Dynamics of Decision Making in Animal Group Motion

We analyze a continuous-time model of a multi-agent system motivated by simulation studies on dynamics of decision making in animal groups in motion. Each individual moves at constant speed in the plane and adjusts its heading in response to relative headings of others in the population. The population includes two subgroups that are “informed” such that individuals in each subgroup have a preferred direction of motion. The model exhibits fast and slow time scales allowing for a reduction in the dimension of the problem. The stable solutions for the reduced model correspond to compromise by individuals with conflicting preferences. We study the global phase space for the proposed reduced model by computing equilibria and exploring stability and bifurcations. B. Nabet’s and N.E. Leonard’s work is supported in part by AFOSR grant FA9550-07-1-0-0528 and ONR grants N00014-02-1-0826 and N00014-04-1-0534. I.D. Couzin’s work was supported by the Royal Society, Balliol College and EPSRC grants GR/S04765/01 and GR/T11234/01, a Searle Scholar Award and DARPA grant HR001-05-1-0057. S.A. Levin’s work was supported in part by DARPA grant HR0011-05-1-0057 and NSF grant EF-0434319.     

Models and people: An alternative view of the emergent properties of computational models

Computer models can help humans gain insight into the functioning of complex systems. Used for training, they can also help gain insight into the cognitive processes humans use to understand these systems. By influencing humans understanding (and consequent actions) computer models can thus generate an impact on both these actors and the very systems they are designed to simulate. When these systems also include humans, a number of self‐referential relations thus emerge which can lead to very complex dynamics. This is particularly true when we explicitly acknowledge and model the existence of multiple conflicting representations of reality among different individuals. Given the increasing availability of computational devices, the use of computer models to support individual and shared decision making could potentially have implications far wider than the ones often discussed within the Information and Communication Technologies community in terms of computational power and network communication. We discuss some theoretical implications and describe some initial numerical simulations. © 2015 Wiley Periodicals, Inc. Complexity 21: 202–213, 2016     

地下水脆弱性风险预警和评估的区间三角模糊多属性决策模型

应用基于逼近理想解排序法的区间三角模糊多属性决策模型,对三江平原六大分区地下水脆弱性进行了风险预警和评估.评估结果与前人吻合,可为有关决策部门采取相应降低环境风险的措施提供参考.实例验证表明,模型具有更高的计算精度和更好的评价效果,为有关环境风险决策部门对地下水风险预警和评估提供了新的思路和方法.     

基于群体一致性的犹豫模糊多属性决策方法

犹豫模糊集允许一个元素属于一个集合的隶属度可以是多个不同的值,是表达决策者之间偏好不一致性的有力工具。针对决策者评价偏差不宜过大的问题,提出了一种基于群体一致性的犹豫模糊多属性决策方法。首先, 我们定义了犹豫模糊元的犹豫度函数,进而定义了犹豫模糊元的一致性指数;在此基础上,构建了基于群体一致性指数最大化的权重优化模型,通过求解优化模型可以得到属性的权重向量。然后,运用灰色关联分析法实现对方案的排序和择优。最后,通过实例分析说明了该方法的可行性和有效性。     

基于二元语义TAC积分算子的语言型多属性群决策方法

针对属性之间存在模糊关联的语言型多属性群决策问题,给出了二元语义TAC(Two-Additive Choquet)积分算子的定义,分析和证明了算子的有关性质,并提出了相应的决策方法。该方法首先将各专家提供的语言短语形式的属性权重信息、属性关联信息与属性评价信息转化为二元语义形式,然后利用二元语义TAC积分算子将转化后的属性相关信息集结为各专家的方案评价值,并进一步集结专家意见获得方案的综合评价值,从而确定其排序。最后,通过实例分析和方法比较说明了所给方法的有效性和优点。研究结果表明,该方法具有属性关联刻画细致、计算过程简单且无信息损失、决策结果可解释性强等优点,为求解属性之间存在模糊关联的语言型多属性群决策问题提供了一种新的途径。     

Sum-fuzzy implementation of a choice function using artificial learning procedure with fixed fraction

In one if his paper Luo transformed the problem of sum-fuzzy rationality into artificial learning procedure and gave an algorithm which used the learning rule of perception. This paper extends the Luo method for finding a sum-fuzzy implementation of a choice function and offers an algorithm based on the artificial learning procedure with fixed fraction. We also present a concrete example which uses this algorithm.     

一种基于决策矩阵的DST-AHP多属性决策方法

针对层次分析法决策时存在两两判断、一致性检验次数过多和判断矩阵残缺性等问题,本文提出了一种基于决策矩阵的DST-AHP多属性决策方法。该方法结合决策矩阵的特征值,构建DST-AHP方法层次结构模型和判断矩阵,并根据判断矩阵定义不同属性下各焦元的基本概率分配函数;然后利用Dempster合成法则对基本概率分配函数值进行合成,依据合成后值对方案进行排序。最后对AHP和DST-AHP两种方法进行比较分析,说明该方法的有效性。     

垃圾填埋场选址问题的模糊数学模型研究

为有助于在环境和经济框架内评价垃圾填埋场选址决策,本文建立了关于该问题的多目标模型,模型中既考虑了安置和运营设施需要的固定成本和可变成本,也考虑了居民区承受的风险,以及各居民区承担风险的公平性。并进一步讨论了用模糊方法处理的一般多目标规划模型的模糊最优解与有效解及弱有效解之间的关系。最后使用两种模糊目标规划方法求解数值例子以分析所建模型的适用性,结果表明,加权模糊方法可以为决策者提供更接近期望值的满意方案。     

基于集对分析联系数的区间数型多属性决策投影模型及其在基坑支护方案评价中的应用

多属性决策时因掌握的信息有限,加之问题本身的复杂性、不确定性以及人类思维的模糊性,其属性的精确值往往难以获取,常用区间数形式表示针对多属性决策中各指标区间数不能直接进行大小比较的问题,提出一种基于集对分析联系数的区间数型多属性决策投影模型.模型用备择方案在理想方案上的投影进行集结,根据区间数的误差分布形式,沟通区间数与联系数的关系,根据a+bi型联系数大小比较法则,综合评价方案优劣.以某大厦基坑支护拟用的4种方案优化选择为例,说明方法的应用与模糊优化理论模型和灰色物元分析优化模型进行了对比,得到的结果一致.     

含语言评价信息的一种模糊多属性群决策方法

对于多属性群决策问题的处理,有时需要采用先决策、后综合的处理方法,而含有语言评价信息的多属性群决策问题,定性目标一般用语言评价信息描述,由决策人给出定性目标和权系数的语言变量评价,用梯形模糊数表示,对定量目标进行无量纲化处理;将决策人对于单一目标的评价指标聚合成多个目标的评价模糊数,采用Bass-Kw akernaak模糊数排序方法对方案进行排序;群体的评价通过Borda函数来集结方案集的群体排序.