Non-Cooperative Behavior Management in Large-Scale Group Decision-Making Considering the Altruistic Behaviors of Experts and Its Application in Emergency Alternative Selection

Emergency decision-making problems usually involve many experts with different professional backgrounds and concerns,leading to non-cooperative behaviors during the consensus-reaching process.Many studies on noncooperative behavior management assumed that the maximumdegree of cooperation of experts is to totally accept the revisions suggested by the moderator,which restricted individuals with altruistic behaviors to make more contributions in the agreement-reaching process.In addition,when grouping a large group into subgroups by clustering methods,existing studies were based on the similarity of evaluation values or trust relationships among experts separately but did not consider them simultaneously.In this study,we introduce a clustering method considering the similarity of evaluation values and the trust relations of experts and then develop a consensusmodel taking into account the altruistic behaviors of experts.First,we cluster experts into subgroups by a constrained Kmeans clustering algorithm according to the opinion similarity and trust relationship of experts.Then,we calculate the weights of experts and clusters based on the centrality degrees of experts.Next,to enhance the quality of consensus reaching,we identify three kinds of non-cooperative behaviors and propose corresponding feedback mechanisms relying on the altruistic behaviors of experts.A numerical example is given to show the effectiveness and practicality of the proposed method in emergency decision-making.The study finds that integrating altruistic behavior analysis in group decision-making can safeguard the interests of experts and ensure the integrity of decision-making information.

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.

On the role of impulsivity and decision-making in suicidal behavior

Suicide risk constitutes a complex set of interacting demographic, clinical, psychobiological and environmental variables. Impulsivity is a long-known risk factor for suicide attempts. However, research based on clearer conceptual refinement in this area is imperative. One emerging field of study is that of decision-making. Impulsivity involves a failure of higher-order control, including decision-making. Using standardized operational definitions that take into consideration relevant aspects of impulsivity, including state- and trait-components and a deeper understanding of the process of decision-making in the suicidal mind, we may come a step closer to understanding suicidality and winning the fight in this scourge of human suffering.

COLLISION AVOIDANCE DECISION-MAKING MODEL OF MULTI-AGENTS IN VIRTUAL DRIVING ENVIRONMENT WITH ANALYTIC HIERARCHY PROCESS

Collision avoidance decision-making models of multiple agents in virtual driving environment are studied. Based on the behavioral characteristics and hierarchical structure of the collision avoidance decision-making in real life driving, delphi approach and mathematical statistics method are introduced to construct pair-wise comparison judgment matrix of collision avoidance decision choices to each collision situation. Analytic hierarchy process （AHP） is adopted to establish the agents＇ collision avoidance decision-making model. To simulate drivers＇ characteristics, driver factors are added to categorize driving modes into impatient mode, normal mode, and the cautious mode. The results show that this model can simulate human＇s thinking process, and the agents in the virtual environment can deal with collision situations and make decisions to avoid collisions without intervention. The model can also reflect diversity and uncertainly of real life driving behaviors, and solves the multi-objective, multi-choice ranking priority problem in multi-vehicle collision scenarios. This collision avoidance model of multi-agents model is feasible and effective, and can provide richer and closer-to-life virtual scene for driving simulator, reflecting real-life traffic environment more truly, this model can also promote the practicality of driving simulator.

SUBJECTIVE RADE-OFF RATE METHOD OF MULTIOBJECTIVE DECISION-MAKING

This paper proposes a mathod of subjective trade-off rate which describles decision-maker's preferince in multiobjective decision-making. Decision-maker can arbitrarity determine his subjective trade-off rate,but it is not sure to be effective.The paper finds aft effective upper bound of subjective trade-off rate,which is the KuhnThcker multiplier of some mathematical programming.For the anbjective trade-off rate not being larger than the upper bonnd,the solving method and properties of the optimal solution corresponding tile trade-off rate are discussed.The paper lastly develops the process of solving multiobjective decision-making with the subjective trade-off rate method.

