Evolutionary Decision-Making and Planning for Autonomous Driving Based on Safe and Rational Exploration and Exploitation

Decision-making and motion planning are extremely important in autonomous driving to ensure safe driving in a real-world environment.This study proposes an online evolutionary decision-making and motion planning framework for autonomous driving based on a hybrid data-and model-driven method.First,a data-driven decision-making module based on deep reinforcement learning(DRL)is developed to pursue a rational driving performance as much as possible.Then,model predictive control(MPC)is employed to execute both longitudinal and lateral motion planning tasks.Multiple constraints are defined according to the vehicle’s physical limit to meet the driving task requirements.Finally,two principles of safety and rationality for the self-evolution of autonomous driving are proposed.A motion envelope is established and embedded into a rational exploration and exploitation scheme,which filters out unreasonable experiences by masking unsafe actions so as to collect high-quality training data for the DRL agent.Experiments with a high-fidelity vehicle model and MATLAB/Simulink co-simulation environment are conducted,and the results show that the proposed online-evolution framework is able to generate safer,more rational,and more efficient driving action in a real-world environment.

Toward Trustworthy Decision-Making for Autonomous Vehicles:A Robust Reinforcement Learning Approach with Safety Guarantees

While autonomous vehicles are vital components of intelligent transportation systems,ensuring the trustworthiness of decision-making remains a substantial challenge in realizing autonomous driving.Therefore,we present a novel robust reinforcement learning approach with safety guarantees to attain trustworthy decision-making for autonomous vehicles.The proposed technique ensures decision trustworthiness in terms of policy robustness and collision safety.Specifically,an adversary model is learned online to simulate the worst-case uncertainty by approximating the optimal adversarial perturbations on the observed states and environmental dynamics.In addition,an adversarial robust actor-critic algorithm is developed to enable the agent to learn robust policies against perturbations in observations and dynamics.Moreover,we devise a safety mask to guarantee the collision safety of the autonomous driving agent during both the training and testing processes using an interpretable knowledge model known as the Responsibility-Sensitive Safety Model.Finally,the proposed approach is evaluated through both simulations and experiments.These results indicate that the autonomous driving agent can make trustworthy decisions and drastically reduce the number of collisions through robust safety policies.

Orientation and Decision-Making for Soccer Based on Sports Analytics and AI:A Systematic Review

Due to ever-growing soccer data collection approaches and progressing artificial intelligence(AI) methods, soccer analysis, evaluation, and decision-making have received increasing interest from not only the professional sports analytics realm but also the academic AI research community. AI brings gamechanging approaches for soccer analytics where soccer has been a typical benchmark for AI research. The combination has been an emerging topic. In this paper, soccer match analytics are taken as a complete observation-orientation-decision-action(OODA) loop.In addition, as in AI frameworks such as that for reinforcement learning, interacting with a virtual environment enables an evolving model. Therefore, both soccer analytics in the real world and virtual domains are discussed. With the intersection of the OODA loop and the real-virtual domains, available soccer data, including event and tracking data, and diverse orientation and decisionmaking models for both real-world and virtual soccer matches are comprehensively reviewed. Finally, some promising directions in this interdisciplinary area are pointed out. It is claimed that paradigms for both professional sports analytics and AI research could be combined. Moreover, it is quite promising to bridge the gap between the real and virtual domains for soccer match analysis and decision-making.

Ethical Decision-Making Framework Based on Incremental ILP Considering Conflicts

Humans are experiencing the inclusion of artificial agents in their lives,such as unmanned vehicles,service robots,voice assistants,and intelligent medical care.If the artificial agents cannot align with social values or make ethical decisions,they may not meet the expectations of humans.Traditionally,an ethical decision-making framework is constructed by rule-based or statistical approaches.In this paper,we propose an ethical decision-making framework based on incremental ILP(Inductive Logic Programming),which can overcome the brittleness of rule-based approaches and little interpretability of statistical approaches.As the current incremental ILP makes it difficult to solve conflicts,we propose a novel ethical decision-making framework considering conflicts in this paper,which adopts our proposed incremental ILP system.The framework consists of two processes:the learning process and the deduction process.The first process records bottom clauses with their score functions and learns rules guided by the entailment and the score function.The second process obtains an ethical decision based on the rules.In an ethical scenario about chatbots for teenagers’mental health,we verify that our framework can learn ethical rules and make ethical decisions.Besides,we extract incremental ILP from the framework and compare it with the state-of-the-art ILP systems based on ASP(Answer Set Programming)focusing on conflict resolution.The results of comparisons show that our proposed system can generate better-quality rules than most other systems.

