Texting while driving as impulsive choice: A behavioral economic analysis

The goal of the present study was to examine the utility of a behavioral economic analysis to investigate the role of delay discounting in texting while driving. A sample of 147 college students completed a survey to assess how frequently they send and read text messages while driving. Based on this information, students were assigned to one of two groups: 19 students who frequently text while driving and 19 matched-control students who infrequently text while driving but were similar in gender, age, years of education, and years driving. The groups were compared on the extent to which they discounted, or devalued, delayed hypothetical monetary rewards using a delay-discounting task. In this task, students made repeated choices between $1000 available after a delay (ranging from 1 week to 10 years) and an equal or lesser amount of money available immediately. The results show that the students who frequently text while driving discounted delayed rewards at a greater rate than the matched control students. The study supports the conclusions that texting while driving is fundamentally an impulsive choice made by drivers, and that a behavioral economic approach may be a useful research tool for investigating the decision-making processes underlying risky behaviors.     

Interpretation: its role in architectural designing

Through studies of processes of architectural designing, a sub-process of interpretation emerges as the single most important force in the shaping of design solutions. An interpretation is formed when design moves, which are enacted on knowledge pertaining to the task, transform its pieces into a stable structure by achieving a unique relationship among them. A look at first-year students' design efforts demonstrates how, through construing the task, interpretations are not generated. The potential for creating global interpretations is likely to be found mostly at the starting point, which reflects the designer's state of mind at the outset of the desing endeavour. Different ways in which experimentation is carried out are analysed, notably play and discovery, without which it is hard to imagine a process of composition of pieces of knowledge. Play and discovery are shown to induce new interpretations and to help substantiate old ones. The contribution of long chains of moves and the timely generation and evaluation of design criteria and constraints is pointed out. These different cognitive aspects reveal how central an activity interpreting is throughout the entire process of architectural designing.     

Robust split‐plot designs

In many experimental situations, practitioners are confronted with costly, time consuming, or hard‐to‐change (HTC) factors. These practical or economic restrictions on randomization can be accommodated with a split‐plot design structure that minimizes the manipulation of the HTC factors. Selecting a good design is a challenging task and requires knowledge of the opportunities and restrictions imposed by the experimental apparatus and an evaluation of statistical performance among competing designs. Building on the well‐established evaluation criteria for the completely randomized context, we emphasize the unique qualitative and quantitative evaluation criteria for split‐plot designs. An example from hypersonic propulsion research is used to demonstrate the consideration of multiple design evaluation criteria. Published in 2007 by John Wiley & Sons, Ltd.     

Teaching engineering design: Can reading a textbook make a difference?

Teaching design is an integral part of most engineering curricula. Often, students are introduced to the engineering design process through a chapter in a textbook. Does this passive approach to teaching an active process aid the students' learning? An experiment was conducted to assess what students learn about the design process when they read a text. Here, 10 students enrolled in a freshman course were asked to read aloud from a freshmen engineering textbook. Half of the subjects read the text prior to solving three open-ended engineering design problems and the other half solved the same problems before they read the text. Both the subjects' process in solving the problems, as well as the quality of their solutions (the product), are assessed. Results show that subjects that read the text before they solved the three problems spent significantly more time solving the problems and were more sophisticated in their problem solving strategies. These subjects also scored better when judged on the quality of their approach to the problem (including the number of design criteria considered, communications, assumptions, and technical accuracy). However, these subjects did not score better on a quality measure of the final solution.     

Contemporary Approaches to Paced Visual Inspection

An approach to the study of inspection task design has been investigated. Twenty female inspectors did a simulated paced visual inspection task for 90 minutes each. The experimental variables were display size, product pacing, and fault information as a measure of defect probability. They were tested in a 15 point design with 3 replications of the center point. The criterion of performance was the fraction of total products correctly identified as being defect free. From the experimental data quantitative estimates of performance increments or decrements associated with changes in inspection task variables were determined. The implications of observations and their importance in the design of an inspection task have been discussed.     

