Group decision-making in animals

Groups of animals often need to make communal decisions, for example about which activities to perform, when to perform them and which direction to travel in; however, little is known about how they do so. Here, we model the fitness consequences of two possible decision-making mechanisms: 'despotism' and 'democracy'. We show that under most conditions, the costs to subordinate group members, and to the group as a whole, are considerably higher for despotic than for democratic decisions. Even when the despot is the most experienced group member, it only pays other members to accept its decision when group size is small and the difference in information is large. Democratic decisions are more beneficial primarily because they tend to produce less extreme decisions, rather than because each individual has an influence on the decision per se. Our model suggests that democracy should be widespread and makes quantitative, testable predictions about group decision-making in non-humans.     

软件可靠性多模型综合评估

软件可靠性工程领域一直存在模型很难应用于实际的问题，不同的软件可靠性模型对同一个软件工程项目的估计差别极大，为此，探讨了软件可靠性估计的稳健方法，研究了多模型的贝叶斯自动混合与选择的理论，其重点是要探讨贝叶斯先验表达及多模型的混合机制，以解决上述难题。     

Representation of a perceptual decision in developing oculomotor commands

Behaviour often depends on the ability to make categorical judgements about sensory information acquired over time. Such judgements require a comparison of the evidence favouring the alternatives, but how the brain forms these comparisons is unknown. Here we show that in a visual discrimination task, the accumulating balance of sensory evidence favouring one interpretation over another is evident in the neural circuits that generate the behavioural response. We trained monkeys to make a direction judgement about dynamic random-dot motions and to indicate their judgement with an eye movement to a visual target. We interrupted motion viewing with electrical microstimulation of the frontal eye field and analysed the resulting, evoked eye movements for evidence of ongoing activity associated with the oculomotor response. Evoked eye movements deviated in the direction of the monkey's judgement. The magnitude of the deviation depended on motion strength and viewing time. The oculomotor signals responsible for these deviations reflected the accumulated motion information that informed the monkey's choices on the discrimination task. Thus, for this task, decision formation and motor preparation appear to share a common level of neural organization.     

A general mechanism for perceptual decision-making in the human brain

Findings from single-cell recording studies suggest that a comparison of the outputs of different pools of selectively tuned lower-level sensory neurons may be a general mechanism by which higher-level brain regions compute perceptual decisions. For example, when monkeys must decide whether a noisy field of dots is moving upward or downward, a decision can be formed by computing the difference in responses between lower-level neurons sensitive to upward motion and those sensitive to downward motion. Here we use functional magnetic resonance imaging and a categorization task in which subjects decide whether an image presented is a face or a house to test whether a similar mechanism is also at work for more complex decisions in the human brain and, if so, where in the brain this computation might be performed. Activity within the left dorsolateral prefrontal cortex is greater during easy decisions than during difficult decisions, covaries with the difference signal between face- and house-selective regions in the ventral temporal cortex, and predicts behavioural performance in the categorization task. These findings show that even for complex object categories, the comparison of the outputs of different pools of selectively tuned neurons could be a general mechanism by which the human brain computes perceptual decisions.     

基于不完整数据的粗糙集电网故障诊断方法

针对电网发生故障时,可能产生的保护装置或断路器拒动、误动以及通信装置传输缺陷所造成的数据不完整情况,提出了一种基于粗糙集理论的新约简算法,并将约简结果综合为一个统一的专家库表·同时应用模糊集和概率论理论,为每条规则加入了相应规则置信度和设备置信度·对于符合的结论,根据支持某一决策的规则数目以及每条规则的置信度,提出了综合分析置信度的算法,并将其应用于电网故障诊断中·运用VB语言编程实现了对故障算例决策表的约简,并通过具体算例测试,证明了该约简算法的有效性和实用性·     

