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BACKGROUND: Genetic improvement of crop varieties requires significant investment. Therefore, varieties must be developed to suit a broad range of breeding targets, such as yield and suitability to rainfall zones, farm management practices and quality traits. In the case of breeding for disease resistance, breeders need to consider the value of genetic improvement relative to other disease management strategies and the dynamics of pathogen genetic and phenotypic diversity. This study uses a benefit-cost analysis framework to assess the economic value of fungicide management and crop genetic improvement in disease resistance for Australian chickpea varieties. RESULTS: When assessing the likelihood of growers switching to new crop varieties with improved genetic resistance to disease, the simulation results reveal that adopting these varieties yielded higher net benefit values compared to implementing current fungicide strategies across all rainfall zones. On average, the increase in net benefit varied between 2.6% and 3.5%. Conversely, when we examined the scenario involving modifying the current fungicide strategy, we observed that, on average, switching from the current fungicide management strategy to one which involved additional fungicides was beneficial in about 73% of the cases. CONCLUSION: Our analysis reveals the importance of factors such as commodity prices, production costs, disease-related variables and risk aversion in determining the economic benefits of adopting new crop protection strategies. Furthermore, the research reveals the need for accessible information and reliable data sources when evaluating the benefits of new agricultural technologies. This would assist growers in making informed and sustainable disease management decisions. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

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Hyperbolic relations between independent and dependent variables are ubiquitous in the experimental analysis of behavior, mentioned in over 150 articles in the Journal of the Experimental Analysis of Behavior. There are two principal forms of hyperbolae: The first describes the relation between response rate and reinforcement rate on variable-interval schedules of reinforcement; it rises asymptotically toward a maximum. The second describes the relation between the current equivalent value of an incentive and its delay or (im)probability; it falls from a maximum toward an asymptote of 0. Where do these come from? What do their parameters mean? How are they related? This article answers the first two questions and addresses the last.

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The Chinese government is implementing policies, such as the "Guidance on comprehensive utilization of bulk solid waste for the 14th Five-Year Plan period", to stimulate phosphogypsum (PG) reduction and recycling. Thus, the comprehensive evaluation of PG recycling technologies for sustainable development is crucial. This study proposes a novel multi-criteria decision analysis (MCDA) method that considers the criteria of resources, environment, economy, and society and risk attitudes of decision-makers and integrates game theory (GT) and utility theory for criteria weighting and ranking to assess industrial-scale PG recycling technologies in China. The results demonstrate that GT provides more reasonable criteria weights than individual weighting methods. PG-based lightweight plaster is the top performer in the resource and environmental dimensions owing to its exceptional resource and energy efficiency. PG utilized for dry-mix mortar and organic fertilizer production exhibited the best utility performance of 0.74 and 0.73, respectively. Measures, such as subsidies and product publicity, should be implemented to promote these technologies. However, technologies with poor performance, such as PG used for the co-production of sulfuric acid and fertilizer or cement, may require optimization or substitution for the sustainable recycling of PG. The proposed MCDA method is robust and can serve as a reliable decision-making tool for other waste-recycling technologies. However, caution must be exercised when determining risk attitude using the MCDA method as it may vary with the number of technologies and affect the final rankings.

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Charitable giving involves a complex economic and social decision because the giver expends resources for goods or services they will never receive. Although psychologists have identified numerous factors that influence charitable giving, there currently exists no unifying computational model of charitable choice. Here, we submit one such model, based within the strictures of Psychological Value Theory (PVT). In four experiments, we assess whether charitable giving is driven by the perceived Psychological Value of the recipient. Across all four experiments, we simultaneously predict response choice and response time with high accuracy. In a fifth experiment, we show that PVT predicts charitable giving more accurately than an account based on competence and warmth. PVT accurately predicts which charity a respondent will choose to donate to and separately, whether a respondent will choose to donate at all. PVT models the cognitive processes underlying charitable donations and it provides a computational framework for integrating known influences on charitable giving. For example, we show that in-group preference influences charitable giving by changing the Psychological Values of the options, rather than by bringing about a response bias toward the in-group.

