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Decision research in psychology has traditionally been influenced by the homo oeconomicus metaphor with its emphasis on normative models and deviations from the predictions of those models. In contrast, the principal metaphor of cognitive psychology conceptualizes humans as ‘information processors’, employing processes of perception, memory, categorization, problem solving and so on. Many of the processes described in cognitive theories are similar to those involved in decision making, and thus increasing cross-fertilization between the two areas is an important endeavour. A wide range of models and metaphors has been proposed to explain and describe ‘information processing’ and many models have been applied to decision making in ingenious ways. This special issue encourages cross-fertilization between cognitive psychology and decision research by providing an overview of current perspectives in one area that continues to highlight the benefits of the synergistic approach: cognitive modeling of multi-attribute decision making. In this introduction we discuss aspects of the cognitive system that need to be considered when modeling multi-attribute decision making (e.g., automatic versus controlled processing, learning and memory constraints, metacognition) and illustrate how such aspects are incorporated into the approaches proposed by contributors to the special issue. We end by discussing the challenges posed by the contrasting and sometimes incompatible assumptions of the models and metaphors.
Adoption of the new biofuel crop carinata (Brassica carinata A. Braun) in the southeastern United States will largely hinge on sound agronomic recommendations that can be economically incorporated into and are compatible with existing rotations. Timing of weed control is crucial for yield protection and long-term weed seedbank management, but predictive weed emergence models have not been as widely studied in winter crops for this purpose. In this work, we use observed and predicted emergence of a winter annual weed community to create recommendations for timing weed control according to weed and crop phenology progression. Observed emergence timings for four winter annual weed species in North Carolina were used to validate previously published models developed for winter annual weeds in Florida by accounting for temperature and daylength differences, and this approach explained more than 70% of the variability in observed emergence. Emergence of stinking chamomile (Anthemis cotula L.) and cutleaf evening primrose (Oenothera laciniata Hill.) followed biphasic patterns comparable to wild radish (Raphanus raphanistrum L.), which were predicted with previously published models accounting for 82% and 84% of the variation, respectively. Using the predictive models for weed emergence and carinata growth, critical control windows (CCW) were estimated for Clayton, NC, and Jay, FL, according to different planting dates. The results demonstrated how early planting coincided with the emergence of three competitive winter weeds, but early control could also remove a large proportion of the predicted emergence of these species. The framework for how planting timing will affect winter weed emergence and crop growth will be an instructive decision-making tool to help prepare farmers to manage weeds in carinata, but it could also be useful for weed management planning for other winter crops.
Healthcare has recently seen numerous exciting applications of artificial intelligence, industrial engineering, and operations research. This book, designed to be accessible to a diverse audience, provides an overview of interdisciplinary research partnerships that leverage AI, IE, and OR to tackle societal and operational problems in healthcare. The topics are drawn from a wide variety of disciplines, ranging from optimizing the location of AEDs for cardiac arrests to data mining for facilitating patient flow through a hospital. These applications highlight how engineering has contributed to medical knowledge, health system operations, and behavioral health. Chapter authors include medical doctors, policy-makers, social scientists, and engineers. Each chapter begins with a summary of the health care problem and engineering method. In these examples, researchers in public health, medicine, and social science as well as engineers will find a path to start interdisciplinary collaborations in health applications of AI/IE/OR.
This chapter reviews how people learn during apprenticeships, ways of guiding beginners while they engage in authentic situated activity with more experienced people. Apprenticeship practices are found throughout the world both in cultures with formal schooling and in those without. Traditional apprenticeship practices tend to focus on physical and visible activities, but most schooling is directed toward conceptual learning outcomes which are usually not physical and visible – like formulas in mathematics or theories in physics. This chapter extends apprenticeship research to cognitive apprenticeship, and describes apprenticeships that are designed to lead to abstract or conceptual knowledge. These involve scaffolding, metacognitive reflection, problem-based learning, and situated social practices. Effective apprenticeship often involves productive failure, when learners initially develop potential solutions that are wrong but that can be productively guided toward conceptually correct answers.
