I am interested in natural and societal phenomena characterized by self-organization or self-adaptation. The key to understanding these phenomena, in my view, is to decipher the mutual influences between individual behaviors and collective outcomes: the former give rise to (and may continuously reproduce) the latter, whereas the latter impose certain order on the former. Since individual behaviors in these phenomena are autonomous, often heterogeneous, and highly interdependent, identifying the mutual influences is theoretically and methodologically challenging. On the one hand, it is hard to conceptualize or predict the collective outcomes before the entire scene is played out. On the other hand, traditional analysis approaches, which assume homogeneity or dependency for simplicity, become ineffective. I would like to study these phenomena using the conceptual and analytical tools from the fields of complex adaptive systems, network science, and data science. From a complex-adaptive-system perspective, the whole is more than the sum of different parts, because the nonlinear interactions among system components produce additional complexity. This field provides a number of tools that help us describe, model, analyze, or simulate the complexity. Network science provides a way to describe the order connect the macro order with micro building blocks, the interdependence of different parts. Data science provides visualization and analysis tools that can help us find patterns from data generated, especially the large number of data generated at the individual level during the process of self-organization. Help create hypothesis how parts of a system give rise to the collective behaviors of the system, and how the system interacts with its environment. There are three interrelated approaches to the modern study of complex systems, (1) how interactions give rise to patterns of behavior, (2) understanding the ways of describing complex systems, and (3) the process of formation of complex systems through pattern formation and evolution. , and consequently use tools like agent-based modeling and social network analysis that foreground the interactions between individuals in a social system. Methodologically, I’m interested in discovering, modeling, and visualizing the structural and dynamic patterns of a specific network and relate them with the functions and outcomes of that network.
My doctoral dissertation work addresses the theoretical and methodological challenges involved with studying how organizational members self-organize their . The underlying motivation for my work is the desire to explore the conditions under which can achieve self-organized. balancing individual members’ exploitation (i.e., exchanging and improving existing knowledge) and exploration (i.e., creating new knowledge) activities, so that both short-term efficiency and long-term development can be guaranteed. But what should an organization do to balance these self-initiated and self-organized (social interactions unavoidably generate informal structures that regulate future interactions) activities? My dissertation study explored the conditions that these activities could be self-balanced under the impact of the emergent informal structure. Using complex adaptive system theories and agent-based modeling, I found that self-balance could be achieved under three conditions: (a) there must be someone working alone to create new knowledge, while others interact to improve existing knowledge (do not have to be the same members though); (b) there must be random knowledge exchanges that occasionally happen between people who have relatively different knowledge bases; (c) the social relations established as a result of knowledge exchanges should not last too long.

My dissertation
My defense slides (For those who know Chinese or are interested in ancient Chinese literature, you may find something interesting at the right margin of some slides ^_^)
BTW, here seems to be a good place for a short list on how to upload PDF files (also other types of files such as images or videos) to Github step by step through Git Bash command line. Note that it's not a general instruction but something that suits my needs.
  1. Open Terminal (for Mac users) or the github bash or shell (for Windows and Linux users).
  2. Make sure you already cloned the repository to which you'd like to upload the file locally on your computer. You can do so by typing
    git clone https://github.com/lybird300/lybird300.github.io.git
  3. On your computer, move the file you'd like to upload to GitHub into the corresponding local directory that was created when you cloned the repository. If you forget where it is, type the following in Git Bash command line
    cd lybird300.github.io
    pwd
    Or git rev-parse --show-toplevel
    . My local directory is C:/Users/linly/lybird300.github.io
  4. At the command line, change the current working directory to your local repository. It's the same command at Step 2 (i.e., "cd path to the local repository")
  5. Add the file(s) in the local repository (windows operation) and stage (I don't know what this term means) them for commit by typing at the command line below (note that you can ask the system to complete the filename for you using the same "tab" trick)
    git add filename
  6. Now you must commit these changes that you have staged in your local repository (You can give necessary comments between the quotes). For example
    $ git commit -m "Adding a new file"
  7. Finally, push the changes in your local repository to GitHub by 
    $ git push origin master
Note