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Whether to undergo genome sequencing in a clinical or research context is generally a voluntary choice. Individuals are often motivated to learn genomic information even when clinical utility-the possibility that the test could inform medical recommendations or health outcomes-is low or absent. Motivations to seek one's genomic information can be cognitive, affective, social, or mixed (e.g., cognitive and affective) in nature. These motivations are based on the perceived value of the information, specifically, its clinical utility and personal utility. We suggest that motivations to learn genomic information are no different from motivations to learn other types of personal information, including one's health status and disease risk. Here, we review behavioral science relevant to motivations that may drive engagement with genome sequencing, both in the presence of varying degrees of clinical utility and in the absence of clinical utility. Specifically, we elucidate 10 motivations that are expected to underlie decisions to undergo genome sequencing. Recognizing these motivations to learn genomic information will guide future research and ultimately help clinicians to facilitate informed decision making among individuals as genome sequencing becomes increasingly available.

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The personalization of autonomous vehicles or advanced driver assistance systems has been a widely researched topic, with many proposals aiming to achieve human-like or driver-imitating methods. However, these approaches rely on an implicit assumption that all drivers prefer the vehicle to drive like themselves, which may not hold true for all drivers. To address this issue, this study proposes an online personalized preference learning method (OPPLM) that utilizes a pairwise comparison group preference query and the Bayesian approach. The proposed OPPLM adopts a two-layer hierarchical structure model based on utility theory to represent driver preferences on the trajectory. To improve the accuracy of learning, the uncertainty of driver query answers is modeled. In addition, informative query and greedy query selection methods are used to improve learning speed. To determine when the driver's preferred trajectory has been found, a convergence criterion is proposed. To evaluate the effectiveness of the OPPLM, a user study is conducted to learn the driver's preferred trajectory in the curve of the lane centering control (LCC) system. The results show that the OPPLM can converge quickly, requiring only about 11 queries on average. Moreover, it accurately learned the driver's favorite trajectory, and the estimated utility of the driver preference model is highly consistent with the subject evaluation score.

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Constructed wetlands have recently started to be applied as the most important type of nature-based solution against the effects of climate change. This study investigates the determination of the most suitable site determination criteria for the application of this important nature-based solution tool by multiple decision-making methods. For this purpose, first of all, the literature was reviewed and the ten most important criteria for constructed wastelands were determined. Then, fieldwork was carried out according to these determined criteria, and a location was determined in the field according to each criterion. The global positioning system device is used to mark 10 locations that have been determined as waypoints for 10 criteria. The determined waypoints were then scored using the relevant criteria, and the best location was determined using the Multiple Attribute Utility Theory methods. Waypoint 1 received the highest score, 8.4, according to the results. Later, waypoint 7 received a score of 6.2, and waypoint 9 received a score of 5.7.

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This study examined the socio-economic factors influencing choice of climate change adaptation practices and the effects of these practices on cassava productivity in Nigeria. Using a multi-stage sampling technique, structured questionnaire was used to survey 100 cassava farmers. The result was analyzed with a multivariate probit and generalized linear regression models. The result showed male dominance (78%) in cassava farming and the mean age of the cassava farmers was 45.46 ± 9.36 years. About 66% of the farmers belonged to cooperative associations and 67% had access to credit facilities. The multivariate model revealed that age of farmers, gender, education qualification, primary occupation, total income, membership of cooperative associations, farming objectives, farming experience, access to extension visit, access to credit, type of land ownership, farm size and climatic conditions significantly influenced choice of climate change adaptation practices among cassava farmers. The generalized linear model identified farming system, multiple crop types/improved crop varieties used, crop diversification, organic manuring, multiple planting dates, use of alternate fallowing, education and credit access to significantly affect cassava productivity. The study concluded that, eco-friendly methods for adapting to climate change increase cassava productivity. Thus, cassava farmers should be trained on the use of best climate change adaptation practices that can boost cassava productivity. In order to practice climate smart farming, it is important to stress the usage of organic manure and alternate fallowing.

