The unconstrained binary quadratic programming problem: a survey

In recent years the unconstrained binary quadratic program (UBQP) has grown in importance in the field of combinatorial optimization due to its application potential and its computational challenge. Research on UBQP has generated a wide range of solution techniques for this basic model that encompasses a rich collection of problem types. In this paper we survey the literature on this important model, providing an overview of the applications and solution methods.     

Cohort change and the diffusion of environmental concern: a cross-national analysis

This study explores value change across cohorts for a multinational population sample. Employing a diffusion-of-innovations approach, we combine competing theories predicting the relationship between socioeconomic status (SES) and environmentalism: post-materialism and affluence theories, and global environmentalism theory. The diffusion argument suggests that high-SES groups first adopt pro-environmental views, but as time passes by, environmentalism diffuses to lower-SES groups. We test the diffusion argument using a sample of 18 countries for two waves (years 1993 and 2000) from the International Social Survey Project. Cross-classified multilevel modeling allows us to identify a nonlinear interaction between cohort and education, our core measure of SES, in predicting environmental concern, while controlling for age and period. We find support for the diffusion argument and demonstrate that the positive effect of education on environmental concern first increases among older cohorts and then starts to level off until a bend point is reached for individuals born around 1940 and becomes progressively weaker for younger cohorts.     

Additive Risk Model Using Piecewise Constant Hazard Function

We study a weighted least squares estimator for Aalen's additive risk model with right-censored survival data which allows for a very flexible handling of covariates. We divide the follow-up period into intervals and assume a constant hazard rate in each interval. The model is motivated as a piecewise approximation of a hazard function composed of three parts: arbitrary nonparametric functions for some covariate effects, smoothly varying functions for others, and known (or constant) functions for yet others. The proposed estimator is an extension of the grouped data version of the Huffer and McKeague (1991 Huffer , F. W. , McKeague , I. W. ( 1991 ). Weighted least squares estimation for Aalen's additive risk model . Journal of the American Statistical Association 86 : 114 – 129 .[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) estimator. For our model, since the number of parameters is finite (although large), conventional approaches (such as maximum likelihood) are easy to formulate and implement. The approach is illustrated by simulations, and is compared to the previous studies. The method is also applied to the Framingham study data.     

Domestic violence in veterans with posttraumatic stress disorder who seek couples therapy

Domestic violence rates among veterans with posttraumatic stress disorder (PTSD) are higher than those of the general population. Individuals who have been diagnosed with PTSD who seek couples therapy with their partners constitute an understudied population. Self-report measures of domestic violence, relationship satisfaction, and intimacy were administered at intake to 179 couples seeking relationship therapy at a Veterans Affairs clinic. Couples in which the veteran was diagnosed with combat-related PTSD were compared with two other groups based on the veteran's primary diagnosis (depression, adjustment disorder/V-code). Both the PTSD- and depression-diagnosed veterans perpetrated more violence than did those with adjustment/V-code diagnoses. Domestic violence rates among depressed and PTSD-diagnosed veterans were much higher than those found in previous research. Implications for assessment and treatment are discussed.     

Optimizing Service Attributes: The Seller's Utility Problem*

Service designers predict market share and sales for their new designs by estimating consumer utilities. The service's technical features (for example, overnight parcel delivery), its price, and the nature of consumer interactions with the service delivery system influence those utilities. Price and the service's technical features are usually quite objective and readily ascertained by the consumer. However, consumer perceptions about their interactions with the service delivery system are usually far more subjective. Furthermore, service designers can only hope to influence those perceptions indirectly through their decisions about nonlinear processes such as employee recruiting, training, and scheduling policies. Like the service's technical features, these process choices affect quality perceptions, market share, revenues, costs, and profits. We propose a heuristic for the NP‐hard service design problem that integrates realistic service delivery cost models with conjoint analysis. The resulting seller's utility function links expected profits to the intensity of a service's influential attributes and also reveals an ideal setting or level for each service attribute. In tests with simulated service design problems, our proposed configurations compare quite favorably with the designs suggested by other normative service design heuristics.     

What makes a developmental network state durable?

As dynamic collaborations between networks of specialists have become more central to developing cutting-edge innovations, designing and sustaining innovation policies that foster them has become an increasingly central concern. This article reviews the characteristics of the “developmental network” or “neo-developmental” states that have been shown to foster innovations at the technological horizon. It then analyzes the US case to suggest why some developmental network approaches to governance have proven relatively durable, while others have not. We conclude with a discussion of what can – and cannot – be learned from the US case, and suggest the importance of comparative work on the sustainability of effective innovation-centered approaches to governance.     

Analyzing and Modeling the Maximum Diversity Problem by Zero-One Programming*

The problem of maximizing diversity deals with selecting a set of elements from some larger collection such that the selected elements exhibit the greatest variety of characteristics. A new model is proposed in which the concept of diversity is quantifiable and measurable. A quadratic zero-one model is formulated for diversity maximization. Based upon the formulation, it is shown that the maximum diversity problem is NP-hard. Two equivalent linear integer programs are then presented that offer progressively greater computational efficiency. Another formulation is also introduced which involves a different diversity objective. An example is given to illustrate how additional considerations can be incorporated into the maximum diversity model.