Extrapolation is often required to inform cost-effectiveness (CE) evaluations of immune-checkpoint inhibitors (ICIs) since survival data from pivotal clinical trials are seldom complete. The objectives of this study were to evaluate the accuracy of estimates of long-term overall survival (OS) predicted in French CE assessment reports of ICIs, and to identify models presenting the best fit to the observed long-term survival data.
A systematic review of French assessment reports of ICIs in the metastatic setting since inception until May 2020 was performed. A targeted literature review was conducted to collect associated extended follow-up of randomized controlled trials (RCTs) used in the CE assessment reports. Difference between projected and observed OS was calculated. A range of standard parametric and spline-based models were applied to the extended follow-up data from the RCT to determine the best-fitting survival models.
Of the 121 CE assessment reports published, 11 reports met the inclusion criteria. OS was underestimated in 73 percent of the CE assessment reports. The mean relative difference between each source was −13 percent (median: −15 percent; IQR: −0.4 to 26 percent). Models providing the best fit were those that could reflect nonmonotonic hazards.
Based on the available data at the time of submission, longer-term survival of ICIs was not fully captured by the extrapolation models used in CE assessments. Standard and flexible parametric models which can capture nonmonotonic hazard functions provided the best fit to the extended follow-up data. However, these models may still have performed poorly if fitted to survival data available at the time of submission to the French National Authority for Health.
Anti-selection occurs when information asymmetry exists between insurers and applicants. When an applicant knows they are at high risk of loss, but the insurer does not, the applicant may try to use this knowledge differential to secure insurance at a lower premium that does not match risk.
In model-based economic evaluations, the effectiveness parameter is often informed by studies with a limited duration of follow-up, requiring extrapolation of the treatment effect over a longer time horizon. Extrapolation from short-term data alone may not adequately capture uncertainty in that extrapolation. This study aimed to use structured expert elicitation to quantify uncertainty associated with extrapolation of the treatment effect observed in a clinical trial.
A structured expert elicitation exercise was conducted for an applied study of a podiatry intervention designed to reduce the rate of falls and fractures in the elderly. A bespoke web application was used to elicit experts’ beliefs about two outcomes (rate of falls and odds of fracture) as probability distributions (priors), for two treatment options (intervention and treatment as usual) at multiple time points. These priors were used to derive the temporal change in the treatment effect of the intervention, to extrapolate outcomes observed in a trial. The results were compared with extrapolation without experts’ priors.
The study recruited thirty-eight experts (geriatricians, general practitioners, physiotherapists, nurses, and academics) from England and Wales. The majority of experts (32/38) believed that the treatment effect would depreciate over time and expressed greater uncertainty than that extrapolated from a trial-based outcome alone. The between-expert variation in predicted outcomes was relatively small.
This study suggests that uncertainty in extrapolation can be informed using structured expert elicitation methods. Using structured elicitation to attach values to complex parameters requires key assumptions and simplifications to be considered.
To model performance of the Sequential Organ Failure Assessment (SOFA) score-based ventilator allocation guidelines during the COVID-19 pandemic.
A retrospective cohort study design was used. Study sites included 3 New York City hospitals in a single academic medical center. We included a random sample (205) of adult patients who were intubated (1002) from March 25, 2020, till April 29, 2020. Protocol criteria adapted from the New York State’s 2015 guidelines were applied to determine which patients would have had mechanical ventilation withheld or withdrawn.
117 (57%) patients would have been identified for ventilator withdrawal or withholding based on the triage guidelines. Of those 117 patients, 28 (24%) survived hospitalization. Overall, 65 (32%) patients survived to discharge.
Triage protocols aim to maximize survival by redirecting ventilators to those most likely to survive. Over 50% of this sample would have been identified as candidates for ventilator exclusion. Clinical judgment would therefore still be needed in ventilator reallocation, thus re-introducing bias and moral distress. This data suggests limited utility for SOFA score-based ventilator rationing. It raises the question of whether there is sufficient ethical justification to impose a life-ending decision based on a SOFA scoring method on some patients in order to offer potential benefit to a modest number of others.
