基于CAS理论的企业员工群体行为模拟

基于复杂适应性系统理论(complex adaptive system,CAS),利用SWARM平台开发了基于多主体的群体行为模拟系统,应用激励学习和遗传算法实现员工在模拟中的学习与适应能力,用元胞自动机方法模拟员工的行为,并建立了群体系统模型,模型中的各个主体具有其自身的行为规则和策略,通过主体以及主体与其周围环境之间的相互作用和影响,使群体系统涌现出宏观层面上的特征.最后对一个员工群体示例进行模拟试验,分析和预测引起员工群体行为变化的原因.     

基于随机突变的员工反生产行为定性模拟研究

反生产行为作为影响员工行为绩效和组织稳定性的关键因素,已成为当前企业最为关切的现实问题之一.结合以往员工反生产行为的相关研究以及在管理现实中凸显的突变特征,在考虑人的心理行为扰动的基础上,从随机突变理论的全新视角构建了员工反生产行为随机尖点突变模型,并分析了反生产行为的非线性演化过程.在运用Cuspfit方法验证突变模型的合理性并进行参数估计后,引入定性模拟理论,将静态严格的随机突变模型转化为能解决模糊的、不确定性的问题的半定性突变模拟模型.最后在Matlab平台上实现该模拟系统,通过虚拟实验对中国某OEM企业面临的员工反生产行为困局进行动态分析.实验结果从企业科学管理模式、员工甄选及情绪管理、反生产行为的危害控制和补救措施等方面为组织反生产行为管理问题提供决策支持.     

基于组织学习的生产系统人人合作方式的仿真研究

为了比较不同的组织合作方式对生产系统绩效的影响,分析了生产系统中组织学习行为和主体合作过程,并基于复杂适应系统理论建立了描述生产系统中生产者的主体模型。在其基础上提出一种集成了主体模型、离散生产系统仿真软件、数据处理软件和应用软件开发环境的人人合作仿真方法。以某摩托车发动机自治生产单元为工程背景,详细给出了基于组织学习的人人合作仿真流程,并以订单完成时间、订单准时完成率、人员利用率及组织学习效应为目标矢量比较分析了指定合作与自主合作对生产系统绩效的不同影响。结果表明,自主合作方式下生产单元的组织柔性较高,更容易应对多品种小批量的混流生产环境。     

基于前景理论与多Agent模拟的KIBS员工合作与冲突行为研究

随着知识经济时代的到来,知识密集型服务业(KIBS)已经成为西方主要发达国家和地区产业结构调整关注的重点方向和近年来学术界的研究热点之一。文章研究了KIBS员工合作与冲突行为产生机理与演化规律,首先结合了人格心理学、前景理论和博弈论的相关理论,设计了KIBS员工合作与冲突行为决策模型。之后在仿真软件Anylogic 6.5.0上建立了相应的多Agent模拟模型,并针对收益参数和群体沟通这两种影响因素设计了模拟实验。最后对模拟结果进行了分析,发现收益参数中除了惩罚系数,控制因子对最终行为比例也产生影响,而沟通概率则对演化过程行为变化的幅度产生影响。     

基于复杂网络演化博弈的传统产业集群低碳演化模型研究

本文基于复杂网络演化博弈理论,从微观异质性主体的预期学习和自适应行为特征出发,综合运用随机博弈及博弈学习模型、多主体系统建模等方法,构建了传统产业集群低碳演化模型,并进行了产业低碳策略竞争、涌现和不同演化情景的仿真分析。研究表明,产业集群的复杂网络结构、主体的异质性预期和决策行为对集群低碳策略的涌现有着重要影响;要想在集群内推广应用新的低碳策略,集群异质性主体的低碳偏好、集群复杂网络的外部效应、低碳策略的采用成本和技术兼容性是重要影响因素。     

高绩效工作系统、员工需求与反生产行为关系研究

为明确高绩效工作系统对反生产行为的作用机制,基于劳动关系理论构建一个有调节的中介模型,通过对237份问卷进行层次回归分析,结果发现:高绩效工作系统对员工的反生产行为有显著负向影响;员工需求在高绩效工作系统与组织指向的反生产行为之间起部分中介作用;员工需求在高绩效工作系统与人际指向的反生产行为之间起完全中介作用;心理契约破裂负向调节员工需求与反生产行为的关系,并在整个模型中起调节作用。     

集成博弈和多智能体的人群工作互动行为研究

基于进化博弈视角,对人群工作互动行为进行多智能体模拟研究.建立了收益和惩罚共享的群体工作收益博弈模型,考虑工作个体的个性决策特征,设计基于历史信息和个体决策特性的混合学习规则,并用多智能体方法对群体工作场景进行描述.在Repast类库基础上,用Ja-va实现该多智能体模拟系统.模拟结果表明:1)群体规模对宏观工作趋势影...     