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The debilitating symptoms of Parkinson's disease, including the hallmark slowness of movement, termed bradykinesia, were described more than 100 years ago. Despite significant advances in elucidating the genetic, molecular and neurobiological changes in Parkinson's disease, it remains conceptually unclear exactly why patients with Parkinson's disease move slowly. To address this, we summarize behavioural observations of movement slowness in Parkinson's disease and discuss these findings in a behavioural framework of optimal control. In this framework, agents optimize the time it takes to gather and harvest rewards by adapting their movement vigour according to the reward that is at stake and the effort that needs to be expended. Thus, slow movements can be favourable when the reward is deemed unappealing or the movement very costly. While reduced reward sensitivity, which makes patients less inclined to work for reward, has been reported in Parkinson's disease, this appears to be related mainly to motivational deficits (apathy) rather than bradykinesia. Increased effort sensitivity has been proposed to underlie movement slowness in Parkinson's disease. However, careful behavioural observations of bradykinesia are inconsistent with abnormal computations of effort costs due to accuracy constraints or movement energetic expenditure. These inconsistencies can be resolved when considering that a general disability to switch between stable and dynamic movement states can contribute to an abnormal composite effort cost related to movement in Parkinson's disease. This can account for paradoxical observations such as the abnormally slow relaxation of isometric contractions or difficulties in halting a movement in Parkinson's disease, both of which increase movement energy expenditure. A sound understanding of the abnormal behavioural computations mediating motor impairment in Parkinson's disease will be vital for linking them to their underlying neural dynamics in distributed brain networks and for grounding future experimental studies in well-defined behavioural frameworks.

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Tversky and Kahneman (1981) told participants to imagine they were at a store about to purchase an item. They were asked if they would be willing to drive 20 min to another store to receive a $5 discount on the item's price. Most participants were willing, but only when the original price of the item was small ($15); when the original price was relatively large ($125), most said they would not drive 20 min for a $5 discount. We examined this framing effect in 296 participants, but instead used a psychophysical-adjustment procedure to obtain quantitative estimates of the discount required with different (a) item prices, (b) delays until the item's receipt, and (c) opportunity costs (in "driving" vs. "delivery" tasks). We systematically replicated Tversky and Kahneman's results, but also extended them by showing a substantial influence of opportunity costs on the consumer discounts required. A behavioral model of delay discounting-additive-utility theory-accounted for 97% of the variance in these consumer discounts.

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Rachlin and colleagues laid the groundwork for treating the discounting of probabilistic goods as a variant of the discounting of delayed goods. This approach was seminal for a large body of subsequent research. The present paper finds the original development problematic: In converting probability to delay, the authors incorrectly dropped trial duration. The subsumption of probability by delay is also empirically questionable, as those are different functions of variables such as magnitude of outcome and commodity versus money. A variant of Rachlin's theme treats human discounting studies as psychophysical matching experiments, in which one compound stimulus is adjusted to equal another. It is assumed that a function of amount (its utility) is multiplied by a function of probability (its weight). Conjoint measurement establishes the nature of these functions, yielding a logarithmic transform on amount, and a Prelec function on probability. This model provides a good and parsimonious account of probability discounting in diverse data sets. Variant representations of the data are explored. By inserting the probabilistically discounted utility into the additive utility theory of delay discounting, a general theory of probabilistic intertemporal choice is achieved.

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With the increasing growth of online shopping, businesses are intertwining to establish new shopping antecedents. Customer experience has steadily become the most important source of retailers' long-term competitive advantage via difference. To preserve long-term and sustained consumer loyalty, retailers must continually improve the customer experiences. This study presents a framework for online retailing in a digital environment called the Online Customer Experience-Engagement Context model in the presence of value co-creation (VCC). Data was gathered from 189 people who purchased products online. For additional analysis, testing the hypothesis, and model construction, SPSS 26 and Smart-PLS were used. The data was then evaluated further using descriptive statistics, path analysis, measurement, and structural model. The findings show that the online customer experience (measured as shopping environment, shopping procedure, staff service experience, and product experience) substantially influenced customer engagement, which improved customer loyalty. The VCC strengthened the link between online customer experience and customer engagement. It suggests that including consumers in co-creating a delightful online customer experience from time to time may be a valuable strategy for online retailers to increase customer engagement and loyalty. To create an overarching outcome, information integration theory (IIT), multi-attribute utility theory (MAUT), and the attitude-behavior-context theory (ABC) theories are converged to explain the proposed model in the study.

