Adaptive Pareto Set Estimation for Stochastic Mixed Variable Design Problems

Many design problems require the optimization of competing objective functions that may be too complicated to solve analytically. These problems are often modeled in a simulation environment where static input may result in dynamic (stochastic) responses to the various objective functions. System reliability, alloy composition, algorithm parameter selection, and structural design optimization are classes of problems that often exhibit such complex and stochastic properties. Since the physical testing and experimentation of new designs can be prohibitively expensive, engineers need adequate predictions concerning the viability of various designs in order to minimize wasteful testing. Presumably, an appropriate stochastic multi-objective optimizer can be used to eliminate inefficient designs through the analysis of simulated responses. This research develops an adaptation of Walston’s [56] Stochastic Multi-Objective Mesh Adaptive Direct Search (SMOMADS) and Paciencia’s NMADS [45] based on Kim and de Weck’s [34] Adaptive Weighted Sum (AWS) procedure and standard distance to a reference point methods. This new technique is compared to standard heuristic based methods used to evaluate several real-world design problems. The main contribution of this paper is a new implementation of MADS for Mixed Variable and Stochastic design problems that drastically reduces dependence on subjective decision maker interaction

A Model for Prejudiced Learning in Noisy Environments

Based on the heuristics that maintaining presumptions can be beneficial in uncertain environments, we propose a set of basic axioms for learning systems to incorporate the concept of prejudice. The simplest, memoryless model of a deterministic learning rule obeying the axioms is constructed, and shown to be equivalent to the logistic map. The system's performance is analysed in an environment in which it is subject to external randomness, weighing learning defectiveness against stability gained. The corresponding random dynamical system with inhomogeneous, additive noise is studied, and shown to exhibit the phenomena of noise induced stability and stochastic bifurcations. The overall results allow for the interpretation that prejudice in uncertain environments entails a considerable portion of stubbornness as a secondary phenomenon.Comment: 21 pages, 11 figures; reduced graphics to slash size, full version on Author's homepage. Minor revisions in text and references, identical to version to be published in Applied Mathematics and Computatio

Reconciling model and information uncertainty in development appraisal

This paper investigates the effect of choices of model structure and scale in development viability appraisal. The paper addresses two questions concerning the application of development appraisal techniques to viability modelling within the UK planning system. The first relates to the extent to which, given intrinsic input uncertainty, the choice of model structure significantly affects model outputs. The second concerns the extent to which, given intrinsic input uncertainty, the level of model complexity significantly affects model outputs. Monte Carlo simulation procedures are applied to a hypothetical development scheme in order to measure the effects of model aggregation and structure on model output variance. It is concluded that, given the particular scheme modelled and unavoidably subjective assumptions of input variance, simple and simplistic models may produce similar outputs to more robust and disaggregated models

Reconciling Model and Information Uncertainty in Development Appraisal

This paper investigates the effect of choices of model structure and scale in development viability appraisal. The paper addresses two questions concerning the application of development appraisal techniques to viability modelling within the UK planning system. The first relates to the extent to which, given intrinsic input uncertainty, the choice of model structure significantly affects model outputs. The second concerns the extent to which, given intrinsic input uncertainty, the level of model complexity significantly affects model outputs. Monte Carlo simulation procedures are applied to a hypothetical development scheme in order to measure the effects of model aggregation and structure on model output variance. It is concluded that, given the particular scheme modelled and unavoidably subjective assumptions of input variance, simple and simplistic models may produce similar outputs to more robust and disaggregated models.

Precisely Wrong or Roughly Right? An Evaluation of Development Viability

This paper investigates the effect of choices of model structure and scale in development viability appraisal. The paper addresses two questions concerning the application of development appraisal techniques to viability modelling within the UK planning system. The first relates to the extent to which, given intrinsic input uncertainty, the choice of model structure significantly affects model outputs. The second concerns the extent to which, given intrinsic input uncertainty, the level of model complexity significantly affects model outputs. Monte Carlo simulation procedures are applied to a hypothetical development scheme in order to measure the effects of model aggregation and structure on model output variance. It is concluded that, given the particular scheme modelled and unavoidably subjective assumptions of input variance, that simple and simplistic models may produce similar outputs to more robust and disaggregated models.

Why Do Some Places Succeed When Others Decline? A Social Interaction Model of Cluster Viability

One of the most convincing explanations papers generally provide concerning clusters in knowledge-based economies refers to the geographically bounded dimension of knowledge spillovers. Here we shall underline that location decision externalities precede local knowledge spillovers in the explanation of cluster aggregate efficiency, which thus requires us to focus on the sequential process of location and the nature of interdependences in location decision-making. To that end, we mean to associate cluster emergence with the formation of locational norms, and to study the critical parameters of their stability. These parameters relate to the type of decision externalities among more or less cognitively distant firms, which influences the weight and the resulting ambivalent role of knowledge spillovers at the aggregate level of clusters. We suggest two theoretical propositions which we test within a simple and general norm location dynamics modeling framework. We then proceed to discuss the results so obtained by comparing them with an emerging related literature based on the life cycle and viability of clustersclusters, location under decision externalities, cognitive distance, knowledge spillovers