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Annuities are financial products that guarantee the holder a fixed return so long as the holder remains alive, thereby providing insurance against lifetime uncertainty. The terms of these contracts depend on the information available to insurance firms. Unlike age and gender, information about individual survival probabilities cannot be readily ascertained. This asymmetric information causes market inefficiencies, such as adverse selection. Groundbreaking in its scope, The Economic Theory of Annuities offers readers a theoretical analysis of the functioning of private annuity markets. Starting with a general analysis of survival functions, stochastic dominance, and characterization of changes in longevity, Eytan Sheshinski derives the demand for annuities using a model of individuals who jointly choose their lifetime consumption and retirement age. The relation between life insurance and annuities that have a bequest option is examined and "annuity options" are proposed as a response to the lack of secondary markets. This book also investigates the macroeconomic policy implications of annuities and changes in longevity on aggregate savings. Sheshinski utilizes statistical population theory to shed light on the debate of whether the surge in savings and growth in Asia and other countries can be attributed to higher longevity of the population and whether this surge is durable. This book shows how understanding annuities becomes essential as governments that grapple with insolvency of public social security systems place greater emphasis on individual savings accounts.

Annuites. --- assurance vie --- Annuities. --- mathematiques actuarielles. --- Finance Act. --- Lagrange multiplier. --- Pareto optimality. --- Ulpianus, Domitius. --- World Bank. --- adverse selection. --- after-tax retirement annuities (ATRA). --- age composition effect. --- aggregate resource constraint. --- annuity puzzle. --- asymmetric information. --- behavioral models. --- bequests. --- bounded rationality. --- bundling. --- certainty equivalence. --- consumption: after retirement. --- cost effects. --- cross-subsidization. --- demand elasticity. --- disability benefits. --- envelope theorem. --- equilibrium. --- expected lifetime utility. --- fertility rate. --- full annuitization. --- game theory. --- general equilibrium effect. --- government. --- hazard rate. --- hyperbolic discounters. --- information. --- insurance. --- investment. --- labor utility. --- life expectancy. --- liquidity. --- longevity. --- markets. --- national defined contribution systems. --- normal retirement age (NRA). --- optimum commodity taxation. --- pension funds. --- pooling equilibrium. --- refundable annuities. --- reverse life insurance. --- risk class. --- self selection. --- sequential equilibrium. --- taxes. --- unintended bequests. --- wages.

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Numerical methods are a specific form of mathematics that involve creating and use of algorithms to map out the mathematical core of a practical problem. Numerical methods naturally find application in all fields of engineering, physical sciences, life sciences, social sciences, medicine, business, and even arts. The common uses of numerical methods include approximation, simulation, and estimation, and there is almost no scientific field in which numerical methods do not find a use. Results communicated here include topics ranging from statistics (Detecting Extreme Values with Order Statistics in Samples from Continuous Distributions) and Statistical software packages (dCATCH—A Numerical Package for d-Variate near G-Optimal Tchakaloff Regression via Fast NNLS) to new approaches for numerical solutions (Exact Solutions to the Maxmin Problem max‖Ax‖ Subject to ‖Bx‖≤1; On q-Quasi-Newton’s Method for Unconstrained Multiobjective Optimization Problems; Convergence Analysis and Complex Geometry of an Efficient Derivative-Free Iterative Method; On Derivative Free Multiple-Root Finders with Optimal Fourth Order Convergence; Finite Integration Method with Shifted Chebyshev Polynomials for Solving Time-Fractional Burgers’ Equations) to the use of wavelets (Orhonormal Wavelet Bases on The 3D Ball Via Volume Preserving Map from the Regular Octahedron) and methods for visualization (A Simple Method for Network Visualization).

Research & information: general --- Mathematics & science --- Clenshaw–Curtis–Filon --- high oscillation --- singular integral equations --- boundary singularities --- local convergence --- nonlinear equations --- Banach space --- Fréchet-derivative --- finite integration method --- shifted Chebyshev polynomial --- Caputo fractional derivative --- Burgers’ equation --- coupled Burgers’ equation --- maxmin --- supporting vector --- matrix norm --- TMS coil --- optimal geolocation --- probability computing --- Monte Carlo simulation --- order statistics --- extreme values --- outliers --- multiobjective programming --- methods of quasi-Newton type --- Pareto optimality --- q-calculus --- rate of convergence --- wavelets on 3D ball --- uniform 3D grid --- volume preserving map --- Network --- graph drawing --- planar visualizations --- multiple root solvers --- composite method --- weight-function --- derivative-free method --- optimal convergence --- multivariate polynomial regression designs --- G-optimality --- D-optimality --- multiplicative algorithms --- G-efficiency --- Caratheodory-Tchakaloff discrete measure compression --- Non-Negative Least Squares --- accelerated Lawson-Hanson solver

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Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms.

