SSIE501/ISE440: 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 in a Two-Dimensional Science
• 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
• Systems Thinking in Science and Society Today
• Biomedicine and Health
• Computational Social Science
• Artificial Intelligence

Policy on Generative Artificial Intelligence (GAI): Students may use GAI tools for gathering information from across sources and assimilating it for understanding, or even for creating an outline for an assignment, but the final submitted assignment must be original work produced by the individual student alone. If students choose to use GAI tools as they work through the assignments in this course, they must document this use in an appendix for each assignment. The documentation should include what tool(s) were used, how they were used, and how the results from the GAI were incorporated into the submitted work. Any content produced by GAI tool must be cited appropriately. Many organizations that publish standard citation formats now provide information on citing GAI (e.g., MLA).

• Participation: 20%.
• 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 and Discussion : 35%.
• Presentation of research papers plus leading of discussion. Students are assigned to paper sets individually or as group, but all students are supposed to read and participate in the discussion of every paper. During class, the presenter 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 presenter is to lead the discussion to make sure we address the important paper contributions and failures. Also, discussant should prepare 2-3 discussion questions.
• GRFP Research Proposal: 45%
• A preliminary GRFP proposal, following NSF-specified limits and format(see Section V), for a project that uses complex systems thinking in your domain of expertise.
• See additional BU materials for GRFP proposals. Include only the two main components:
1. Personal, Relevant Background and Future Goals Statement (3 pages).
2. Graduate Research Plan Statement (2 pages).
3. Note: "page limits include all references, citations, charts, figures, images, and lists of publications and presentations. Applicants must certify that the two statements (Personal, Relevant Background and Future Goals Statement, and Graduate Research Plan Statement) in the application are their own original work". See Section V.A for more details.
• Proposal should employ 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 would be useful, even transformative, 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. Critical discussion of systems science; what you like and dislike and where it may and may not be applied in your problem area.
2. Describing where you feel you would need to gain additional proficiency and training in. The course is an introduction, so it is totally reasonable to need additional training and reading in certain areas as part of a GRFP. For instance, let’s say you want to build large networks from health records to identify patient comorbidity patterns in a given population. You may want to outline what is your plan to learn more about network inference from multivariate data, including machine learning methods for electronic health records (e.g. course and faculty available at BU).
3. Do mention papers and topics from class. At least 40% of the readings should be cited with context. Cited references and bibliography do count for page limits.
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.
• 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.

Luis M. Rocha: Thursdays: 9:00 - 11:30am, EB-S4, EB-C3, or Online.

TA: TBA