Stroke Risk Assessment Decision-Making Using a Machine Learning Model:Logistic-AdaBoost

Stroke is a chronic cerebrovascular disease that carries a high risk.Stroke risk assessment is of great significance in preventing,reversing and reducing the spread and the health hazards caused by stroke.Aiming to objectively predict and identify strokes,this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost(Logistic-AB)based on machine learning.First,the categorical boosting(CatBoost)method is used to perform feature selection for all features of stroke,and 8 main features are selected to form a new index evaluation system to predict the risk of stroke.Second,the borderline synthetic minority oversampling technique(SMOTE)algorithm is applied to transform the unbalanced stroke dataset into a balanced dataset.Finally,the stroke risk assessment decision-makingmodel Logistic-AB is constructed,and the overall prediction performance of this new model is evaluated by comparing it with ten other similar models.The comparison results show that the new model proposed in this paper performs better than the two single algorithms(logistic regression and AdaBoost)on the four indicators of recall,precision,F1 score,and accuracy,and the overall performance of the proposed model is better than that of common machine learning algorithms.The Logistic-AB model presented in this paper can more accurately predict patients’stroke risk.

Determinants of Tobacco Cultivation and the Associated Impacts of Adoption in Hurungwe District, Zimbabwe

Tobacco is an essential cash crop in Zimbabwe and a strategic livelihood option for hundreds of thousands of rural households. However, the crop is linked to negative environmental, economic, and social impacts. The existing studies on tobacco cultivation in Zimbabwe present contradictory findings on the determinants and impacts of adoption, leaving unanswered questions about the crop’s sustainability impact in the country. This article investigates the determinants of smallholder farmers’ decisions to grow tobacco and the associated impacts of adoption. Random and purposive sampling were used to select 273 household surveys, including tobacco and non-tobacco smallholder farmers, and 56 expert interviews to answer the research questions. We employed regression models alongside expert interviews and document analysis to identify the determinants influencing the decision-making process of smallholder farmers in Zimbabwe regarding tobacco cultivation. Additionally, our investigation aimed to elucidate the perceived impacts associated with the adoption of this agricultural practice. The regression analysis indicated that the farmer’s age, education level, farming experience, family size, household income, and perceived high farm profitability are significant drivers of tobacco adoption. We also discovered divergent and convergent perceptions of the critical impacts of tobacco cultivation. The study highlights the need for proactive multi-stakeholder collaboration and sustainable financial arrangements to address the negative impacts of tobacco production. As the primary stakeholder responsible for regulating and promoting agricultural activities, the Zimbabwean government should provide meaningful financial support, increase access to credit, and ensure better market facilities for alternative crops to reduce the over-dependence on tobacco.

Assessing Factors Influencing Solar Energy Adoption among Small and Medium Enterprises in Mzuzu City, Malawi

One of the many renewable energy sources that offer advantages is solar energy, which also lowers energy prices and promotes environmental sustainability and energy security. Despite these advantages, various barriers, such as installation costs, have prevented small and medium-sized enterprises from investigating this invention. Malawi has a significant energy shortfall such that most businesses have been hindered from their profit maximization goals. The “Photovoltaic systems” (PV) that transform sunlight into electricity are the subject of this study. This type of solar energy system is situated on the building’s roof and generally produces electricity for businesses and even homes. Solar energy offers a great impact to small and medium enterprises in Mzuzu city with a cost-effective and dependable alternative to energy that has the potential to change the game. Therefore the aim of the study was to identify factors that encourage the adoption of solar energy among small medium enterprises in the city of Mzuzu city. And to identify some of barriers faced when adopting solar energy among small and medium enterprises in the city of Mzuzu. The research approach employed in the study was a survey. A survey is a type of research methodology in which primary data is gathered from a sample using a questionnaire. When information is to be gathered from a wider sample, a survey is employed. A bigger sample size was needed in this study in order to facilitate hypothesis testing. It is advised to apply a logical approach while using the survey. The survey utilized a five-point Likert scale. The study used convenience sampling to select study participants. The sample size in this study was determined using Cochran’s sample size formula. Statistical Package for Social Sciences (SPSS) and Microsoft Excel were used for statistical analysis. About 97.2% of the participants were aware of solar as a source of energy compared to 2.8 % who were unaware. The majority of participants use solar energy for lighting only, seconded by those who use electricity. The least number of participants use solar energy for cooling only. The majority of participants 21.5% indicated partnership and collaboration as the most motivating factor for the adoption of solar energy. This was followed by technical expertise 19.1 % the least number of participants 10.8% expressed that policy and regulatory frameworks were associated with the adoption of solar energy. This study found that there are no statistically significant factors influencing barriers to the adoption of solar energy. The price of solar energy adoption was identified as the least factor associated with the acceptance or rejection of solar energy. Nonetheless, the reasons given by the homes that had embraced solar technology aligned with the findings of other studies. This survey also found that although the public was aware of solar energy, and technology, there were still a number of factors that mattered, especially for non-adopters.