Technology: general issues --- global optimization --- cuckoo search algorithm --- Q-learning --- mutation --- self-adaptive step size --- evolutionary computation --- playtesting --- game feature --- game simulation --- game trees --- playtesting metric --- validation --- Pareto optimality --- h-index --- ranking --- dominance --- Pareto-front --- multi-indicators --- multi-metric --- multi-resources --- citation --- universities ranking --- swarm intelligence --- simulated annealing --- krill herd --- particle swarm optimization --- quantum --- elephant herding optimization --- engineering optimization --- metaheuristic --- constrained optimization --- multi-objective optimization --- single objective optimization --- differential evolution --- success-history --- premature convergence --- turning-based mutation --- opposition-based learning --- ant colony optimization --- opposite path --- traveling salesman problems --- whale optimization algorithm --- WOA --- binary whale optimization algorithm --- bWOA-S --- bWOA-V --- feature selection --- classification --- dimensionality reduction --- menu planning problem --- evolutionary algorithm --- decomposition-based multi-objective optimisation --- memetic algorithm --- iterated local search --- diversity preservation --- single-objective optimization --- knapsack problem --- travelling salesman problem --- seed schedule --- many-objective optimization --- fuzzing --- bug detection --- path discovery --- evolutionary algorithms (EAs) --- coevolution --- dynamic learning --- performance indicators --- magnetotelluric --- one-dimensional inversions --- geoelectric model --- optimization problem --- multi-task optimization --- multi-task evolutionary computation --- knowledge transfer --- assortative mating --- unified search space --- quantum computing --- grey wolf optimizer --- 0-1 knapsack problem --- green shop scheduling --- fuzzy hybrid flow shop scheduling --- discrete artificial bee colony algorithm --- minimize makespan --- minimize total energy consumption

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Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms.

global optimization --- cuckoo search algorithm --- Q-learning --- mutation --- self-adaptive step size --- evolutionary computation --- playtesting --- game feature --- game simulation --- game trees --- playtesting metric --- validation --- Pareto optimality --- h-index --- ranking --- dominance --- Pareto-front --- multi-indicators --- multi-metric --- multi-resources --- citation --- universities ranking --- swarm intelligence --- simulated annealing --- krill herd --- particle swarm optimization --- quantum --- elephant herding optimization --- engineering optimization --- metaheuristic --- constrained optimization --- multi-objective optimization --- single objective optimization --- differential evolution --- success-history --- premature convergence --- turning-based mutation --- opposition-based learning --- ant colony optimization --- opposite path --- traveling salesman problems --- whale optimization algorithm --- WOA --- binary whale optimization algorithm --- bWOA-S --- bWOA-V --- feature selection --- classification --- dimensionality reduction --- menu planning problem --- evolutionary algorithm --- decomposition-based multi-objective optimisation --- memetic algorithm --- iterated local search --- diversity preservation --- single-objective optimization --- knapsack problem --- travelling salesman problem --- seed schedule --- many-objective optimization --- fuzzing --- bug detection --- path discovery --- evolutionary algorithms (EAs) --- coevolution --- dynamic learning --- performance indicators --- magnetotelluric --- one-dimensional inversions --- geoelectric model --- optimization problem --- multi-task optimization --- multi-task evolutionary computation --- knowledge transfer --- assortative mating --- unified search space --- quantum computing --- grey wolf optimizer --- 0-1 knapsack problem --- green shop scheduling --- fuzzy hybrid flow shop scheduling --- discrete artificial bee colony algorithm --- minimize makespan --- minimize total energy consumption

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In this book, 20 papers focused on different fields of power electronics are gathered. Approximately half of the papers are focused on different control issues and techniques, ranging from the computer-aided design of digital compensators to more specific approaches such as fuzzy or sliding control techniques. The rest of the papers are focused on the design of novel topologies. The fields in which these controls and topologies are applied are varied: MMCs, photovoltaic systems, supercapacitors and traction systems, LEDs, wireless power transfer, etc.

Technology: general issues --- vehicle-grid coupling system --- low frequency oscillation --- traction line-side converter (LSC) --- model-based predictive current control (MBPCC) --- dSPACE semi-physical verification --- switching converters --- sliding-mode control --- current-mode control --- hysteresis control --- PV-connected inverter --- MPPT --- SPPT --- adaptive hysteresis current control --- hybrid storage systems --- power electronic converters --- half-bridge current-source converters --- supercapacitors --- cascaded H-bridge (CHB) --- dc-link voltage balance control --- multilevel converter --- power control --- single-phase system --- pulsating output current --- light emitting diode (LED) --- peak to average ratio (PTAR) --- power factor correction --- harmonic injection --- modelling --- feedback loop control --- three-port converter --- linear active disturbance rejection control --- virtual damping --- linear extended state observer --- power converters --- digital control --- design space --- frequency domain --- switched affine systems --- hybrid systems --- fuzzy identification --- fuzzy modeling --- two degrees of freedom --- fuzzy model predictive control --- PLC --- bus converter --- DC bus --- LED driver --- buck converter --- inversion formulae --- phase margin --- gain crossover frequency --- wireless power transfer --- inductive power transfer --- Pareto optimality --- coil design --- magnetics design --- GaN-based inverter and converter --- zeta inverter --- active clamp --- synchronous rectification --- power efficiency --- circulating current --- fuzzy --- proportional integral --- proportional resonant --- MMC --- DC–DC converter --- experimental verification --- Inductor–Diode --- Inductor–Capacitor–Diode --- nonisolated --- step-down --- two-stage buck converter --- voltage regulation --- power electronic converter --- AC/AC converter --- matrix converter --- reliability --- DPWM --- photovoltaic power system --- differential flatness --- nonlinear control --- networked power converters --- PFC converters --- reactive power resources --- supervisory controller --- HIL Testbed --- binary particle swarm optimization (BPSO) --- nonsingular terminal sliding mode control (NTSMC) --- global best solution --- total harmonic distortion (THD) --- DC–AC converter --- decoupling --- reduced order generalized integrator (ROGI) --- optimal gain --- distributed power generation system (DPGS) --- grid-connected voltage source converters (GC-VSCs)