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In a decision-making problem, there is often some uncertainty regarding the user preferences. We assume a parameterised utility model, where in each scenario we have a utility function over alternatives, and where each scenario represents a possible user preference model consistent with the input preference information. With a set A of alternatives available to the decision-maker, we can consider the associated utility function, expressing, for each scenario, the maximum utility among the alternatives. We consider two main problems: firstly, finding a minimal subset of A that is equivalent to it, i.e., that has the same utility function. We show that for important classes of preference models, the set of possibly strictly optimal alternatives is the unique minimal equivalent subset. Secondly, we consider how to compare A to another set of alternatives B , where A and B correspond to different initial decision choices. This is closely related to the problem of computing setwise max regret. We derive mathematical results that allow different computational techniques for these problems, using linear programming, and especially, with a novel approach using the extreme points of the epigraph of the utility function.

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Mitigating the impacts of COVID-19 comes with the evaluation of tradeoffs. However, the exact magnitude of the tradeoffs being made cannot be known ahead of time. There are three major concerns to balance: life, liberty, and economy. Here, we create a multi-attribute utility function including those three attributes and provide reasonable bounds on the weights of each. No one set of weights on the utility function can be considered "correct." Furthermore, the outcomes of each mitigation strategy are deeply uncertain. Not only do we need to take into account the characteristics of the disease, but we also need to take into account the efficacy of the mitigation strategies and how each outcome would be evaluated. To handle this, we use Robust Decision Making methods to simulate plausible outcomes for various strategies and evaluate those outcomes using different weights on the multi-attribute utility function. The simulation is done with a compartmental epidemiological model combined with a simple economic model and a model of liberty costs. Rather than trying to optimize likely outcomes for a particular version of the utility function, we find which strategies are robust across a wide range of plausible scenarios even when there is disagreement about how to weigh the competing values of life, liberty, and economy.

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Industrial relocation (IR) is a business strategy consisting of moving operations locations. The purpose of this paper is to present how to assess, with multi-attribute decision-making (MADM), alternatives for IR. With MADM, IR strategies can be assessed not only based on a single attribute, as costs, or profits. This paper presents the application of MADM in a real case of IR. Four leading methods of MADM were applied: analytic hierarchy process (AHP), multi-attribute utility theory (MAUT), multi-attribute value theory (MAVT), and technique of order preference by similarity to ideal solution (TOPSIS). Results of AHP, MAUT, MAVT, and TOPSIS were quite similar, indicating the decision for the company not to relocate. A joint comparison of results with compatibility indices and correlation coefficients is the major novelty presented by this paper to the field of Operations Research, known as MADM.

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Healthy young adults have a most preferred walking speed, step length and step width that are close to energetically optimal. However, people can choose to walk with a multitude of different step lengths and widths, which can vary in both energy expenditure and preference. Here, we further investigated step length-width preferences and their relationship to energy expenditure. In line with a growing body of research, we hypothesized that people's preferred stepping patterns would not be fully explained by metabolic energy expenditure. To test this hypothesis, we used a two-alternative forced-choice paradigm. Fifteen participants walked on an oversized treadmill. Each trial, participants performed two prescribed stepping patterns and then chose the pattern they preferred. Over time, we adapted the choices such that there was 50% chance of choosing one pattern over another (equally preferred). If people's preferences are based solely on metabolic energy expenditure, then these equally preferred stepping patterns should have equal energy expenditure. In contrast, we found that energy expenditure differed across equally preferred step length-width patterns (P<0.001). On average, longer steps with higher energy expenditure were preferred over shorter and wider steps with lower energy expenditure (P<0.001). We also asked participants to rank a set of shorter, wider and longer steps from most preferred to least preferred, and from most energy expended to least energy expended. Only 7/15 participants had the same rankings for their preferences and perceived energy expenditure. Our results suggest that energy expenditure is not the only factor influencing a person's conscious gait choices.

