The Center for the Study of Complex Systems is pleased to offer the undergraduate academic minor in Complex Systems. This minor was first introducted in the Fall of 2010. Students who wish to enroll can do so in Wolverine Access.
CMPLXSYS 250: Social Systems, Energy, and Public Policy- is an incredibly complex topic by the virtue of the inter-linkages of science, technology, public policy, economics, and human behaviors. This course will examine all aspects of energy: supply and demand; technical and social; with a concerted look at the natural place of social science (behavior, pricing, externalities, social norms) in the energy sphere.
CMPLXSYS 260/SOC 260: Tipping Points, Bandwagons and Cascades: From Individual Behavior to Social Dynamics --- In this class, we examine how interdependent behaviors of individuals can lead to some surprising and unexpected social outcomes. We will explore both theoretical models and empirical applications of social dynamics, including sexual networks and marriage markets, the formation and transformation of neighborhoods, the success or failure of social movements, and the diffusion of innovation.
CMPLXSYS 391/POLSCI 391 Introduction to Models. We study the science, art, and practice of modeling. Models help usto understand the logic of phenomena, to explain, communicate, predict, act, design, andexplore. We focus on models relevant to political, economic, and social systems but we willventure into other disciplines. The models we study apply to a diverse array of types ofactors ranging from individuals, to groups, to organizations, and nations. Understanding, in-terpreting, and applying these models requires a willingness to grasp abstractions, to interpretdiagrams, and to perform algebraic manipulations of equation based models. Tentative Fall 2018 Syllabus
CMPLXSYS 425: Evolution In Silico While every population of living organisms is evolving, not everything that evolves is alive. Nature’s success at finding innovative solutions to complex problems has inspired many computational implementations of the evolutionary process. Philosophically, this is possible because evolution is itself a substrate neutral process (i.e., evolution can occur regardless of what particular substance makes up the individuals in a population). This fundamental property of evolution creates a deep connection between computational implementations and the biological process responsible for the diversity of life on Earth. We will highlight this connection and the possibility of two-way interdisciplinary discovery through regular readings and discussions. Some of the various implementations of evolution we will learn about include approaches to solve optimization problems, building controllers and/or bodies for robots, and using computational instances of Darwinian evolution to study fundamental questions in biology. Detailed Course Information Here.
CMPLXSYS 470/PHYS 470: Experiments in Nonlinear Dynamics --- The ideas of nonlinear science are essential to the modern scholar with beneficial applications ranging from economic forecasting, climate modeling and social networking. This course introduces the core concepts of nonlinear dynamics through laboratory experiments on physical systems.
CMPLXSYS 489-001: Collective Intelligence --- Collective Intelligence (CI) refers to the potential for a group or population to have better information, make better predictions, or to find better solutions to problems than the average or the best individual. Democracies, markets, juries, scientific panels, and insect colonies rely on collective intelligence. In this course, we study the phenomenon of collective intelligence focusing on what makes it possible, how it depends on diversity, coordination, and incentives. We place particular focus on the role of institutions - both formal and informal - in producing collective intelligence. The course borrows from literatures in philosophy of science, political science, economics, organizational behavior, neuroscience, and machine learning. Students should be comfortable with algebraic models, basic statistics, and readings from multiple fields. Course grades will be determined by individual assignments, group activities, and exams.
CMPLXSYS 501: An Introduction to Complex Systems --- This course covers a broad range of fundamental topics relevant to the study of complex systems. The course work involves weekly readings focus on "classics" in the complex systems literature, in order to give students a broad, general understanding for the variety of work that falls under the rubric of complex systems. Topics to be covered will include evolutionary systems, self-organized criticality, measures of complexity, approaches to modeling complex adaptive systems, and emergence. Authors to be covered include Holland, Axelrod, Kaufmann, Bak, and Gell-Mann. Grading will be based on the participation in the discussions and on two or three term papers.
CMPLXSYS 531: Basic Computing Skills for Programming Agent Based Models --- This course covers the basic computing skills which are required for implementing again-based models using Swarm (and other similar packages) in a LINUX/UNIX environment, including (a) basic LINUX/UNIX commands, (b) basic programming concepts (variables, operators, flow-control), (c) creating simple C and objective C programs and (d) basic Object-Oriented Programming concepts. For students intending to take CMPLXSYS 530.
CMPLXSYS 535/PHYS 508: Theory of Social and Technological Networks --- Introduce and develop the mathematical theory of networks, particularly social and technological networks; applications to important network-driven phenomena in epidemiology of human infections and computer viruses, cascading failure in grids, network resilience and opinion formation. Topics covered: experimental studies of social networks, WWW, internet, information, and biological networks.
CMPLXSYS 541/PHYS 541: Introduction to Nonlinear Dynamics and the Physics of Complexity --- An introduction to nonlinear science with an elementary treatment from the point of view of the physics of chaos and fractal growth.