimentUI() {
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super(List.of(
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new DoubleParameter("Discount Rate", 0.03, 0.01, 0.05),
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new BooleanParameter("Show Cost", false)
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// new Parameter("Abatement Max Time", 50.0, 10.0, 200.0)
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));
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}
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public String getTitle() { return "DICE Model - Full Abatement Model - Optimized Emisison Path (Calibration)"; }
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public void runCalculation(BooleanSupplier isCancelled, DoubleConsumer progress) {
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Map<String, Object> currentParameterSet = getExperimentParameters().stream().collect(Collectors.toMap(p -> p.getBindableValue().getName(), p -> p.getBindableValue().getValue()));
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System.out.println("Calculation with Parameters: " + currentParameterSet);
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/*
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* Create a time discretization
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*/
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final int numberOfTimeSteps = (int)Math.round(timeHorizon / timeStep);
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final TimeDiscretization timeDiscretization = new TimeDiscretizationFromArray(0.0, numberOfTimeSteps, timeStep);
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/*
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* Create our savings rate model: a constant.
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*/
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final UnaryOperator<Double> savingsRateFunction = time -> 0.26;
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/*
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* Discount rate
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*/
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final double discountRate = (Double)currentParameterSet.get("Discount Rate");
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// Initial parameters for our abatement function
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if(initialParameters == null) {
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initialParameters = new double[timeDiscretization.getNumberOfTimes()];
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Arrays.fill(initialParameters, -Math.log(-Math.log(0.8)));
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}
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final AdamOptimizerUsingFiniteDifferences optimizer = new AdamOptimizerUsingFiniteDifferences(initialParameters, 800, 0.05, GradientMethod.AVERAGE) {
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private int iteration = 0;
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@Override
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public RandomVariable setValue(RandomVariable[] parameters) {
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if(Thread.currentThread().isInterrupted()) {
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this.stop();
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}
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double[] abatementParameter = Arrays.stream(parameters).mapToDouble(RandomVariable::getAverage).map(x -> Math.exp(-Math.exp(-x))).toArray();
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abatementParameter[0] = 0.03;
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initialParameters = Arrays.stream(parameters).mapToDouble(RandomVariable::getAverage).toArray();
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/*
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* Create our abatement model
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*/
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final UnaryOperator<Double> abatementFunction = time -> abatementParameter[(int)Math.round(time/timeStep)];
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/*
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* Create the DICE model
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*/
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final ClimateModel climateModel = new DICEModel(timeDiscretization, abatementFunction, savingsRateFunction, discountRate);
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final double value = climateModel.getValue().expectation().doubleValue();
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// Penalty for non-smoothness - it works without this, but this helps the optimizer to avoid onszillations (that are la
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double roughness = 0.0;
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for(int i=1; i<abatementParameter.length-2; i++) {
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roughness += Math.pow(abatementParameter[i+1] - abatementParameter[i], 2.0);
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}
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roughness = Math.sqrt(roughness);
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roughness /= abatementParameter.length;
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roughness *= 0.1;
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// Update the plot every 200 iterations
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if(iteration%200 == 0 && !Thread.currentThread().isInterrupted()) {
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String spec = "r = " + numberPercent2.format(discountRate) + "; value = " + numberDigit3.format(value);
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plots.plot(climateModel, spec);
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boolean showCost = (boolean)currentParameterSet.get("Show Cost");
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if(showCost) {
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plots.plotCost(climateModel, discountRate, spec);
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}
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else {
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plots.closeCost();
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}
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}
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iteration++;
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return Scalar.of(-value);// + roughness);
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}
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};
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optimizer.run();
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System.out.println("Optimizer finished.");
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// Get optimal value
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final RandomVariable[] bestParameters = optimizer.getBestFitParameters();
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double[] abatementParameter = Arrays.stream(bestParameters).mapToDouble(RandomVariable::getAverage).map(x -> Math.exp(-Math.exp(-x))).toArray();
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abatementParameter[0] = 0.03;
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System.out.println(Arrays.toString(abatementParameter));
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/*
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* Create our abatement model
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*/
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final UnaryOperator<Double> abatementFunction = time -> abatementParameter[(int)Math.round(time/timeStep)];
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/*
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* Create the DICE model
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*/
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final ClimateModel climateModel = new DICEModel(timeDiscretization, abatementFunction, savingsRateFunction, discountRate);
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/*
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* Plots
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*/
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if(!Thread.currentThread().isInterrupted() && !isCancelled.getAsBoolean()) {
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synchronized(this) {
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String spec = "r = " + numberPercent2.format(discountRate);
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plots.plot(climateModel, spec);
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boolean showCost = (boolean)currentParameterSet.get("Show Cost");
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if(showCost) {
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plots.plotCost(climateModel, discountRate, spec);
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}
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else {
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plots.closeCost();
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}
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}
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}
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}
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@Override
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protected void onClose() {
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synchronized(this) {
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super.onClose();
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if(plots != null) plots.close();
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}
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}
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}

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