Neural mechanism of proposer's decision-making in the ultimatum and dictator games

Previous studies have demonstrated that reactions to unfair offers in the ultimatum game are correlated with negative emotion. However, little is known about the difference in neural activity between a proposer＇s decision-making in the ultimatum game compared with the dictator game. The present functional magnetic resonance imaging study revealed that proposing fair offers in the dictator game elicited greater activation in the right supramarginal gyrus, right medial frontal gyrus and left anterior cingulate cortex compared with proposing fair offers in the ultimatum game in 23 Chinese undergraduate and graduate students from Beijing Normal University in China. However, greater activation was found in the right superior temporal gyrus and left cingulate gyrus for the reverse contrast. ＂The results indicate that proposing fair offers in the dictator game is more strongly associated with cognitive control and conflicting information processing compared with proposing fair offers in the ultimatum game.

Modeling heterogeneous behaviors with different strategies in a terrorist attack

Existing simulations of terrorist attacks do not consider individual variations.To overcome this lim-itation,we propose a framework to model heterogeneous behavior of individuals during terrorist attacks.We constructed an emotional model that integrated personality and visual perception for pedestrians.The emotional model was then integrated with pedestrian relationship networks to establish a decision-making model that sup-ported pedestrians’altruistic behaviors.A mapping model has been developed to correlate antisocial personality traits with attack strategies employed by terrorists.Experiments demonstrate that the proposed algorithm can generate practical heterogeneous behaviors that align with existing psychological research findings.

A Theoretical Approach to the Effective Decision-Making Process

This research develops and elaborates studies done for a contribution to the 2019 PIC International Conference 2019 in Malta, about the decision-making process. Decision-making is the act of choosing between two or more courses of action. In the wider process of problem-solving, decision-making involves choosing between possible solutions to a problem, and these decisions can be made through either an intuitive or reasoned process, or a combination of the two. The study of decision-making processes, to be understood as the role of human factors, becomes particularly interesting in complex organizations. This research aims to analyze how an effective team, within organizations, can develop a more correct and effective decision-making, in order to get an optimal solution, overcoming the typical uncertainty. The paper describes the point of departure of decision in complex, time-pressured, uncertain, ambiguous and changing environments. The use of a leading case (the Tenerife air accident, 1977), will lead us to the desired results, i.e. to demonstrate how an effective decisional process, including team dynamics, can be useful to reduce the risk, present in all decisions, and reduce errors. The case of Tenerife air disaster, confirm our research. In that case, in fact, the group dynamics prove not to have worked. Thus, we can state that if a team approach had been followed instead of a more individual one, the results would probably have been different. The central belief of the research, is that classic decision theory could benefit from a team approach, which reduces the risk that a decision may lead to undesirable consequences. As demonstrated with the case study, within organizations, the decision-making is not a solitary action. Decisions, in fact, are made within a team and in order to be able to function effectively in a group, and manage group situations, there are essential skills. The team can then become a resource for the decisional process and problem solving, but it is necessary to understand the dynamics.

数智时代人机协同的研究现状与未来方向

数智时代因人机协同情境的普遍存在而出现新的决策范式。本专栏旨在促进学者研究与人机协同中的管理决策与组织行为有关的重要问题。尽管在20世纪人工智能的起始阶段学者注意到人与机器共同决策的重要问题,但因人工智能技术在很长时间内没有取得重要突破而没有被继续关注。伴随着人工智能的广泛应用以及认知科学在过去几十年取得的长足进步,学界研究人机协同中的决策和行为问题有了丰富的数据、场景、理论和方法。同时,实践界对于人机协同的新现象还存在认识上的不足和适应性困难,阻碍了企业和组织成员借助数智技术实现智能增强。本文总结了在商业与管理、计算机和心理学领域的学者所从事的有关人们对于机器算法的信任或依赖的研究,特别介绍了在企业情境下开展的人机协同研究。在简要介绍本专栏收录的七篇文章的主要研究发现后,我们指出了未来人机协同研究的重要方向,旨在倡导学界同行开展深入的研究,既能够建立数智化情境下的人机协同理论,又为中国企业顺利实现数智化转型提供参考。