Cortical substrates for exploratory decisions in humans

Decision making in an uncertain environment poses a conflict between the opposing demands of gathering and exploiting information. In a classic illustration of this 'exploration-exploitation' dilemma, a gambler choosing between multiple slot machines balances the desire to select what seems, on the basis of accumulated experience, the richest option, against the desire to choose a less familiar option that might turn out more advantageous (and thereby provide information for improving future decisions). Far from representing idle curiosity, such exploration is often critical for organisms to discover how best to harvest resources such as food and water. In appetitive choice, substantial experimental evidence, underpinned by computational reinforcement learning (RL) theory, indicates that a dopaminergic, striatal and medial prefrontal network mediates learning to exploit. In contrast, although exploration has been well studied from both theoretical and ethological perspectives, its neural substrates are much less clear. Here we show, in a gambling task, that human subjects' choices can be characterized by a computationally well-regarded strategy for addressing the explore/exploit dilemma. Furthermore, using this characterization to classify decisions as exploratory or exploitative, we employ functional magnetic resonance imaging to show that the frontopolar cortex and intraparietal sulcus are preferentially active during exploratory decisions. In contrast, regions of striatum and ventromedial prefrontal cortex exhibit activity characteristic of an involvement in value-based exploitative decision making. The results suggest a model of action selection under uncertainty that involves switching between exploratory and exploitative behavioural modes, and provide a computationally precise characterization of the contribution of key decision-related brain systems to each of these functions.     

粗糙集在决策系统中的应用

介绍了粗糙集理论实现数据分类和规则推理的基本原理，对关系数据库中的二维决策表提出了一个问题解决模型，并在同时满足支持度和可信度的基础上对规则进行筛选，提高了决策的准确性和合理性。     

Effective leadership and decision-making in animal groups on the move

For animals that forage or travel in groups, making movement decisions often depends on social interactions among group members. However, in many cases, few individuals have pertinent information, such as knowledge about the location of a food source, or of a migration route. Using a simple model we show how information can be transferred within groups both without signalling and when group members do not know which individuals, if any, have information. We reveal that the larger the group the smaller the proportion of informed individuals needed to guide the group, and that only a very small proportion of informed individuals is required to achieve great accuracy. We also demonstrate how groups can make consensus decisions, even though informed individuals do not know whether they are in a majority or minority, how the quality of their information compares with that of others, or even whether there are any other informed individuals. Our model provides new insights into the mechanisms of effective leadership and decision-making in biological systems.     

Knowledge Discovery for Event Series Decision Based on Rough Set

To make decisions about event series is part of our life, and to discover knowledge from these decisions is of great significance in the field of controlling and decision-making. The paper takes event series as the exterior form of movements with the dynamic attributes, and gets the Markov transition probabilities matrix to express those attributes with statistics. First, according to the matrix, the decision table is constructed. Then, by reducing attributes based on rough set theory, the decision table is reduced, and the decision rules are acquired as well. Finally we make the decision through defining rule distance and taking the minimum rule distance as decision principle. Followed is an example, which proves that the algorithm is feasible and effective to the event series decision.     

Bayesian integration in sensorimotor learning

When we learn a new motor skill, such as playing an approaching tennis ball, both our sensors and the task possess variability. Our sensors provide imperfect information about the ball's velocity, so we can only estimate it. Combining information from multiple modalities can reduce the error in this estimate. On a longer time scale, not all velocities are a priori equally probable, and over the course of a match there will be a probability distribution of velocities. According to bayesian theory, an optimal estimate results from combining information about the distribution of velocities-the prior-with evidence from sensory feedback. As uncertainty increases, when playing in fog or at dusk, the system should increasingly rely on prior knowledge. To use a bayesian strategy, the brain would need to represent the prior distribution and the level of uncertainty in the sensory feedback. Here we control the statistical variations of a new sensorimotor task and manipulate the uncertainty of the sensory feedback. We show that subjects internally represent both the statistical distribution of the task and their sensory uncertainty, combining them in a manner consistent with a performance-optimizing bayesian process. The central nervous system therefore employs probabilistic models during sensorimotor learning.     