略论人力资源管理中知识型员工管理

随着知识经济的发展,知识型员工正逐渐成为现代组织的主体,是新型的工作群体。如果仅仅运用传统的激励手段去管理现代企业的知识型员工,往往会使企业陷入困境。因此,有必要探讨运用现代激励手段和理论工具对知识型员工进行有效激励。本文分析了知识型员工的激励因素,结合知识型员工的概念、特点以及玛汉·坦姆仆的激励模型,进一步深入研究知识型员工的人力资源管理与开发对策。     

基于突变理论的企业员工反生产行为研究

员工反生产行为已经成为影响企业绩效、阻碍企业发展的关键因素之一。在分析反生产行为各影响因素的基础上,结合经典的员工反生产行为形成机制模型,从突变理论的全新视角,在考虑人的心理随机扰动前提下构建员工反生产行为随机尖点突变模型。运用突变模型解释员工心理行为在个体情绪和情境压力作用下的变化过程,并基于随机突变理论和统计学方法对模型进行验证,最后基于模型的数理分析提出员工反生产行为的避免机制和控制策略。研究结果表明,突变模型对员工反生产行为的解释优于传统的线性模型和非线性模型;完善的组织制度和员工关怀是有效的反生产行为避免机制,在控制负面行为危害上,心理疏导要比改善组织情境更有效率;在同样的情境压力下,心理状态的差异会造成截然不同的员工行为,企业能通过调节心理阈值点明显降低极端反生产行为出现的概率;员工心理感知的恢复具有滞后性,企业要在基本应急补救措施上进行更多的努力。     

基于Agent的物流系统仿真及进化算法研究

本文研究在电子商务环境下连锁经营物流系统规划问题,采用基于自适应Agent建模技术进行仿真研究,为了弥补有限自适应仿真优化出现局部最优解或满意解的现象,采用进化算法(遗传算法)对规划方案(物流仿真模型)进行整体进化操作,通过全自动的、并行的重复仿真与运算,最终寻求全局最优解.求解时提出了从基于Agent模型向线性编码的编码解码算法及并行仿真、自动寻优机制,从而构筑一个基于SWARM平台的仿真优化+进化计算的规划平台,总结出基于上述研究成果的连锁经营物流规划的全新方法.     

基于三层-回声模型的供应链复杂适应系统资源流研究

以劳动价值论为基础分析了供应链系统资源价值的结构不对称性、非线性、动态性、时空性以及各种资源的有机整体性等特点,对供应链中资源价值“染色体”进行了串联并形成多主体区室,通过随机数确定主体相互作用点并将各个资源进行整合;对资源供给-分配进行If-Then规则以及相关机会约束规划准则的设置,将其应用于供应链复杂适应系统主体交互作用的三层-回声模型中,在Swarm平台建模过程中融入遗传算法进行模拟仿真。结果表明,供应链复杂适应系统中各个资源是一个不可分割的有机整体,管理、技术、信息、市场、人才和理念等资源在其中起着核心纽带作用,经过权函数对资源价值的调整更能体现资源的有效性,三层-回声模型是描述供应链主体间相互作用较为实用而准确的内部模型,系统中主体按该模型进行合作、竞争与资源分配,并达到系统总资源利用的最优化效果。     

面向第4方物流的多代理人作业整合优化算法

物流作业整合是现代物流方法中减少物流成本的主要手段.在综合考虑代理商选择和线路优化两问题的基础上,建立了基于图状结构的面向第4方物流的多代理人作业整合优化模型.为了避免将代理商选择和线路优化作为两个互相分离的子问题来处理,提出了求解它的两层邻域搜索算法.第1层采用了转移、交换和环形移动3种移动策略求解作业在代理商之间的分配,而在第2层提出了路合并、路生成替换策略,形成了适于该问题的优化算法.随机产生20例算例,将两层邻域搜索算法的计算结果与基于k-最短路的枚举算法的计算结果进行比较,说明了该算法的可行性和有效性.     

Learner Engagement: A New Perspective for Enhancing Our Understanding of Learner Motivation and Workplace Learning

Abstract

Organizations invest millions of dollars in workplace learning programs to develop human capital for competitive advantage. The effectiveness of workplace learning programs is directly linked to learner motivation. However, we contend that our current understanding of learner motivation and workplace learning is limited by the tendency to conduct research based on the traditional instructional system design model and the limited adoption of organizational behavior theories to guide such research. We propose that studying workforce learning from a psychological engagement perspective is necessary to gain a better understanding of learner motivation and subsequent knowledge and skill acquisition. We review studies of learning methods and contextual factors that likely influence learner engagement. We propose a research agenda focusing on six directions for future research.     

Strategic Learning and the Topology of Social Networks

We consider a group of strategic agents who must each repeatedly take one of two possible actions. They learn which of the two actions is preferable from initial private signals and by observing the actions of their neighbors in a social network. We show that the question of whether or not the agents learn efficiently depends on the topology of the social network. In particular, we identify a geometric “egalitarianism” condition on the social network that guarantees learning in infinite networks, or learning with high probability in large finite networks, in any equilibrium. We also give examples of nonegalitarian networks with equilibria in which learning fails.     

