复杂适应性系统

课程大纲

 

INSTRUCTOR INFORMATION

Name: Dr. Ning Nan                                                    

Email: ning.nan@sauder.ubc.ca                           

Technology requirement: please bring your laptop computers

COURSE DESCRIPTION

This course will be taught in the style of a research seminar typical of North American graduate study programs. It introduces students to the complexity research stance and agent-based modeling method. The theories and method are introduced in the context of information systems research domains. The focus is on merits and limitations of complexity research in reference to primary research domains in the IS field.

 

The two-day course aims to engage students in a variety forms of discussions and hands-on activities!

LEARNING OBJECTIVES  

  • Acquire basic understandings of key concepts in a complexity research stance such as emergence, self-organization, and co-evolution
  • Have hands-on experience with the agent-based computer simulation approach
  • Become familiar with the research literature applying the complexity stance
  • Obtain a critical view of the theory-method fit in the information systems field

ASSESSMENT

SUMMARY

Participation       40%

Research proposal    60%

DETAILS

– Participation will be assessed by attendance in classes and contributions to class discussions. A central component of a research seminar is two-way communications between the instructor and students, and student-led discussions. Students are expected to read all mandatory readings listed in the table below before class. Fail to do so will prevent a student from engaging in class discussions and ultimately lead to a low participant grade.

A research proposal applying the theory and method introduced in this course will be completed after the class. On the second day, students will be guided to initiate and refine ideas of a research proposal. Specific requirements for the research proposal will be given during class.

 

SCHEDULE 

  CLASS TOPICS READINGS
Day 1

 

Introduction to a complexity stance ·       Mandatory reading: Anderson, Philip W. “More is different.” Science 177.4047 (1972): 393-396.

·       Optional reading: Simon, Herbert A. “The Architecture of Complexity.” Proceedings of the American Philosophical Society, Vol. 106, No. 6. (Dec. 12, 1962), pp. 467-482.

Day 1

 

Introduction to agent-based modeling ·       Mandatory reading: Nan, N. (2011). Capturing bottom-up information technology use processes: a complex adaptive systems model. MIS quarterly, 505-532.

·       Mandatory reading: Introduction to SugarScape (beginning to the “current features” section): http://sugarscape.sourceforge.net/documentation/design.html

·       Hands on: SugarScape model in Netlogo’s model library

Day 1

 

Core beliefs of the complexity stance ·       Mandatory reading: Ladyman, James, James Lambert, and Karoline Wiesner. “What is a complex system?.” European Journal for Philosophy of Science 3.1 (2013): 33-67.

·       Optional reading: Complexity concepts & definitions, at http://misq.org/skin/ComplexityConcepts.pdf

Day 1

 

ABM hands on &

Introduction to the Netlogo software

Day 2

 

Application of the complexity stance to IS research ·       Mandatory reading: N. Nan, J. Zhao, Y. Liu, F. Wang, and H. Tian. “From Hidden Order to Hidden Solution:

A Multilevel Coevolutionary Perspective of Digital Competitive Interaction” working paper.

Day 2

 

Applications of ABM to IS research ·       Mandatory reading: N. Nan and H. Tanriverdi, Unifying the Role of IT in Hyperturbulence and Competitive Advantage via a Multilevel Perspective of IS Strategy, MIS Quarterly, forthcoming.
Day 2

 

Research proposal initiation
Day 2

 

Research proposal discussion and refinement

 

 

Tips for Reading the Articles:  

 

Students are not expected to fully understand the assigned readings before class. The purpose of reading the articles before class is to get ready for the discussion in class. The seminar style class is to let students lead the discussion, ask and answer questions, learn how to evaluate work in the field, and learn how to generate research projects in the field. Do not assume everything said in a published article is correct. The following questions may help you get more insights from an article:

  • What is the main argument of this study
  • What is the novelty of this study
  • What is the theoretical foundation of the research model
  • What is the methodological approach of the study (if it is an empirical study)
  • Any weakness or limitation of the study
  • What future questions can this study lead to

 

 

 

课后总结

 