基于不确定性多属性决策方法的网络论坛危机信息传播决策研究

摘要：
网络论坛危机信息传播决策作为应急管理的重要分支，其决策方法的研究具备重要的理论与现实价值.目前国内对于其决策方法的研究上缺乏有效的定量研究手段.提出了基于多属性不确定性决策的论坛危机信息传播决策的定量研究方法，并利用算例数据对于多决策者主体、决策属性权重完全未知的多属性研究算例进行了评估.研究结论表明本文的研究方法可以有效的作用于论坛危机信息传播的决策过程. 关键词：
网络论坛； 应急管理； 不确定多属性决策 中图分类号： G 206.3
文献标志码： A     

企业信息技术更新的战略决策

针对不确定环境中,竞争状态下企业的信息技术更新的战略决策问题,运用时间博弈的理论与方法,建立了双头垄断竞争状态下,企业信息技术更新的三种战略决策模型,即开放式、反馈式和竞争式战略决策模型。讨论了上述三模型的均衡解和解存在的条件与假设,以及其适用范围和应用。同时,上述战略决策方式对于研究新技术的扩散与转移等问题,也具有突出的理论意义和实际价值。     

基于价值的制造企业协同AMT研发决策

针对先进制造技术(AMT)研发的不确定性及其研发价值,分析制造企业间决策的协同对企业AMT研发的影响.以合作企业独立AMT研发和下游制造企业协同AMT研发两种决策模式为研究对象,建立制造企业间协同研发决策的经济博弈模型,比较分析了这两种决策模式下企业AMT研发过程.研究结果表明,合作企业的AMT研发能够通过下游制造企业的协同决策获得技术价值的增长,这种价值的增长表现为两个方面:一方面由于下游制造企业的协同将使得合作企业确定更大的AMT研发努力水平;另外一方面将会增加所有合作企业的AMT研发绩效.     

考虑多种损坏构成特征的沥青路面预防性养护决策方法

由于沥青路面损坏构成的多样性, 相同的路面状况指数(pavement condition index, PCI)可能代表不同的损坏组合. 当多种损坏并存且损坏程度接近时, 用PCI和主导损坏(最严重、扣分最多的路面损坏)难以得到具有针对性的养护对策. 因此, 通过对PCI的深入分析, 明确了主导损坏代表性不足的路段, 以现行预防性养护决策方法为基础, 补充了一种考虑损坏构成特征、更具针对性的决策方法. 以上海城市道路近5年的检测、养护数据为分析基础, 首先利用有序聚类算法将路段按PCI水平分组, 分析了不同阶段路面损坏构成和差异水平; 然后, 针对多种损坏并存且损坏差异不显著的路段, 根据预防性养护的实施效果筛选了能够反映正确预防性养护经验的有效养护路段; 最后, 基于有效养护路段建立并对比分析了2个基于BP(back propagation)神经网络的养护决策模型. 结果表明: 当PCI水平介于优良(84.4~93.0分)时, 不同损坏程度接近, 主导损坏代表性不足; 考虑多种损坏构成特征的BP神经网络模型表现出更高的决策精度, 测试集决策正确率达86.20%, 优于仅考虑主导损坏的模型(58.50%). BP神经网络与传统决策树法结合能够优化沥青路面决策过程, 提高养护对策选取的针对性.     

任务背景下腹侧注意功能网络的fMRI研究

基于静息状态的功能磁共振成像(functional MRI,fMRI)已经成为当前人脑功能研究的重要手段之一,本研究采用任务背景来获取一种更为"纯净"的静息状态,其中脑区的"任务背景"被定义为那些不会激活感兴趣脑区的任务.由于到目前为止还不清楚在"任务背景"下人脑除默认网络外的其他"高级"功能网络内部是否被中断,本文研究了人脑听觉背景下腹侧注意网络内部的功能连接.结果表明在听觉背景下该网络内部存在显著的功能连接,这一结果说明简单的感觉任务不会中断人脑的腹侧注意网络.本研究首次证明在任务背景下,除默认网络之外,人脑还有一些高级功能网络内部存在显著功能连接.     