D-optimal designs

Many classical symmetrical designs have desirable characteristics, one of which is called D-optimality. The D-optimality concept can also be applied to select a design when the classical symmetrical designs cannot be used, such as when the experimental region is not regular in shape, when the number of experiments chosen by a classical design is too large or when one wants to apply models that deviate from the usual first or second order ones. The D-optimality concept is developed and it is also explained that D-optimality is only one possible criterion to choose a particular design. A few other criteria are also given that complement the information obtained by the D-criterion.     

The construction of robust mixture‐process experimental designs via genetic algorithm

Mixture experiments with the presence of process variables are commonly encountered in the manufacturing industry. The experimenter who plans to conduct mixture experiments in which a process involves the combination of machines, methods, and other resources will try to find condition of design factors which make the product/process insensitive or robust to the variability transmitted into the response variable. We propose the genetic algorithm (GA) for generating robust mixture‐process experimental designs involving control and noise variables. When the noise variables, which are extremely difficult to control or not routinely controlled during the manufacturing process and may change without warning, are considered in a mixture experiment, we propose the robust design setting. When considering a robust design, the design that has a lower and flatter faction of design space curves for all levels of the controllable process variables at varying noise interaction is preferable. We evaluate the designs with respect to these criteria for both the mean model and the slope model. The evaluation demonstrates that the proposed GA designs are robust to the contribution of the interactions involving the noise variables.     

A statistical engineering strategy for mixture problems with process variables

Experimenting with both mixture components and process variables, especially when there is likely to be interaction between these two sets of variables, is discussed. We consider both design and analysis questions within the context of addressing an actual mixture/process problem. We focus on a strategy for attacking such problems, as opposed to finding the best possible design or best possible model for a given set of data. In this sense, a statistical engineering framework is used. In particular, when we consider the potential of fitting parsimonious linear additive or nonlinear models as opposed to larger linearized models, we find potential to reduce the size of experimental designs. It is difficult in practice to know what type of model will best fit the resulting data. Therefore, an integrated, sequential design and analysis strategy is recommended. Using two published data sets and one new data set, we find that in some cases nonlinear models, or linear additive models —with no process/mixture interaction terms, enable reduction of experimentation on the order of 50%. In other cases, additive or nonlinear models will not suffice. We therefore provide guidelines as to when such an approach is likely to succeed, and propose an overall strategy for these types of problems.     

Construction of mixed-level supersaturated designs and comparison of their performance: Application to a gas chromatographic method

A supersaturated design is a design for which there are fewer runs than effects to be estimated. Although two-level supersaturated designs are becoming increasingly popular, mixed-level designs are scarcely used. Mixed-level designs are needed when the response is based on a polynomial response surface model or in situations where factors are nominal variables (with more than two modalities). The aim of this study is to explore the construction of mixed-level supersaturated designs and to evaluate their performance from the analysis of peppermint oil using a gas chromatographic method as application. This experimental setup requires the study of seven factors at two levels and five factors at three levels. Different building methods are tested from asymmetric or symmetric supersaturated designs. The mixed-level supersaturated designs obtained are compared from the point of view of a priori criteria with the aim of evaluating which criteria are better suited to judge the quality and fitness for purpose of these experimental designs. Finally, the results of the supersaturated designs are compared to the complete classical design.     

Analytic Cognitive Task Allocation: a decision model for cognitive task allocation

This paper introduces Analytic Cognitive Task Allocation (Analytic CTA) as a decision model for cognitive task allocation. Analytic CTA can support system designers systematically analyse alternative allocation choices, evaluate them through criterion-based quantitative judgements and integrate partial judgements towards a final allocation decision. Analytic CTA was applied in the design of cognitive task allocation by testing three hypotheses. The hypotheses postulated that Analytic CTA can effectively support system designers in three ways: (a) in the identification of task requirements; (b) in the definition of design trade-offs between human allocation and automation; and (c) in the derivation of the allocation decision. Two groups of system designers allocated cognitive functions from three task scenarios in production planning and control. The experimental group, which was supported by the decision model, made allocation decisions which expressed weak preference for either human allocation or automation, consistent with the experimental tasks which had been selected as to elicit weak preferences. The control group relied on the early selection of an automation technology as the means of structuring the design problem. This involved reducing the task to a version suitable for automation, introduced a pro-automation bias and resulted in allocation decisions expressing strong preference for automation. These results were found in support of the experimental hypotheses.     

