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.

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.

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%.

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.

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.