时间:2017.10.30

主题:复杂系统的界定

南宁教授在本次课程第一部分中带领我们一起学习了有关Ladyman, James, James Lambert, and Karoline Wiesner. “What is a complex system?.” European Journal for Philosophy of Science 3.1 (2013): 33-67.这篇论文的内容。通过分析复杂系统的充分和必要条件来对复杂系统的特征进行界定,强调了反馈是复杂系统的一个重要的必要条件。论文首先审查了以往对复杂系统的各种界定,并考虑了大量与文献中复杂系统和本领域中复杂系统相关的核心功能集;发现这些功能中有一些是复杂性的既不必要也不充分条件,而且过于模糊,无法用于任何分析;然后重新回顾了科学文献中的一些对复杂程度的标准定义,提出一个分类法,认为最能捕捉复杂系统定性概念的论点是统计复杂性;最后提供了一个必要条件列表作为复杂性特征的界定。

总的来说,CAS的特点及其充分、必要条件整理如下:

  • 非线性(Nonlinearity):非线性指主体以及它们的属性在发生变化时, 并非遵从简单的线性关系。特别在主体与系统或环境反复的交互作用中, 这一点更为明显。(既不是复杂系统的必要条件也不是其充分条件)
  • 反馈(Feedback):系统的得到的反馈取决于它在较早的时候如何与它的neighbours进行交互(必要条件但不是充分条件);
  • 自发性(Spontaneous order)复杂系统不是随机的,也不是完全有序的,但是显示某种自发性是复杂系统的必要条件;
  • 鲁棒性(Robustness是复杂系统的必要条件但不是充分条件;缺乏中央控制(Lack of central control)是复杂系统的一个特征但不是充分条件;
  • 涌现(Emergence: 涌现现象最为本质的特征是由小到大、由简入繁 涌现现象产生的根源是适应性主体在某种或多种毫不相关的简单规则的支配下的相互作用。主体间的相互作用是主体适应规则的表现, 这种相互作用具有耦合性的前后关联, 而且更多地充满了非线性作用, 使得涌现的整体行为比各部分行为的总和更为复杂。在涌现生成过程中, 尽管规律本身不会改变, 然而规律所决定的事物却会变化, 因而会存在大量的不断生成的结构和模式。这些永恒新奇的结构和模式, 不仅具有动态性还具有层次性, 涌现能够在所生成的既有结构的基础上再生成具有更多组织层次的生成结构。也就是说, 一种相对简单的涌现可以生成更高层次的涌现, 涌现是复杂适应系统层级结构间整体宏观的动态现象(摘自百度百科)。(必要条件但不是充分条件)
  • 层级组织(Hierarchical organization:复杂系统通常拥有许多组织层(Organization),这些组织层形成了系统和子系统之间的层次体系(Hierarchy);

以上复杂性系统所有特征的最终结果:一个被组织成拥有多种层次结构和属性的实体,上下层次的属性之间相互作用,呈现出规律性、因果性,以及各种对称性,有序性和周期性行为。

  • 大量性(Numerosity):上述层级组织(Hierarchical organization)只有当系统包含大量组成部分(a large number of parts),并且这些组成部分都参与交互时才存在。

 

总之,复杂系统和复杂性的定义并不简单,它是潜在的哲学上的兴趣。前面介绍的秩序、组织的概念以及反馈的观点都暗示了复杂性的信息理论方法,因为这有助于对复杂系统的理解,即通过各部分之间的信息交换来维持其秩序和等级组织

 

 

 

时间:2017.10.30

主题:Netlogo软件的应用

在本次课程第二部分中,我们学习了Netlogo软件中的SugarScape模型,最简单的SugarScape模型是由主体(Agent)和糖(Sugar)构成,主体在糖域中移动来获取养分,但是在这个过程中主体之间缺乏互动性,因此课堂上进行分组讨论如何增强主体之间的互动性,讨论结束后各小组派代表发言,每个小组在讲解时,提出一种主体间的互动方案,并与现实中企业案例相结合,其中主要有跟随效果,即主体间进行跟随,一个主题选择某块糖的位置,其周围的主体下次便会跟随着选择这个位置,反映在现实案例中主要是商家在进货时会借鉴其他商家进货之类,还有兼并效果,及吃糖较多的“大主体”会吃掉吃糖较少“小主体”,反映在现实案例中主要是大公司会兼并小公司之类。最后小组间进行提问,并由南宁教授进行总结。