Towards a conceptual model of procedural knowledge degradation

Research has derived a dynamic range of theories associated with the acquisition of knowledge. In contrast, the current research seeks mechanisms by which acquired knowledge degrades, as well as mechanisms that could be computationally modelled. For procedural tasks, such as product assembly or supervisory control, variables including interference can inhibit the ability to retrieve procedural knowledge in different ways than the retrieval of declarative knowledge like phone numbers. High consequence tasks, such as air traffic control or maintenance on nuclear weapons, require proficiency to be maintained. Thus, an understanding of mechanisms that might complicate effective performance could improve approaches to job design. To explore theoretical underpinnings for procedural knowledge degradation, three areas of literature were explored. The areas included theoretical, computational, and ecological research. The literature serves as input for a conceptual model of procedural knowledge degradation.     

Engineering Design Processes: A Comparison of Students and Expert Practitioners

In this paper we report on an in‐depth study of engineering design processes. Specifically, we extend our previous research on engineering student design processes to compare the design behavior of students and expert engineers. Nineteen experts from a variety of engineering disciplines and industries each designed a playground in a lab setting, and gave verbal reports of their thoughts during the design task. Measures of their design processes and solution quality were compared to pre‐existing data from 26 freshmen and 24 seniors. The experts spent significantly more time on the task overall and in each stage of engineering design, including significantly more time problem scoping. The experts also gathered significantly more information covering more categories. Results support the argument that problem scoping and information gathering are major differences between advanced engineers and students, and important competencies for engineering students to develop. Timeline representations of the expert designers' processes illustrate characteristic distinctions we found and may help students gain insights into their own design processes.     

Determination of ranged sets of design specifications by incorporating design-space heterogeneity

Setting design specifications (targets) is a critical task in the early stages of the design process. Flexible targets can accommodate uncertainty and changes in design by postponing design commitments and preserving design freedom. In this article, a new method is developed for obtaining a ranged set of design specifications that meets design criteria whilst incorporating design-space heterogeneity; meaning some areas in the design attribute space are more achievable than the others. The proposed method has two notable features. First, a quantization algorithm based on rough-set theory is used to decompose a design attribute space into sub-regions on the basis of how well they meet design criteria. Second, a new design-flexibility measure is used as a metric to select the most desired ‘target region’ on the bases of both the size of the region and the influence of potential design alternatives on overall achievability. The proposed approach enhances the capacity of a design system to adapt to evolving design knowledge, as well as to unexpected changes. The proposed method is demonstrated by a numerical example and the design of a domestic blender.     

Experimental Design: Review and Comment

We review major developments in the design of experiments, offer our thoughts on important directions for the future, and make specific recommendations for experimenters and statisticians who are students and teachers of experimental design, practitioners of experimental design, and researchers jointly exploring new frontiers. Specific topics covered are optimal design, computer-aided design, robust design, response surface design, mixture design, factorial design, block design, and designs for nonlinear models.     

Materials selection for a cooling plate using control area diagrams

Merit indices are used to rank materials and are of fundamental importance in materials selection. Traditionally, merit indices have only been available for elementary design cases. In the present paper merit indices are generalised to cooling systems where heat flow and strength are design criteria in a materials optimisation framework. A cooling tube and a cooling plate are considered. A new concept, merit exponent is used that is related to the merit indices. A definition of the merit exponent is given also for cases with many design variables. In each design case a number of merit exponents are involved. It is a nontrivial task to identify which they are and when each of them is applicable. For this purpose control area diagrams (CAD) are used. A CAD is a diagram with the controlling properties on the axes, and areas where one or more constraints are active. For the cooling systems the controlling properties are heat conductivity and strength. The active constraints define the relevant merit exponent. The constraints involve the controlling properties and geometrical variables. Principles are established for how to set up the CAD and to derive the merit exponents.     