考虑主体失望情绪的分布语言自信群决策方法

针对分布语言自信评价信息下的多属性群决策问题,提出了一种考虑主体失望情绪的行为多属性群决策方法。首先,考虑实际群决策过程因为决策者的背景、经验、知识等原因会对自身给出的分布语言评价信息有着不同水平的自信程度(这反映了分布语言评价信息的可靠性),定义了一种新的评价结构,即分布语言自信评价;其次,在分布语言自信评价下,考虑有限理性的决策主体,其失望情绪常隐含于决策判断或评价中,利用失望理论,将主体失望情绪融入决策过程,同时,获得分布语言自信评价下的主体感知效用,将主体分布语言自信决策矩阵转化为主体自信效用决策矩阵;然后,定义自信效用向量Dice相似测度和加权Dice相似测度,结合属性的不兼容性程度构建优化模型确定属性权重;接着,基于TOPSIS思想对备选方案进行择优排序;最后,在循环经济背景下评估生态工业园区的决策算例,说明该方法的可行性与有效性。     

绿灯充足时段交通违法监控对车速及行为决策影响分析

为了解信号交叉口处绿灯充足时段交通违法监控是否对机动车车速及驾驶人驾驶行为决策产生影响,运用录像法,分别对经过青岛市黄岛区2类4个信号交叉口绿灯充足时段的机动车进行调查。采集了车辆经过交叉口的车速、加速度、所在车道等具体数据,对经过2类信号交叉口的车速及行为决策进行对比分析;并运用logistic回归模型建立信号交叉口驾驶行为决策模型。结果表明在绿灯充足时段,在装有交通违法监控的信号交叉口,进入交叉口前的机动车车速较低,且离散程度较小;进入装有监控的信号交叉口时,驾驶人采取匀速行为决策的比例比无监控情况高22.78%;较减速行为决策相比,加速和匀速行为决策与交通违法监控、车速及所在车道有关。     

基于决策偏好距离的多阶段群决策快速集结方法

针对具有互反判断矩阵偏好信息的多阶段群决策问题,文章提出了一种基于决策偏好距离的快速集结方法。首先对决策偏好距离相关概念进行定义,阐述了基于决策偏好距离的多阶段群决策集结过程;其次,利用决策偏好距离算法对单一专家的阶段内权重和阶段间权重进行测度,得出最优的专家权重;然后,通过加权几何平均算子拟合出最优的专家判断矩阵和最优的群体决策偏好;最后,给出算例,分析结果表明该方法具有合理性。     

安全风险决策阶段性模型构建:神经科学探索

决策者的主观认知心理与决策行为密切相关，外在行为受内在的大脑决策支配，故明晰安全风险决策与安全/不安全行为之间的关系是有效控制不安全行为的关键。通过文献分析梳理了安全风险研究的主要领域、内容和方法以及安全模型构建存在的问题。在此基础上，以风险信息的思维过程为主线，将风险决策过程分为风险感知、风险认知和风险决策3个阶段，阐明了各阶段的内涵、特点、研究内容和分析路径，构建了3阶段相互衔接的安全模型。同时，明确了事件相关电位（Event-related Potentials, ERPs）和功能性磁共振成像（functional Magnetic Resonance Imaging, fMRI）两类神经科学技术在安全风险决策过程研究中的应用意义。所构建的风险感知-认知-决策的三阶段安全风险决策模型（SR-PCD模型），有利于进一步研究风险决策过程中的个体风险处理机制、明晰风险决策与行为的关系，对安全风险机理研究、事故管控、安全管理等具有理论和实际意义，可为未来安全风险研究提供可参考的实验研究框架。     

考虑不确定性因素影响的污水处理工艺选择

针对不确定性多属性决策优选法在应用中存在的如何将灰数科学白化的问题,建议将多位专家意见以区间数的形式给出,利用满意区间上的专家满意的上下阈,得到所需的规范值,建立综合决策矩阵.以小区域污水处理设施的选择为例,结合小区域污水处理厂拥有多目标与目标矛盾性、稳定性和不确定性的特点,通过专家意见规范化处理,利用综合决策矩阵比较,最终获得问题的最优解.实验证明,改进的不确定性多属性决策方法既省去了烦琐的计算步骤,又降低了决策者在决策过程中承担的风险,具有较高的实用价值.