Climate change has affected the geographical distributions of most species worldwide; in particular, insects of economic importance inhabiting tropical regions have been impacted. Current and future predictions of change in geographic distribution are frequently included in species distribution models (SDMs). The potential spatial distributions of the fruit fly Anastrepha striata Schiner, the main species of agricultural importance in guava crops, under current and possible future scenarios in Colombia were modeled, and the establishment risk was assessed for each guava-producing municipality in the country. SDMs were developed using 221 geographical records in conjunction with nine scenopoetic variables. The model for current climate conditions indicated an extensive suitable area for the establishment of A. striata in the Andean region, smaller areas in the Caribbean and Pacific, and almost no areas in the Orinoquia and Amazonian regions. A brief discussion regarding the area's suitability for the fly is offered. According to the results, altitude is one of the main factors that direct the distribution of A. striata in the tropics. The Colombian guava-producing municipalities were classified according to the degree of vulnerability to fly establishment as follows: 42 were high risk, 16 were intermediate risk, and 17 were low risk. The implementation of future integrated management plans must include optimal spatial data and must consider environmental aspects, such as those suggested by the models presented here. Control decisions should aim to mitigate the positive relationship between global warming and the increase in the dispersal area of the fruit fly.
Recent progress has been made in quantifying snowmelt in the Himalaya. Although the conditions are favorable for refreezing, little is known about the spatial variability of meltwater refreezing, hindering a complete understanding of seasonal snowmelt dynamics. This study aims to improve our understanding about how refreezing varies in space and time. We simulated refreezing with the seNorge (v2.0) snow model for the Langtang catchment, Nepalese Himalaya, covering a 5-year period. Meteorological forcing data were derived from a unique elaborate network of meteorological stations and high-resolution meteorological simulations. The results show that the annual catchment average refreezing amounts to 122 mm w.e. (21% of the melt), and varies strongly in space depending on elevation and aspect. In addition, there is a seasonal altitudinal variability related to air temperature and snow depth, with most refreezing during the early melt season. Substantial intra-annual variability resulted from fluctuations in snowfall. Daily refreezing simulations decreased by 84% (annual catchment average of 19 mm w.e.) compared to hourly simulations, emphasizing the importance of using sub-daily time steps to capture melt–refreeze cycles. Climate sensitivity experiments revealed that refreezing is highly sensitive to changes in air temperature as a 2°C increase leads to a refreezing decrease of 35%.
Chapter 7 presents the soil carbon cycle. The chapter largely by-passes the still uncertain processes that occur at the molecular scale. The focus is on macroscopic properties and how they vary with space and time. Soil C storage is first examined from a box model perspective, which introduces mass balance equations and how they are useful, when coupled with data, in beginning to understanding soil C dynamics. The chapter includes an introductory perspective on the vertical trends in soil C and the transport-reaction models that are needed to fully explain these patterns. Soil organic C is largely removed from soil as CO2, and production-diffusion models are introduced to explain observable CO2 depth profiles and to calculate the fluxes to the atmosphere. Diffusion impacts the C isotope composition of soil CO2 and any CaCO3 minerals that subsequently form. These are examined through the lens of diffusion modeling, which is now common, and critical, in any examination of soil properties with depth.
Understand common scheduling as well as other advanced operational problems with this valuable reference from a recognized leader in the field. Beginning with basic principles and an overview of linear and mixed-integer programming, this unified treatment introduces the fundamental ideas underpinning most modeling approaches, and will allow you to easily develop your own models. With more than 150 figures, the basic concepts and ideas behind the development of different approaches are clearly illustrated. Addresses a wide range of problems arising in diverse industrial sectors, from oil and gas to fine chemicals, and from commodity chemicals to food manufacturing. A perfect resource for engineering and computer science students, researchers working in the area, and industrial practitioners.