Bridging process and structure through criteria

Scholars in design face two central challenges in design methods inquiry: firstly, to integrate process and solution, and secondly, to examine developmental junctures leading to innovation. With these goals in mind, we maintain that design criteria deserve greater attention and illustrate this position with a study of noted designers' use of criteria. The primary finding was that five criteria functions were considered during the design process. The study also explored potential relationships between designer variables (i.e., professional expertise and personality type) and criteria use.     

Stochastic Subset Optimization for optimal reliability problems

Reliability-based design of a system often requires the minimization of the probability of system failure over the admissible space for the design variables. For complex systems this probability can rarely be evaluated analytically and so it is often calculated using stochastic simulation techniques, which involve an unavoidable estimation error and significant computational cost. These features make efficient reliability-based optimal design a challenging task. A new method called Stochastic Subset Optimization (SSO) is proposed here for iteratively identifying sub-regions for the optimal design variables within the original design space. An augmented reliability problem is formulated where the design variables are artificially considered as uncertain and Markov Chain Monte Carlo techniques are implemented in order to simulate samples of them that lead to system failure. In each iteration, a set with high likelihood of containing the optimal design parameters is identified using a single reliability analysis. Statistical properties for the identification and stopping criteria for the iterative approach are discussed. For problems that are characterized by small sensitivity around the optimal design choice, a combination of SSO with other optimization algorithms is proposed for enhanced overall efficiency.     

Mechanisms of knowledge degradation in a resource management task ¶

Previous research has focused more on learning relative to forgetting despite both being generally recognized as important human performance issues. This research pursued an improved understanding of variables that impact the effectiveness with which humans can retrieve procedural knowledge, such as mathematics or programming. Two studies reviewed yielded varying results in experiments that investigated factors that can inhibit the retrieval of procedural knowledge from memory. To explore theoretical explanations for the opposing results, 32 human participants performed a resource management task in the multi-attribute task battery (MATB) manipulating task strategy and colour symbology. Human performance resulted in significant interference effects for a switch in strategy, but not with a change in the implicit meaning of colours. Therefore, the participants’ sensitivity to the implicit meaning of visual stimuli was not as significant as a more comprehensive change in rule structure associated with procedural knowledge.     

Use of experimental designs to optimize fluidized bed granulation of maltodextrin

The goal of this study was to use the experimental design approach in order to determine which process parameters are the most influent to granules properties. The agglomeration process was performed with a fluidized bed processor equipped with a top-spray tilted nozzle. The granules were obtained by binding fluidized particles of maltodextrin (DE 12) with an aqueous solution of maltodextrin. The variables considered in the experimental design were fluidization air flow rate, drying air temperature, spraying pressure and binder flow rate. The physical properties of the granules were evaluated in terms of granule size, particle size distribution, dissolution time and flowability. Moreover, the amount of binder for each experiment was noted. The statistical analysis performed with Statgraphics® Centurion version XVI software indicated that the best operating conditions were found for low fluidization air flow rate, high drying air temperature, high spraying pressure and high binder flow rate. The study also revealed the difficult task of acquiring all the optimum properties at the same time.     

Multilevel assessment method for reliable design of composite structures

This work aims at demonstrating the interest of a new methodology for the design and optimization of composite materials and structures. Coupling reliability methods and homogenization techniques allow the consideration of probabilistic design variables at different scales. The main advantage of such an original micromechanics-based approach is to extend the scope of solutions for engineering composite materials to reach or to respect a given reliability level. This approach is illustrated on a civil engineering case including reinforced fiber composites. Modifications of microstructural components properties, manufacturing process, and geometry are investigated to provide new alternatives for design and guidelines for quality control.