The Spherical q-Linear Diophantine Fuzzy Multiple-Criteria Group Decision-Making Based on Differential Measure

Spherical q-linearDiophantine fuzzy sets(Sq-LDFSs)provedmore effective for handling uncertainty and vagueness in multi-criteria decision-making(MADM).It does not only cover the data in two variable parameters but is also beneficial for three parametric data.By Pythagorean fuzzy sets,the difference is calculated only between two parameters(membership and non-membership).According to human thoughts,fuzzy data can be found in three parameters(membership uncertainty,and non-membership).So,to make a compromise decision,comparing Sq-LDFSs is essential.Existing measures of different fuzzy sets do,however,can have several flaws that can lead to counterintuitive results.For instance,they treat any increase or decrease in the membership degree as the same as the non-membership degree because the uncertainty does not change,even though each parameter has a different implication.In the Sq-LDFSs comparison,this research develops the differentialmeasure(DFM).Themain goal of the DFM is to cover the unfair arguments that come from treating different types of FSs opposing criteria equally.Due to their relative positions in the attribute space and the similarity of their membership and non-membership degrees,two Sq-LDFSs formthis preference connectionwhen the uncertainty remains same in both sets.According to the degree of superiority or inferiority,two Sq-LDFSs are shown as identical,equivalent,superior,or inferior over one another.The suggested DFM’s fundamental characteristics are provided.Based on the newly developed DFM,a unique approach tomultiple criterion group decision-making is offered.Our suggestedmethod verifies the novel way of calculating the expert weights for Sq-LDFSS as in PFSs.Our proposed technique in three parameters is applied to evaluate solid-state drives and choose the optimum photovoltaic cell in two applications by taking uncertainty parameter zero.The method’s applicability and validity shown by the findings are contrasted with those obtained using various other existing approaches.To assess its stability and usefulness,a sensitivity analysis is done.

A NovelMethod for Determining Tourism Carrying Capacity in a Decision-Making Context Using q−Rung Orthopair Fuzzy Hypersoft Environment

Tourism is a popular activity that allows individuals to escape their daily routines and explore new destinations for various reasons,including leisure,pleasure,or business.A recent study has proposed a unique mathematical concept called a q−Rung orthopair fuzzy hypersoft set(q−ROFHS)to enhance the formal representation of human thought processes and evaluate tourism carrying capacity.This approach can capture the imprecision and ambiguity often present in human perception.With the advanced mathematical tools in this field,the study has also incorporated the Einstein aggregation operator and score function into the q−ROFHS values to supportmultiattribute decision-making algorithms.By implementing this technique,effective plans can be developed for social and economic development while avoiding detrimental effects such as overcrowding or environmental damage caused by tourism.A case study of selected tourism carrying capacity will demonstrate the proposed methodology.

UAV maneuvering decision-making algorithm based on deep reinforcement learning under the guidance of expert experience

Autonomous umanned aerial vehicle(UAV) manipulation is necessary for the defense department to execute tactical missions given by commanders in the future unmanned battlefield. A large amount of research has been devoted to improving the autonomous decision-making ability of UAV in an interactive environment, where finding the optimal maneuvering decisionmaking policy became one of the key issues for enabling the intelligence of UAV. In this paper, we propose a maneuvering decision-making algorithm for autonomous air-delivery based on deep reinforcement learning under the guidance of expert experience. Specifically, we refine the guidance towards area and guidance towards specific point tasks for the air-delivery process based on the traditional air-to-surface fire control methods.Moreover, we construct the UAV maneuvering decision-making model based on Markov decision processes(MDPs). Specifically, we present a reward shaping method for the guidance towards area and guidance towards specific point tasks using potential-based function and expert-guided advice. The proposed algorithm could accelerate the convergence of the maneuvering decision-making policy and increase the stability of the policy in terms of the output during the later stage of training process. The effectiveness of the proposed maneuvering decision-making policy is illustrated by the curves of training parameters and extensive experimental results for testing the trained policy.

