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We present two models of the electric field for a canonical problem in electric discharge machining. In particular, an analytical solution based on optimal parameter estimation is discussed, followed by a comparison with numerical solutions based on finite elements and Galerkin boundary elements. The problem is interesting because the structure of the field near the sharp asperity is a critical parameter in realistic models of the electric discharge machining process.

A hybrid formulation is proposed that incorporates finite element substructuring and Galerkin boundary elements in the numerical solution of Poisson's or Laplace's equation with open boundaries. Substructuring the problem can dramatically decreases the size of matrix to be solved. It is shown that the boundary integration that results from application of Green's first theorem to the weighted residual statement can be used to advantage by imposing potential and flux continuity through the contour which separates the interior and exterior regions. In fact, the boundary integration is of exactly the same form as that found in Galerkin boundary elements.

We derive marginal conditions of optimality (i.e., Euler equations) for a general class of Dynamic Discrete Choice (DDC) structural models. These conditions can be used to estimate structural parameters in these models without having to solve for approximate value functions. This result extends to discrete choice models the GMM-Euler equation approach proposed by Hansen and Singleton (1982) for the estimation of dynamic continuous decision models. We first show that DDC models can be represented as models of continuous choice where the decision variable is a vector of choice probabilities. We then prove that the marginal conditions of optimality and the envelope conditions required to construct Euler equations are also satisfied in DDC models. The GMM estimation of these Euler equations avoids the curse of dimensionality associated to the computation of value functions and the explicit integration over the space of state variables. We present an empirical application and compare estimates using the GMM-Euler equations method with those from maximum likelihood and two-step methods.

This chapter estimates the demand for flights in an international air travel market using a unique dataset with detailed information not only on flight choices but also on contemporaneous prices and characteristics of all the alternative non-booked flights. The estimation strategy employs a simple discrete choice random utility model that we use to analyze how choices and its response to prices depend on the departing airport, the identity of the carrier, and the departure date and time. The results show that a 10% increase in prices in a 100-seat aircraft throughout a 100-period selling season decreases quantity demanded by 7.7 seats. We also find that the quantity demanded is more responsive to prices for Delta and American, during morning and evening flights and that the response to prices changes significantly over different departure dates.

In this article, we consider the nonparametric identification of Markov dynamic games models in which each firm has its own unobserved state variable, which is persistent over time. This class of models includes most models in the Ericson and Pakes (1995) and Pakes and McGuire (1994) framework. We provide conditions under which the joint Markov equilibrium process of the firms’ observed and unobserved variables can be nonparametrically identified from data. For stationary continuous action games, we show that only three observations of the observed component are required to identify the equilibrium Markov process of the dynamic game. When agents’ choice variables are discrete, but the unobserved state variables are continuous, four observations are required.

Capital budgeting projects fail about as often as they succeed. Recent research shows that accountants may frame information related to capital budgeting projects to be consistent with their preference for the project (e.g., accept or reject), perhaps in order to persuade management to agree with them. Psychology research consistently shows that framed information results in systematic differences in judgments. The purpose of this study is to examine whether framed information affects capital budgeting decisions, and to examine whether this effect is moderated by the importance of the potential project. Results from an experimental case completed by 173 participants indicate attribute frames affect capital budgeting decisions, however, the effect is moderated by the importance of the decision.

Capital budgeting decisions frequently go awry. We investigate whether the party gathering the data utilizes persuasive communications when presenting the information to a superior. Specifically, we analyze whether the information is framed differently depending on his or her opinion. Since prior research has shown that differential framing of the same information affects decisions this may be one contributor to capital budgeting failures. We found that participants did frame the information differently depending on whether they chose to accept or reject the project. Our control group, no decision required, was materially different from the reject group but not materially different from the accept group.

The purpose of this paper is to understand the social impact of data on communities from cases of community data utilization.

This study took an interpretive qualitative approach and conducted a semi-structured phone interview with 45 participants from data intermediaries and local community organizations.

The results demonstrate both direct and indirect impacts of data on local levels, including resolving local problems from data-driven decisions, realizing unknown problems or correcting misrepresented problems, changing community data practices, strengthening community identity and enhancing the community’s data skills.

The research shows that communities’ data utilization supported community-led actions and initiatives from the bottom-up perspective, which demonstrates the need for supporting communities’ data work.

Minimizing inequality in data utilization should be resolved so that all communities can benefit from the power of data.

By demonstrating evidence of data being critical to encouraging communities’ data utilization, this study fills the gap in existing research, which lacks a clear explanation for how the potential of data can be realized at the local level.

When introducing new health technologies, decision makers must integrate research evidence with local operational management information to guide decisions about whether and under what conditions the technology will be used. Multi‐criteria decision analysis can support the adoption or prioritization of health interventions by using criteria to explicitly articulate the health organization's needs, limitations, and values in addition to evaluating evidence for safety and effectiveness. This paper seeks to describe the development of a framework to create agreed‐upon criteria and decision tools to enhance a pre‐existing local health technology assessment (HTA) decision support program.

The authors compiled a list of published criteria from the literature, consulted with experts to refine the criteria list, and used a modified Delphi process with a group of key stakeholders to review, modify, and validate each criterion. In a workshop setting, the criteria were used to create decision tools.

A set of user‐validated criteria for new health technology evaluation and adoption was developed and integrated into the local HTA decision support program. Technology evaluation and decision guideline tools were created using these criteria to ensure that the decision process is systematic, consistent, and transparent.

This framework can be used by others to develop decision‐making criteria and tools to enhance similar technology adoption programs.

The development of clear, user‐validated criteria for evaluating new technologies adds a critical element to improve decision‐making on technology adoption, and the decision tools ensure consistency, transparency, and real‐world relevance.

Gives a bibliographical review of the finite element meshing and remeshing from the theoretical as well as practical points of view. Topics such as adaptive techniques for meshing and remeshing, parallel processing in the finite element modelling, etc. are also included. The bibliography at the end of this paper contains 1,727 references to papers, conference proceedings and theses/dissertations dealing with presented subjects that were published between 1990 and 2001.