Directed search with real options

While search is normally modelled by economists purely in terms of decisions over making observations, this paper models it as a process in which information is gained through feedback from innovatory product launches. The information gained can then be used to decide whether to exercise real options. In the model the initial decisions involve a product design and the scale of production capacity. There are then real options to change these factors based on what is learned. The case of launching product variants in parallel is also considered. Under ‘true’ uncertainty, the model can be seen in terms of heuristic decision-making based on subjective beliefs with limited foresight. Search costs, the values of the real options, beliefs, and the cost of capital are all shown to be significant in determining the search path.

Buyer–seller interaction in experimental spatial markets

We report results from experimental spatial markets with endogenous buyer location on a discrete version of Hotelling's linear city. Buyer locations favor more often the hypothesis of transportation cost minimization than that of strategic location aimed at increasing price competition between sellers. However, the latter of the two hypotheses receives systematic support too. Differentiation by seller-subjects is substantially less than the theory would predict for the specific framework used. Our results suggest that location strategies adopted by subjects can be seen as a rational process favoring conservative product design and spatial agglomeration of economic activities.

Sparse kernel density estimator using orthogonal regression based on D-optimality experimental design

A novel sparse kernel density estimator is derived based on a regression approach, which selects a very small subset of significant kernels by means of the D-optimality experimental design criterion using an orthogonal forward selection procedure. The weights of the resulting sparse kernel model are calculated using the multiplicative nonnegative quadratic programming algorithm. The proposed method is computationally attractive, in comparison with many existing kernel density estimation algorithms. Our numerical results also show that the proposed method compares favourably with other existing methods, in terms of both test accuracy and model sparsity, for constructing kernel density estimates.

Sparse multioutput radial basis function network construction using combined locally regularised orthogonal least square and D-optimality experimental design

A construction algorithm for multioutput radial basis function (RBF) network modelling is introduced by combining a locally regularised orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximised model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of producing a very parsimonious RBF network model with excellent generalisation performance. The D-optimality design criterion enhances the model efficiency and robustness. A further advantage of the combined approach is that the user only needs to specify a weighting for the D-optimality cost in the combined RBF model selecting criterion and the entire model construction procedure becomes automatic. The value of this weighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values.

Sparse kernel regression modeling using combined locally regularized orthogonal least squares and D-optimality experimental design

The note proposes an efficient nonlinear identification algorithm by combining a locally regularized orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximized model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of producing a very parsimonious model with excellent generalization performance. The D-optimality design criterion further enhances the model efficiency and robustness. An added advantage is that the user only needs to specify a weighting for the D-optimality cost in the combined model selecting criterion and the entire model construction procedure becomes automatic. The value of this weighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values.

Regression based D-optimality experimental design for sparse kernel density estimation

This paper derives an efficient algorithm for constructing sparse kernel density (SKD) estimates. The algorithm first selects a very small subset of significant kernels using an orthogonal forward regression (OFR) procedure based on the D-optimality experimental design criterion. The weights of the resulting sparse kernel model are then calculated using a modified multiplicative nonnegative quadratic programming algorithm. Unlike most of the SKD estimators, the proposed D-optimality regression approach is an unsupervised construction algorithm and it does not require an empirical desired response for the kernel selection task. The strength of the D-optimality OFR is owing to the fact that the algorithm automatically selects a small subset of the most significant kernels related to the largest eigenvalues of the kernel design matrix, which counts for the most energy of the kernel training data, and this also guarantees the most accurate kernel weight estimate. The proposed method is also computationally attractive, in comparison with many existing SKD construction algorithms. Extensive numerical investigation demonstrates the ability of this regression-based approach to efficiently construct a very sparse kernel density estimate with excellent test accuracy, and our results show that the proposed method compares favourably with other existing sparse methods, in terms of test accuracy, model sparsity and complexity, for constructing kernel density estimates.

Nonlinear model structure design and construction using orthogonal least squares and D-optimality design

A very efficient learning algorithm for model subset selection is introduced based on a new composite cost function that simultaneously optimizes the model approximation ability and model robustness and adequacy. The derived model parameters are estimated via forward orthogonal least squares, but the model subset selection cost function includes a D-optimality design criterion that maximizes the determinant of the design matrix of the subset to ensure the model robustness, adequacy, and parsimony of the final model. The proposed approach is based on the forward orthogonal least square (OLS) algorithm, such that new D-optimality-based cost function is constructed based on the orthogonalization process to gain computational advantages and hence to maintain the inherent advantage of computational efficiency associated with the conventional forward OLS approach. Illustrative examples are included to demonstrate the effectiveness of the new approach.

A Bayesian design criterion for locating the optimum point on a response surface

Most factorial experiments in industrial research form one stage in a sequence of experiments and so considerable prior knowledge is often available from earlier stages. A Bayesian A-optimality criterion is proposed for choosing designs, when each stage in experimentation consists of a small number of runs and the objective is to optimise a response. Simple formulae for the weights are developed, some examples of the use of the design criterion are given and general recommendations are made. (C) 2003 Elsevier B.V. All rights reserved.

Design and theoretical study of nickel catalysts for syndiotactic polyolefins

A nickel catalyst was modeled with ligand L-2, [ NH = CH-CH = CH-O](-), which should have potential use as a syndiotactic polyolefin catalyst, and the reaction mechanism was studied by theoretical calculations using the density functional method at the B3LYP/ LANL2MB level. The mechanism involves the formation of the intermediate [(NiLMe)-Me-2](+), in which the metal occuples a T-shaped geometry. - This intermediate has two possible structures with the methyl group trans either to the oxygen or to the nitrogen atom of L-2. The results show that both structures can lead to the desired product via similar reaction paths, A and B. Thus, the polymerization could be considered as taking place either with the alkyl group occupying the position trans to the Ni-O or trans to the Ni-N bond in the catalyst. The polymerization process thus favors the catalysis of syndiotactic polyolefins. The syndiotactic synthesis effects could also be enhanced by variations in the ligand substituents. From energy considerations, we can conclude that it is more favorable for the methyl group to occupy the trans-O position to form a complex than to occupy the trans-N position. From bond length considerations, it is also more favoured for ethene to occupy the trans-O position than to occupy the trans-N position.

COMPUTER assisted technologies to enhance CONCURRENce in building DESIGN

Modern organisms are adapted to a wide variety of habitats and lifestyles. The processes of evolution have led to complex, interdependent, well-designed mechanisms of todays world and this research challenge is to transpose these innovative solutions to resolve problems in the context of architectural design practice, e.g., to relate design by nature with design by human. In a design by human environment, design synthesis can be performed with the use of rapid prototyping techniques that will enable to transform almost instantaneously any 2D design representation into a physical three-dimensional model, through a rapid prototyping printer machine. Rapid prototyping processes add layers of material one on top of another until a complete model is built and an analogy can be established with design by nature where the natural lay down of earth layers shapes the earth surface, a natural process occurring repeatedly over long periods of time. Concurrence in design will particularly benefit from rapid prototyping techniques, as the prime purpose of physical prototyping is to promptly assist iterative design, enabling design participants to work with a three-dimensional hardcopy and use it for the validation of their design-ideas. Concurrent design is a systematic approach aiming to facilitate the simultaneous involvment and commitment of all participants in the building design process, enabling both an effective reduction of time and costs at the design phase and a quality improvement of the design product. This paper presents the results of an exploratory survey investigating both how computer-aided design systems help designers to fully define the shape of their design-ideas and the extent of the application of rapid prototyping technologies coupled with Internet facilities by design practice. The findings suggest that design practitioners recognize that these technologies can greatly enhance concurrence in design, though acknowledging a lack of knowledge in relation to the issue of rapid prototyping.

Using artefacts to mediate understanding in design conversations

The journey from the concept of a building to the actual built form is mediated with the use of various artefacts, such as drawings, product samples and models. These artefacts are produced for different purposes and for people with different levels of understanding of the design and construction processes. This paper studies design practice as it occurs naturally in a real-world situation by observing the conversations that surround the use of artefacts at the early stages of a building's design. Drawing on ethnographic data, insights are given into how the use of artefacts can reveal a participant's understanding of the scheme. The appropriateness of the method of conversation analysis to reveal the users' understanding of a scheme is explored by observing spoken micro-interactional behaviours. It is shown that the users' understanding of the design was developed in the conversations around the use of artefacts, as well as the knowledge that is embedded in the artefacts themselves. The users' confidence in the appearance of the building was considered to be gained in conversation, rather than the ability of the artefacts to represent a future reality.

Sparse model identification using orthogonal forward regression with basis pursuit and D-optimality

An efficient model identification algorithm for a large class of linear-in-the-parameters models is introduced that simultaneously optimises the model approximation ability, sparsity and robustness. The derived model parameters in each forward regression step are initially estimated via the orthogonal least squares (OLS), followed by being tuned with a new gradient-descent learning algorithm based on the basis pursuit that minimises the l(1) norm of the parameter estimate vector. The model subset selection cost function includes a D-optimality design criterion that maximises the determinant of the design matrix of the subset to ensure model robustness and to enable the model selection procedure to automatically terminate at a sparse model. The proposed approach is based on the forward OLS algorithm using the modified Gram-Schmidt procedure. Both the parameter tuning procedure, based on basis pursuit, and the model selection criterion, based on the D-optimality that is effective in ensuring model robustness, are integrated with the forward regression. As a consequence the inherent computational efficiency associated with the conventional forward OLS approach is maintained in the proposed algorithm. Examples demonstrate the effectiveness of the new approach.

M-estimator, and D-optimality model construction using orthogonal forward regression

This correspondence introduces a new orthogonal forward regression (OFR) model identification algorithm using D-optimality for model structure selection and is based on an M-estimators of parameter estimates. M-estimator is a classical robust parameter estimation technique to tackle bad data conditions such as outliers. Computationally, The M-estimator can be derived using an iterative reweighted least squares (IRLS) algorithm. D-optimality is a model structure robustness criterion in experimental design to tackle ill-conditioning in model Structure. The orthogonal forward regression (OFR), often based on the modified Gram-Schmidt procedure, is an efficient method incorporating structure selection and parameter estimation simultaneously. The basic idea of the proposed approach is to incorporate an IRLS inner loop into the modified Gram-Schmidt procedure. In this manner, the OFR algorithm for parsimonious model structure determination is extended to bad data conditions with improved performance via the derivation of parameter M-estimators with inherent robustness to outliers. Numerical examples are included to demonstrate the effectiveness of the proposed algorithm.