The Influence of Information Technology(IT)on Accrual Accounting Adoption:The Case of the Jordanian Public Sector

Jordan is one of many countries that intends to reform its public sector by adopting accrual accounting,which is primarily carried out through the International Public Sector Accounting Standards(IPSAS)implementation plan,scheduled to be completed by 2021.Considering the difficulties of adoption,this process cannot be completed without an appropriate Information Technology(IT)system capable of providing the accounting information required for this reform.Therefore,this study aims to examine the influence of IT on accrual accounting adoption in the Jordanian public sector.The study employed the quantitative survey approach.Based on 331 usable questionnaires,the descriptive findings showed that the use of IT in the Jordanian public sector is relevant to daily accounting transactions in general and to accrual accounting in particular.The regression analysis showed a positive,significant relationship between IT use and accrual accounting practice.The IT specialist should be able to understand the needs of public sector in adopting accrual-based accounting.In line with today’s digitalization age,which integrates advanced technologies and numerous techniques,this study suggests that a combination of accounting and IT experts is a value added to sharpen the competitiveness of public sector reform.This study contributes to the extant literature in public sector,concerning the relationship between IT and accrual accounting adoption in the context of Jordan.

Decision-Making and Management of Self-Care in Persons with Traumatic Spinal Cord Injuries: A Preliminary Study

Patients and physicians understand the importance of self-care following spinal cord injury (SCI), yet many individuals with SCI do not adhere to recommended self-care activities despite logistical supports. Neurobehavioral determinants of SCI self-care behavior, such as impulsivity, are not widely studied, yet understanding them could inform efforts to improve SCI self-care. We explored associations between impulsivity and self-care in an observational study of 35 US adults age 18 - 50 who had traumatic SCI with paraplegia at least six months before assessment. The primary outcome measure was self-reported self-care. In LASSO regression models that included all neurobehavioral measures and demographics as predictors of self-care, dispositional measures of greater impulsivity (negative urgency, lack of premeditation, lack of perseverance), and reduced mindfulness were associated with reduced self-care. Outcome (magnitude) sensitivity, a latent decision-making parameter derived from computationally modeling successive choices in a gambling task, was also associated with self-care behavior. These results are preliminary;more research is needed to demonstrate the utility of these findings in clinical settings. Information about associations between impulsivity and poor self-care in people with SCI could guide the development of interventions to improve SCI self-care and help patients with elevated risks related to self-care and secondary health conditions.

Research on Public Engineering Emergency Decision-Making Based on Multi-Granularity Language Information

To effectively deal with fuzzy and uncertain information in public engineering emergencies,an emergency decision-making method based on multi-granularity language information is proposed.Firstly,decision makers select the appropriate language phrase set according to their own situation,give the preference information of the weight of each key indicator,and then transform the multi-granularity language information through consistency.On this basis,the sequential optimization technology of the approximately ideal scheme is introduced to obtain the weight coefficient of each key indicator.Subsequently,the weighted average operator is used to aggregate the preference information of each alternative scheme with the relative importance of decision-makers and the weight of key indicators in sequence,and the comprehensive evaluation value of each scheme is obtained to determine the optimal scheme.Lastly,the effectiveness and practicability of the method are verified by taking the earthwork collapse accident in the construction of a reservoir as an example.

A Blind Spot in the Reframing of a Universe of Possibles: Towards a Suitable Model for Decision-Making Theory and A.I.

Bayesian inference model is an optimal processing of incomplete information that, more than other models, better captures the way in which any decision-maker learns and updates his degree of rational beliefs about possible states of nature, in order to make a better judgment while taking new evidence into account. Such a scientific model proposed for the general theory of decision-making, like all others in general, whether in statistics, economics, operations research, A.I., data science or applied mathematics, regardless of whether they are time-dependent, have in common a theoretical basis that is axiomatized by relying on related concepts of a universe of possibles, especially the so-called universe (or the world), the state of nature (or the state of the world), when formulated explicitly. The issue of where to stand as an observer or a decision-maker to reframe such a universe of possibles together with a partition structure of knowledge (i.e. semantic formalisms), including a copy of itself as it was initially while generalizing it, is not addressed. Memory being the substratum, whether human or artificial, wherein everything stands, to date, even the theoretical possibility of such an operation of self-inclusion is prohibited by pure mathematics. We make this blind spot come to light through a counter-example (namely Archimedes’ Eureka experiment) and explore novel theoretical foundations, fitting better with a quantum form than with fuzzy modeling, to deal with more than a reference universe of possibles. This could open up a new path of investigation for the general theory of decision-making, as well as for Artificial Intelligence, often considered as the science of the imitation of human abilities, while being also the science of knowledge representation and the science of concept formation and reasoning.

Crossing the Achilles Heel of Algorithms:Identifying the Developmental Dilemma of Artificial Intelligence-Assisted Judicial Decision-Making

In the developmental dilemma of artificial intelligence(AI)-assisted judicial decision-making,the technical architecture of AI determines its inherent lack of transparency and interpretability,which is challenging to fundamentally improve.This can be considered a true challenge in the realm of AI-assisted judicial decision-making.By examining the court’s acceptance,integration,and trade-offs of AI technology embedded in the judicial field,the exploration of potential conflicts,interactions,and even mutual shaping between the two will not only reshape their conceptual connotations and intellectual boundaries but also strengthen the cognition and re-interpretation of the basic principles and core values of the judicial trial system.

四手操作下ADOPT模式在老年急性牙髓炎治疗中的应用效果评估

目的评估四手操作下ADOPT模式在老年急性牙髓炎治疗中的应用效果。方法采用随机抽样法,选取2022年1—12月南京大学医学院附属口腔医院收治的80例老年急性牙髓炎病人作为研究对象,按就诊时间分为研究组和对照组,每组40例。研究组进行四手操作下ADOPT模式治疗,对照组进行常规四手操作治疗。分别采用Frankl量表、正性和负性情绪量表(PANAS)、一般自我效能量表(GSES)、护理满意度问卷调查表评定2组病人的治疗依从性、正负情绪、自我效能以及护理满意度并比较。结果研究组的依从率为80.0%,显著高于对照组的30.0%;研究组的正性情绪得分为(31.32±1.05)分,显著高于对照组的(26.95±1.05)分;干预后研究组病人的自我效能评分为(37.27±1.79)分,显著高于对照组的(21.15±1.02)分;研究组的护理满意度为97.5%,显著高于对照组的72.5%,差异均有统计学意义(P<0.05)。结论四手操作下ADOPT模式应用于老年急性牙髓炎诊疗过程中可以显著提高病人的治疗依从性、正性情绪和自我效能,以及病人对护理工作的满意度。

ADOPT问题解决模式护理对大面积烧伤患者创面愈合及心理状态的影响

目的:探究ADOPT问题解决模式护理对大面积烧伤患者伤残接受度、创面愈合及美观满意度的影响。方法:前瞻性选择2020年1月-2022年12月笔者医院收治的患者460例大面积烧伤患者为研究对象,将2020年1月-2021年3月收治的患者纳入对照组,2021年4月-2022年12月收治的纳入观察组,对照组采用大面积烧伤后常规护理,观察组采用ADOPT问题解决模式护理,持续护理3个月。分别于干预前和干预3个月后,采用伤残接受度量表(Acceptance of disability scale,AODS)评估患者伤残接受度,采用视觉模拟评分(Visual analogue scale,VAS)评估患者创面疼痛程度,采用简明烧伤健康量表(Burn specific health scale,BSHS-A)评估患者生活质量;干预4周后,计算创面愈合率;干预3个月后,采用(Vancouver scar scale,VSS)评估患者创面愈合后瘢痕增生情况,采用美观满意度评分表评估患者美观满意度;记录创面愈合时间。结果:干预后,观察组患者AODS评分、创面覆盖率、美观满意度、BSHS-A评分均明显高于对照组,创面愈合时间明显短于对照组,VAS评分和VSS评分明显低于对照组(P<0.05)。结论:ADOPT问题解决模式能够提高大面积烧伤患者伤残接受度和美观满意度,有利于创面愈合,患者生活质量也得以显著提高。

基于ADOPT模式的团体心理干预对老年直肠癌患者自我管理能力、生活质量及希望水平的影响

目的 探讨基于ADOPT模式的团体心理干预对老年直肠癌患者自我管理能力、生活质量及希望水平的影响。方法 依据干预方法的不同将200例老年直肠癌患者分为观察组(n=109)和常规组(n=91),常规组患者给予常规干预,观察组患者给予基于ADOPT模式的团体心理干预。比较两组患者的造口适应情况[造口适应量表(OAS)]、自我管理能力、负性情绪[焦虑自评量表(SAS)和抑郁自评量表(SDS)]、生活质量[欧洲癌症研究与治疗组织生命质量测定量表(EORTC QLQ-C30)]、希望水平[Herth希望量表(HHI)]和满意度。结果 出院时和出院后6个月,两组患者OAS量表各维度评分和总分均高于本组术后1天,且观察组患者OAS量表各维度评分和总分均高于常规组,差异均有统计学意义(P﹤0.05)。出院时,两组患者SAS、SDS评分均低于本组术后1天,肠造口病人自我管理能力问卷、EORTC QLQ-C30、HHI量表各维度评分均高于本组术后1天,观察组患者SAS、SDS评分均低于常规组,肠造口病人自我管理能力问卷、EORTC QLQ-C30、HHI量表各维度评分均高于常规组,差异均有统计学意义(P﹤0.05)。观察组患者的总满意度为98.17%,明显高于常规组患者的89.01%,差异有统计学意义(P﹤0.01)。结论 基于ADOPT模式的团体心理干预可明显缓解老年直肠癌患者的负性情绪,强化患者适应情况和自我管理能力,提高生活质量、希望水平和满意度。

Artificial intelligence promotes shared decision-making through recommending tests to febrile pediatric outpatients

BACKGROUND:To promote the shared decision-making(SDM)between patients and doctors in pediatric outpatient departments,this study was designed to validate artificial intelligence(AI)-initiated medical tests for children with fever.METHODS:We designed an AI model,named Xiaoyi,to suggest necessary tests for a febrile child before visiting a pediatric outpatient clinic.We calculated the sensitivity,specificity,and F1 score to evaluate the efficacy of Xiaoyi’s recommendations.The patients were divided into the rejection and acceptance groups.Then we analyzed the rejected examination items in order to obtain the corresponding reasons.RESULTS:We recruited a total of 11,867 children with fever who had used Xiaoyi in outpatient clinics.The recommended examinations given by Xiaoyi for 10,636(89.6%)patients were qualified.The average F1 score reached 0.94.A total of 58.4%of the patients accepted Xiaoyi’s suggestions(acceptance group),and 41.6%refused(rejection group).Imaging examinations were rejected by most patients(46.7%).The tests being time-consuming were rejected by 2,133 patients(43.2%),including rejecting pathogen studies in 1,347 patients(68.5%)and image studies in 732 patients(31.8%).The difficulty of sampling was the main reason for rejecting routine tests(41.9%).CONCLUSION:Our model has high accuracy and acceptability in recommending medical tests to febrile pediatric patients,and is worth promoting in facilitating SDM.

Farmers’precision pesticide technology adoption and its influencing factors:Evidence from apple production areas in China

The research aimed to understand farmers’willingness to adopt(WTA)and willingness to pay(WTP)for precision pesticide technologies and analyzed the determinants of farmers’decision-making.We used a two-stage approach to consider farmers’WTA and WTP for precision pesticide technologies.A survey of 545 apple farmers was administered in Bohai Bay and the Loess Plateau in China.The data were analyzed using the double-hurdle model.The results indicated that 78.72%of respondents were willing to apply precision pesticide technologies provided by service organizations such as cooperatives and dedicated enterprises,and 69.72%were willing to buy the equipment for using precision pesticide technologies.The results of the determinant analysis indicated that farmers’perceived perceptions,farm scale,cooperative membership,access to digital information,and availability of financial services had significant and positive impacts on farmers’WTA precision pesticide technologies.Cooperative membership,technical training,and adherence to environmental regulations increased farmers’WTP for precision pesticide technologies.Moreover,nonlinear relationships between age,agricultural experience,and farmers’WTA and WTP for precision pesticide technology services were found.