校园足球政策执行利益主体决策行为的演化博弈研究

校园足球政策执行涉及利益主体众多,均衡各方主体利益是校园足球政策有效执行的关键所在.从利益视角出发,构建地方校足办、学校和家长三方利益主体的动态演化博弈模型,以Matlab为工具仿真分析三方主体决策行为的演化路径和演化规律.结果表明:地方校足办、学校、家长三方初始策略概率相互影响,不同概率取值对三方主体向理想状态收敛速度产生差异化影响,当三方达到较高水平的初始概率时,动态系统更易达到理想稳定状态;地方校足办的收益水平对系统趋于理想状态具有正向作用,收益越高,系统收敛于理想稳定状态的速度越快.为促使三方策略选择趋向利益均衡状态,从加大理念价值传播、提高三方参与意愿、建立利益表达机制、降低信息不对称程度、完善执行考核机制、构建监督评价体系等方面提出相应的政策建议.

基于前因因素的建筑工人安全行为评价模型与应用

建筑项目中的事故主要与建筑工人的不安全行为有关,因此对建筑工人安全行为进行事前评价是预防事故发生的关键。首先通过文献可视化分析初步筛选建筑工人安全行为前因因素,结合专家意见构建建筑工人安全行为评价的指标体系;然后采用群决策改进的层次分析法确定各评价指标的综合权重,并建立可拓物元评价模型;最后对某施工项目建筑工人安全行为进行评价与分析,以验证模型的合理性和可靠性。结果表明:建筑工人安全行为评价指标中安全意识所占权重较高,是事故预防的重要因素;实例应用结果表明该项目建筑工人总体安全行为等级处于“Ⅲ级”为一般安全,说明该项目施工作业人员整体处于比较安全的水平;建筑工人个体安全行为的维度指标评价结果为正态分布或偏正态分布,表明该评价模型能够有效识别建筑工人的安全行为水平,可为建筑安全管理的研究和实践提供参考。

作品演绎行为的决策机制构建

著作权法调整原作品著作权人与演绎作者基于作品演绎行为产生的法律关系,通过改变原作品产生新的独创性表达,以实现著作权制度鼓励作品创作和传播的基本目标。演绎行为的规则适用直接决定了作品演绎行为的法律性质,而演绎行为的合法性直接影响原作品著作权人与演绎作者的决策选择。帕累托最优的经济学标准要求制度规则的设计最大化规则适用的收益,整体上减少规则适用的成本。作品演绎行为的决策机制构建应当完善规则共识形成的制度性规范,以明确的规则与稳定的预期促进行为主体在决策过程中实现帕累托最优结果。

基于深度强化学习的无信号灯路口决策研究

无信号灯左转路口是自动驾驶场景中最为危险的场景之一,如何实现高效安全的左转决策是自动驾驶领域的重大难题。深度强化学习(DRL)算法在自动驾驶决策领域具有广阔应用前景。但是,深度强化学习在自动驾驶场景中存在样本效率低、奖励函数设计困难等问题。提出一种基于专家先验的深度强化学习算法(CBAMBC SAC)来解决上述问题。首先,利用SMARTS仿真平台获得专家先验知识;然后,使用通道-空间注意力机制(CBAM)改进行为克隆(BC)方法,在专家先验知识的基础上预训练模仿专家策略;最后,使用模仿专家策略指导深度强化学习算法的学习过程,并在无信号灯路口左转决策中进行验证。实验结果表明,基于专家先验的DRL算法比传统的DRL算法更具优势,不仅可以免去人为设置奖励函数的工作量,而且可以显著提高样本效率从而获得更优性能。在无信号灯路口左转场景下,CBAM-BC SAC算法与传统DRL算法(SAC)、基于传统行为克隆的DRL算法(BC SAC)相比,平均通行成功率分别提高了14.2和2.2个百分点。

高校毕业生未来时间洞察力对求职行为的影响——基于职业规划能力和职业决策自我效能感的作用机制

以天津市高校毕业生为例,实证分析了未来时间洞察力、职业决策自我效能感、职业规划能力与毕业生求职行为的关系。研究表明:高校毕业生的未来时间洞察力、职业决策自我效能感、职业规划能力、求职行为之间两两显著正相关。高校毕业生未来时间洞察力可以直接预测求职行为,也可以通过职业决策自我效能感的中介作用间接影响求职行为,职业规划能力在其中有负向调节作用。

