SSIE501: Introduction to Systems Science

Description:The course deals with the foundations of Systems Science, as well as current advances in Complex Networks and Systems which is the modern expression of this interdisciplinary field. A complex system is any system featuring a large number of interacting components (agents, processes, etc.) whose aggregate activity is nonlinear (not derivable from the summations of the activity of individual components) and typically exhibits hierarchical self-organization under selective pressures. The networks of interactions that comprise such systems can be studied separately from or together with the multivariate dynamics that define them. In the former case, the focus is the graph structure of networks which is typically pursued in Network Science, whereas dynamical systems theory focuses on the latter. Understanding networked complex systems is key to solving some of the most vexing problems confronting humankind, from discovering how thoughts and behaviors arise from dynamic brain connections, to detecting and preventing the spread of epidemics or unhealthy behaviors across a population. But the study of complex systems requires an understanding of both structure and dynamics which often interact in non-separable multiple levels where information, selection, and collective dynamics operate. Indeed, modeling network interactions among variables operating at multiple scales is an essential capability for effective interventions in complex systems---such as the dynamic web of cellular processes and genetic regulation, the intricate wiring of the brain, as well as patterns of human and social behavior involved in disease. Across all these systems, mapping, analyzing, modeling, and visualizing underlying networks are indispensable steps toward understanding how they work. The sciences of complexity are also necessarily based on interdisciplinary research so that teams of scientists can most effectively approach both the general characteristics of all these systems as well as the specific methodologies required to measure and model them.

Aims: This course is designed to introduce and discuss the history, methodology and impact of complex systems science; we cover key literature, recent advances in the field, and introduce useful computational techniques in the field. We will study concepts such as Information, General Systems Theory, Networks, Modeling, Multi-Level Complexity, as well as their impact on science and society. The course will also attempt to define and understand what systems thinking can bring to science and society.

Lecture Outline

• Cybernetics and the Information Turn
• Inventing Computers and Information
• Information, and Semiotics
• Organisms as Computers
• From Cybernetics to Systems Science
• Systemhood and General Systems
• Organized Complexity
• Modeling Complex Systems
• Complex Systems Science under Limits
• Limits of Computation
• Dynamics and Tipping Points
• Self-organizing Systems
• Interdisciplinarity
• Team Science
• Costs and Needs of Interdisciplinary Science
• Organization of Complex Systems
• Heterarchies and Networks
• Hierarchies and Multi-Level Complexity
• Evolutionary Systems and Model Failure
• Collective Intelligence: Life as intertwined networks
• Hands-On Python
• Playing with Information, Complex networks and systems

• Participation: 15%.
• based upon attendance and participation in class discussion. We expect that students will approach the course as they should a professional job and attend every class.
• Paper Presentation: 25%.
• Presentation of research paper plus leading of discussion. Students are assigned to papers as lead discussants, but all students are supposed to read and participate in discussion of every paper. During class, a lead discussant prepares a short summary of the paper (10-15 minutes)---no formal presentations or PowerPoint unless figures are indispensable. The summary should: 1) Identify the key goals of the paper (not go in detail over every section); 2) What discussant liked and did not like; 3) What authors achieved and did not; 4) Any other relevant connections to other class readings and beyond. After initial summary, discussion is opened to all, and role of lead discussant is to lead the discussion to make sure we address the important paper contributions and failures.
• Python Homework: 25%
• From final workshop
• Project Proposal Paper: 35%
• A paper (no more than 8 pages, no less than 4, excluding figures) with a proposal for a project that uses complex systems thinking in your domain of expertise.
• Paper should outline a proposal for using systems science methods, as covered in class, on a problem that interests you. This could be focusing on a problem in your workplace or in a context you would like to be involved with. Identify the problem area and explain relevant data, principles, unresolved problems, or issues you would like to tackle.Explain why systems science methods and approaches could be useful for problem area.
• Outline how you would go about using systems science methods to approach the knowledge, practice, or analytical gaps you identify in the problem area. This includes:
1. Describing where you feel you would need to gain proficiency in.
2. The course was an introduction, so it is totally reasonable to need additional training and reading in certain areas. For instance, let’s say you want to built large networks from health records to identify patient comorbidity patterns in a given population. You may want to say you would like to learn more about network inference from multivariate data, including machine learning methods for electronic health records.
3. Do mention papers and topics from class. At least 25% of the readings should be cited with context.
4. Do some research on Google scholar to identify potential publications that you would like to read to learn more about problem area and systems methodologies.
5. List references.
• Discuss the potential impact of your approach and systems thinking at large in the problem area you chose. Consider impacts in problem area as well as broader impacts in society and the world at large.
• Discuss, including in a critical manner, what you like and dislike about systems science. Where it can and cannot be applied in your problem area.

Luis Rocha: Thursdays 9 - 11:30am, Engineering Building C3 (Complex Adaptive Systems and Computational Intelligence Lab) or Online, or by appointment

Hiroki Sayma: