10,425 research outputs found
Computer Science and Game Theory: A Brief Survey
There has been a remarkable increase in work at the interface of computer
science and game theory in the past decade. In this article I survey some of
the main themes of work in the area, with a focus on the work in computer
science. Given the length constraints, I make no attempt at being
comprehensive, especially since other surveys are also available, and a
comprehensive survey book will appear shortly.Comment: To appear; Palgrave Dictionary of Economic
Exploration versus Exploitation Using Kriging Surrogate Modelling in Electromagnetic Design
This paper discusses the use of kriging surrogate modelling in multiobjective design optimisation in electromagnetics. The importance of achieving appropriate balance between exploration and exploitation is emphasised when searching for the global optimum. It is argued that this approach will yield a procedure to solve time consuming electromagnetic design problems efficiently and will also assist the decision making process to achieve a robust design of practical devices considering tolerances and uncertainties
On Repetitive Scenario Design
Repetitive Scenario Design (RSD) is a randomized approach to robust design
based on iterating two phases: a standard scenario design phase that uses
scenarios (design samples), followed by randomized feasibility phase that uses
test samples on the scenario solution. We give a full and exact
probabilistic characterization of the number of iterations required by the RSD
approach for returning a solution, as a function of , , and of the
desired levels of probabilistic robustness in the solution. This novel approach
broadens the applicability of the scenario technology, since the user is now
presented with a clear tradeoff between the number of design samples and
the ensuing expected number of repetitions required by the RSD algorithm. The
plain (one-shot) scenario design becomes just one of the possibilities, sitting
at one extreme of the tradeoff curve, in which one insists in finding a
solution in a single repetition: this comes at the cost of possibly high .
Other possibilities along the tradeoff curve use lower values, but possibly
require more than one repetition
A neural network-based framework for financial model calibration
A data-driven approach called CaNN (Calibration Neural Network) is proposed
to calibrate financial asset price models using an Artificial Neural Network
(ANN). Determining optimal values of the model parameters is formulated as
training hidden neurons within a machine learning framework, based on available
financial option prices. The framework consists of two parts: a forward pass in
which we train the weights of the ANN off-line, valuing options under many
different asset model parameter settings; and a backward pass, in which we
evaluate the trained ANN-solver on-line, aiming to find the weights of the
neurons in the input layer. The rapid on-line learning of implied volatility by
ANNs, in combination with the use of an adapted parallel global optimization
method, tackles the computation bottleneck and provides a fast and reliable
technique for calibrating model parameters while avoiding, as much as possible,
getting stuck in local minima. Numerical experiments confirm that this
machine-learning framework can be employed to calibrate parameters of
high-dimensional stochastic volatility models efficiently and accurately.Comment: 34 pages, 9 figures, 11 table
Preliminary space mission design under uncertainty
This paper proposes a way to model uncertainties and to introduce them explicitly in the design process of a preliminary space mission. Traditionally, a system margin approach is used in order to take the min to account. In this paper, Evidence Theory is proposed to crystallise the inherent uncertainties. The design process is then formulated as an optimisation under uncertainties(OUU). Three techniques are proposed to solve the OUU problem: (a) an evolutionary multi-objective approach, (b) a step technique consisting of maximising the belief for different levels of performance, and (c) a clustering method that firstly identifies feasible regions.The three methods are applied to the Bepi Colombo mission and their effectiveness at solving the OUU problem are compared
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