景区分时预约策略下的旅游线路优化方法

为提升游客的旅游体验和景区的服务水平,探讨分时预约策略下游客游览决策行为,分析游客拥挤-调适行为对游线设计的影响及其机理.考虑游客分时预约时段选择下入园时间和游览总时间的变化、可游览景点数等约束,以游客总体拥挤感知最小、游线总体满意度最大和景点间总体走行时间最小为优化目标,构建分时预约策略下的多目标景区路线优化模型,并以颐和园为例进行实证和线路优度评价.结果表明颐和园上午预约时段(06:00—12:00)与下午预约时段(12:00—19:00)的推荐游线优度值分别为75.09、69.00,均高于颐和园推荐游线,表明优化模型精度较高.研究可为合理制定景区分时预约等客流调控措施、旅游线路优化设计、均衡景区客流的时空分布提供支撑.

WebService Behavior技术及其应用研究

WebService Behavior是一种新的Web Services访问技术。在分析Web Services技术的基础上,从配属WebService Behav-ior、定位Web Services、Behavior对Web Services方法的调用及返回结果处理等方面深入研究了WebService Behavior调用原理。讨论了WebService Behavior技术在Web页面数据动态刷新、Web页面动画制作等方面的应用。

基于深度强化学习的智能车辆行为决策研究

自动驾驶车辆决策系统直接影响车辆综合行驶性能,是实现自动驾驶技术需要解决的关键难题之一。基于深度强化学习算法DDPG(deep deterministic policy gradient),针对此问题提出了一种端到端驾驶行为决策模型。首先,结合驾驶员模型选取自车、道路、干扰车辆等共64维度状态空间信息作为输入数据集对决策模型进行训练,决策模型输出合理的驾驶行为以及控制量,为解决训练测试中的奖励和控制量突变问题,改进DDPG决策模型对决策控制效果进行优化,并在TORCS(the open racing car simulator)平台进行仿真实验验证。结果表明:所提出的决策模型可以根据车辆和环境实时状态信息输出合理的驾驶行为以及控制量,与DDPG模型相比,改进的模型具有更好的控制精度,且车辆横向速度显著减小,车辆舒适性以及车辆稳定性明显改善。

基于实验经济学方法的不同支付方式对医生决策行为影响研究

目的探索医生在不同医保支付方式下及面对不同健康状况患者时提供医疗服务行为的变化。方法招募110名医学院校学生作为被试者,通过实验经济学方法模拟医生进行医疗行为决策场景。结果被试者在按服务项目支付(fee-for-service,FFS)中为每名患者提供的医疗服务平均数量高于按人头支付(capitation payment,CAP);被试者在混合支付方式下提供的服务数情况优于单一支付方式;FFS系统中对重症患者的最优决策比例最高,而在CAP系统中对轻症和重症患者作出的最优决策数差异无统计学意义(P>0.05);不同支付方式、医生利润及患者健康状况对被试者提供的最优决策存在影响。结论在FFS系统中,轻症患者接受了更多的过度医疗服务;在CAP系统中,中症和重症患者治疗不足的情况更严重;混合支付方式能够一定程度克服单一支付方式的弊端,发挥综合优势。被试者在提供医疗行为的过程中存在利他异质性,可以通过改变外部客观条件和优化内生性因素对医生决策行为进行干预。

共享决策护理在冠心病PCI术后患者中的应用效果及对机体状态的影响

目的探讨共享决策护理在冠心病经皮冠状动脉介入治疗(PCI)术后患者中的应用效果及对机体状态的影响。方法选取2021年7月至2022年7月接受PCI治疗的80例冠心病患者为研究对象,运用随机数字表法将其分为对照组、观察组,各40例。对照组接受常规护理,观察组接受共享决策护理。比较两组的健康行为、依从性及机体状态。结果护理后,观察组的健康行为量表(HPL)各维度评分均高于对照组,差异具有统计学意义(P<0.05)。护理后,观察组的通用依从性量表(GAS)各维度评分均高于对照组,差异具有统计学意义(P<0.05)。护理后,观察组的西雅图心绞痛量表(SAQ)各维度评分均高于对照组,差异具有统计学意义(P<0.05)。结论共享决策护理可提升冠心病PCI术后患者的依从性,提升健康行为改善效果,进而改善机体状态。