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This paper introduces an interactive framework to guide decision-makers in a multi-criteria supplier selection process. State-of-the-art multi-criteria methods for supplier selection elicit the decision-maker's preferences among the criteria by processing pre-collected data from different stakeholders. We propose a different approach where the preferences are elicited through an active learning loop. At each step, the framework optimally solves a combinatorial problem multiple times with different weights assigned to the objectives. Afterwards, a pair of solutions among those computed is selected using a particular query selection strategy, and the decision-maker expresses a preference between them. These two steps are repeated until a specific stopping criterion is satisfied. We also introduce two novel fast query selection strategies, and we compare them with a myopically optimal query selection strategy. Computational experiments on a large set of randomly generated instances are used to examine the performance of our query selection strategies, showing a better computation time and similar performance in terms of the number of queries taken to achieve convergence. Our experimental results also show the usability of the framework for real-world problems with respect to the execution time and the number of loops needed to achieve convergence.

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There has been a growing interest in psychological measurements that use the multiple-alternative forced-choice (MAFC) response format for its resistance to response biases. Although several models have been proposed for the data obtained from such measurements, none have succeeded in incorporating the response time information. Given that currently, many psychological measurements are performed via computers, it would be beneficial to develop a joint model involving an MAFC item response and response time. The present study proposes the first model that combines a cognitive process model that underlies the observed response time and the forced-choice item response model. Specifically, the proposed model is based on the linear ballistic accumulator model of response time, which is substantially extended by reformulating its parameters so as to incorporate the MAFC item responses. The model parameters are estimated by the Markov chain Monte Carlo (MCMC) algorithm. A simulation study confirmed that the proposed approach could appropriately recover the parameters. Two empirical applications are reported to demonstrate the use of the proposed model and compare it with existing models. The results showed that the proposed model could be a useful tool for jointly modeling the MAFC item responses and response times.

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Customer experience is a source of retailers' long-term competitive advantage. This study has examined the relationship between online customer experience and brand love through the mechanism of relationship quality in the context of online shopping in Pakistan. The moderating effect of value co-creation on the relationship of online customer experience with relationship quality and brand love has also been examined. Data were collected from a purposive sampling of 189 online customers in an online survey. Results showed that online customer experience significantly impacts customer relationship quality, which leads to brand love. The relationship between online customer experience and relationship quality is found more robust at high levels of value co-creation. However, we observed a significant negative moderating effect of value co-creation on the direct relationship between online customer experience and brand love. It suggests that including customers in the value co-creation process and affording them a pleasurable online shopping experience may be an excellent way to enhance customer relationship quality and brand love. Theoretical and practical implications of these findings are discussed.

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Aim: The aim of this paper is to develop an understanding of how behavioral theories have influenced the way preferences for health-related quality of life are elicited and interpreted. We focus on the Time Trade-off (TTO) method given it represents the quality-adjusted life-year (QALY) concept-that survival in less-than-full health can be deemed equivalent to a shorter survival in full health. To our knowledge this is the first review using a combination of systematic scoping review, bibliometrics and VOSviewer visualization to map the development of ideas in health economics. Methods: A priori, we selected three behavioral theories to explore within our review, referred to here as Expected Utility Theory, Non-Expected Utility Theory and Probabilistic Choice Theory. A fourth topic, Order Effects, is defined broadly to encompass behavioral theories around timing/sequence of events. For the main search, Scopus was used to identify literature that had (a) elicited TTO values and/or (b) contributed to the way TTO values were elicited and interpreted, from inception to July 2021. Papers that focused on the latter category were given the label "behavioral" and underwent additional analyses. A two stage-screening was applied to assess eligibility. Co-citation, co-authorship and co-occurrence of keywords was used to chart the development of TTO over time. Results: A total of 1,727 records were retrieved from Scopus and were supplemented by an additional 188 papers. There were 856 applied and 280 behavioral papers included in the final corpus, with the behavioral set split equally into four sets of 70 papers to chart the development of keywords over time: (1) 1972-1999; (2) 2000-2010, (3) 2010-2015 and (4) 2015-2021. Discussion: The keyword analysis suggested that whilst some ideas transition quickly from economic theory to the TTO literature, such as the impact of Order Effects, others take longer to be assimilated, for example Non-Expected Utility models or failure of constant discounting. It is therefore important that researchers within health economics work more closely with those in mainstream economics and keep abreast of the wider economics and behavioral sciences to expedite the uptake of new and relevant ideas.