Chaapter 5 covers the impact of experience and past behavior on attitude and behavior change. In addition to experience, the influence of past behavior can be due to biased scanning, cognitive dissonance, and self-perception. Biased scanning entails forming attitudes on the basis of thoughts about specific information associated with the behavior. Cognitive dissonance entails a conflict between one’s attitude and one’s behavior and can lead to changing the attitude to resolve the inconsistency. Self-perception theory entails inferring one’s attitude from a past behavior that is salient at the time. These processes and supporting researach are described.
Research on purpose is aimed at identifying the perceptual variables that a system is controlling when it is carrying out various purposeful behaviors. The basic methodology used to do this kind of research is the test for controlled variable or TCV, which involves testing hypotheses about the perceptual variables the organism is controlling. These hypotheses are formal definitions of the perception under control. They are tested by looking for lack of an expected effect of disturbances to the hypothesized controlled variable. The TCV has to start with the formulation of hypotheses about the variables around which behavior is organized. The researcher has to be able to look at what an organism is doing and come up with definitions of the variables it is controlling – definitions that are precise enough so that it is possible to predict how disturbances would affect these variables if they were not being controlled.
Robots of next-generation physically interact with the world rather than be caged in a controlled area, and they need to make contact with the open-ended environment to perform their task. Compliant robot links offer intrinsic mechanical compliance for addressing the safety issue for physical human–robot interactions (pHRI). However, many important research questions are yet to be answered. For instance, how do system parameters, for example, mechanical compliance, motor torque, impact velocities, and so on, affect the impact force? how to formulate system impact dynamics of compliant robots, and how to size their geometric dimensions to maximize impact force reduction. In this paper, we present a parametric study of compliant link (CL) design for safe pHRI. We first present a theoretical model of the pHRI system that is comprised of robot dynamics, an impact contact model, and dummy head dynamics. After experimentally validating the theoretical model, we then systematically study the effects of CL parameters on the impact force in more detail. Specifically, we explore how the design and actuation parameters affect the impact force of pHRI system. Based on the parametric studies of the CL design, we propose a step-by-step process and a list of concrete guidelines for designing CL with safety constraints in pHRI. We further conduct a simulation case study to validate this design process and design guidelines.
This chapter is a guideline of the whole book. We highlight the importance of the research here from historical perspective and current policy relevance. We position this project in the literature and justify the research here. We also outline the exposition structure of subsequent chapters.
When punishment is immediate, firm, and accompanied by a clear (and fair) explanation, and when it occurs in a variety of settings, it can be very effective in eliminating undesirable behavior. However, punishment can also produce undesireable side effects. One is conditioning fear. If children are punished for poor schoolwork, for example, this can create a dislike of the subject and of school; the resulting anxiety can also interfere with learning. Another possible side-effect is aggression. Aggression is an innate reaction to painful stimuli, and we can also learn to imitate aggressive models—if parents control behavior with physical force, children can learn that force is an appropriate way to get others to do what you want. Research has shown that spanking or other forms of painful punishment can substantially increase aggression, and seeing aggression on TV or films can have similar effects. Alternatives to corporal punishment include extinction, time-out, response cost, and reinforcing good behavior instead of punishing bad behavior.
A set of embedded atom model (EAM) interatomic potentials was developed to represent highly idealized face-centered cubic (FCC) mixtures of Fe–Ni–Cr–Co–Al at near-equiatomic compositions. Potential functions for the transition metals and their crossed interactions are taken from our previous work for Fe–Ni–Cr–Co–Cu [D. Farkas and A. Caro: J. Mater. Res. 33 (19), 3218–3225, 2018], while cross-pair interactions involving Al were developed using a mix of the component pair functions fitted to known intermetallic properties. The resulting heats of mixing of all binary equiatomic random FCC mixtures not containing Al is low, but significant short-range ordering appears in those containing Al, driven by a large atomic size difference. The potentials are utilized to predict the relative stability of FCC quinary mixtures, as well as ordered L12 and B2 phases as a function of Al content. These predictions are in qualitative agreement with experiments. This interatomic potential set is developed to resemble but not model precisely the properties of this complex system, aiming at providing a tool to explore the consequences of the addition of a large size-misfit component into a high entropy mixture that develops multiphase microstructures.