Will the Dismantlement of Early Retirement Schemes Increase Older Unemployment? A Competing‐risk Analysis for Belgium

On the basis of administrative data for Belgium, we estimate a competing‐risk model on transitions from employees aged 50 and older into unemployment, early and old‐age retirement while accounting for forward‐looking work disincentives. Our estimates are used to simulate a cut in early retirement benefits. Although this would enhance the financial sustainability of the social security system, our simulations predict a strong increase in unemployment among older blue‐collar workers in traditional industries. Members of private saving plans or occupational pension schemes and highly educated workers are predicted to move into the old‐age pension system.     

Sales Force and Competition in Financial Product Markets: The Case of Mexico's Social Security Privatization

This paper examines how sales force impacts competition and equilibrium prices in the context of a privatized pension market. We use detailed administrative data on fund manager choices and worker characteristics at the inception of Mexico's privatized social security system, where fund managers had to set prices (management fees) at the national level, but could select sales force levels by local geographic areas. We develop and estimate a model of fund manager choice where sales force can increase or decrease customer price sensitivity. We find exposure to sales force lowered price sensitivity, leading to inelastic demand and high equilibrium fees. We simulate oft proposed policy solutions: a supply‐side policy with a competitive government player and a demand‐side policy that increases price elasticity. We find that demand‐side policies are necessary to foster competition in social safety net markets with large segments of inelastic consumers.     

Reverse Auctions with Multiple Reinforcement Learning Agents*

Reverse auctions in business‐to‐business (B2B) exchanges provide numerous benefits to participants. Arguably the most notable benefit is that of lowered prices driven by increased competition in such auctions. The competition between sellers in reverse auctions has been analyzed using a game‐theoretic framework and equilibria have been established for several scenarios. One finding of note is that, in a setting in which sellers can meet total demand with the highest‐bidding seller being able to sell only a fraction of the total capacity, the sellers resort to a mixed‐strategy equilibrium. Although price randomization in industrial bidding is an accepted norm, one might argue that in reality managers do not utilize advanced game theory calculations in placing bids. More likely, managers adopt simple learning strategies. In this situation, it remains an open question as to whether the bid prices converge to the theoretical equilibrium over time. To address this question, we model reverse‐auction bidding behavior by artificial agents as both two‐player and n‐player games in a simulation environment. The agents begin the game with a minimal understanding of the environment but over time analyze wins and losses for use in determining future bids. To test for convergence, the agents explore the price space and exploit prices where profits are higher, given varying cost and capacity scenarios. In the two‐player case, the agents do indeed converge toward the theoretical equilibrium. The n‐player case provides results that reinforce our understanding of the theoretical equilibria. These results are promising enough to further consider the use of artificial learning mechanisms in reverse auctions and other electronic market transactions, especially as more sophisticated mechanisms are developed to tackle real‐life complexities. We also develop the analytical results when one agent does not behave strategically while the other agent does and show that our simulations for this environment also result in convergence toward the theoretical equilibrium. Because the nature of the best response in the new setting is very different (pure strategy as opposed to mixed), it indicates the robustness of the devised algorithm. The use of artificial agents can also overcome the limitations in rationality demonstrated by human managers. The results thus have interesting implications for designing artificial agents in automating bid responses for large numbers of bids where human intervention might not always be possible.     

Optimal Enhancement and Lifetime of Software Systems: A Control Theoretic Analysis

We develop an optimal control model to maximize the net value provided by a software system over its useful life. The model determines the initial number of features in the system, the level of dynamic enhancement effort, and the lifetime of the system. The various factors affecting these optimal choices are systems characteristics (e.g., complexity, age, quality), user learning, and process maturity. We also consider that there is a time lag between the addition of a feature and the realization of its benefit to users. The basic model is extended to consider the decision of replacing the existing system by a new one.     

Market Entry Costs,Producer Heterogeneity,and Export Dynamics

As the exchange rate, foreign demand, and production costs evolve, domestic producers are continually faced with two choices: whether to be an exporter and, if so, how much to export. We develop a dynamic structural model of export supply that characterizes these two decisions. The model embodies plant‐level heterogeneity in export profits, uncertainty about the determinants of future profits, and market entry costs for new exporters. Using a Bayesian Monte Carlo Markov chain estimator, we fit this model to plant‐level panel data on three Colombian manufacturing industries. We obtain profit function and sunk entry cost coefficients, and use them to simulate export responses to shifts in the exchange‐rate process and several types of export subsidies. In each case, the aggregate export response depends on entry costs, expectations about the exchange rate process, prior exporting experience, and producer heterogeneity. Export revenue subsidies are far more effective at stimulating exports than policies that subsidize entry costs.