Calibrating with Multiple Criteria: A Demonstration of Dominance

Calibrating with Multiple Criteria: A Demonstration of Dominance

Pattern oriented modelling (POM) is an approach to calibration or validation that assesses a model using multiple weak patterns. We extend the concept of POM, using dominance to objectively identify the best parameter candidates. The TELL ME agent-basedmodel is used to demonstrate the approach. This model simulates personal decisions to adopt protective behaviour during an influenza epidemic. T...

Dominance Solvable Games with Multiple Payoff Criteria

Two logically distinct and permissive extensions of iterative weak dominance are introduced for games with possibly vector-valued payoffs. The first, iterative partial dominance, builds on an easy-tocheck condition but may lead to solutions that do not include any (generalized) Nash equilibria. However, the second and intuitively more demanding extension, iterative essential dominance, is shown...

Decision criteria in risk analysis : an application of stochastic dominance with respect to a function

Multiple Criteria Decision Making Combined with VRP: A Categorized Bibliographic Study

In this research author reviews references related to the topic of multi criterion (goal programming, multiple objective linear and nonlinear programming, bi-criterion programming, Multi Attribute Decision Making, Compromise Programming, Surrogate Worth Trade-off Method) and various versions of vehicle routing problem (VRP), Multi depot VRP (MDVRP), VRP with time windows (VRPWTW), Stochastic VR...

Comparisons of heterogeneous distributions and dominance criteria

We are interested in the comparisons of standard-of-living across societies when observations of both income and household structure are available. We generalise the approach of Atkinson and Bourguignon (1987) to the case where the marginal distributions of needs can vary across the household populations under comparison. We assume that a sympathetic observer uses a utilitarian social welfare f...