Complex Systems Undergrad Courses

CMPLXSYS 100 Complexity: From Simple Rules to Complex Behavior

In this course, we explore a broad range of introductory topics in complex systems, examining how interactions between individuals can lead to emergent patterns, in systems ranging from cells, to societies, to climate change. The course also provides a friendly introduction to programming in an applied context.

Meets distribution Requirement: MSA

Meets General Requirement: BS; QR/1

PUBPOL 250/CMPLXSYS 250/ENVIRON 250 Social Systems and Energy

Public Policy is the home of this course.

Social Systems, Energy & Public Policy Energy is an incredibly complex topic by 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.Every aspect of present-day society depends on the continuing availability of clean, affordable, flexible, secure, and safe energy resources. Yet nearly 90% of our current energy needs are met by fossil fuels. Our reliance on fossil fuels has led to declining supplies, rising prices, global climate change, and security concerns. The current global energy economy is not sustainable. The technological challenges are formidable; but they cannot be considered solutions without considering the human and social behavioral side of energy demand.The quest for solutions to "The Energy Problem" is dominated by technology "fixes". The visions of practical technological fixes, whether electricity energy generation, oil exploration and extraction, pollution mitigation, automobile fuel efficiency and alternatives to combustion engines, etc., necessarily build on what we know today and presume that we can achieve in a couple of decades or so, through sufficient R&D, an energy supply-demand balance that fulfills a wide range of incompatible requirements — cheap, environmentally benign, politically secure, unconstrained supply, convenient, and safe. While we expect technology to come to our energy-rescue and support our established patterns of economic growth and energy-intensive lifestyles, we tend to expect very little from the human and social behavioral side of energy use and demand. In some ways the Energy Problem is yet another version of C.P. Snow's Two Cultures — parallel technology and social cultures with little mutual understanding and rare cross-over exchange.The Complex Systems view would hold that society and Energy technologies have coevolved through the actions of individual agents (inventors, scientists, entrepreneurs, financiers, writers, politicians, kings and queens, dictators, and statesman), learning, adapting, selecting, exchanging information, and interacting through transactions of many kinds. At every stage, the social, economic, and technological systems were tightly coupled. It is not possible to understand Energy Problems without framing them in a systems context.

CMPLXSYS 251/SOC 251 Computational Social Sciences  

Due to the growth in electronic sources such as cell phones, Facebook, Twitter, and other online platforms, researchers now have enormous amounts of data about every aspect of our lives - from what we buy, to where we go, to who we know, to what we believe. This has led to a revolution in social science, as we are able to measure human behaviour with precision largely thought impossible just a decade ago. Computational Social Science is an exciting and emerging field that sits at the intersection of computer science, statistics, and social science. This course provides hands-on, intermediate-level instruction in the methods and ideas of Computational Social Science. We will discuss how new online data sources and methods that are being used to analyze them can shed new light on old social science questions, and also ask brand new questions. We will also explore some of the ethical and privacy challenges of living in a world where big data and algorithmic decision-making have become more commonplace. Each week, students will have the opportunity to try their hand at analyzing big data from sources ranging from online dating profiles to New York city taxicabs to #metoo Tweets and other sources.

Meets distribution requirements: Social Science and QR/1 

SOC 260/CMPLXSYS 260 Tipping Points, Bandwagons and Cascades: From Individual Behavior to Social Dynamics 

Sociology is the home department of this course.

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 270 Agent-Based Modeling (ABM) 

Many systems can be modeled as being composed of agents interacting with one another and their environment. As a method, agent based modeling (ABM) can explain phenomena in the biological and social sciences, ranging from evolution to epidemic spread to flocking to cooperation to racial segregation in neighborhoods. Very simple rules governing agent behavior can lead to complex and emergent phenomena. In this course students will use Python to examine and modify well-studied agent based models of complex systems, as well as formulate models of their own.

Meets distribution Requirement: MSA

Meets General Requirement: QR/1

CMPLXSYS 325 Memes, Measles and Misinformation 

This course explores how contagious processes can help us understand a range of different phenomena observed in the real world—from infectious disease transmission, to the spread of information, misinformation, and disinformation. We also explore the feedbacks and interactions between many of these different transmission systems. We examine these questions through four main course modules: 1) infectious disease transmission, 2) networks, heterogeneity, and the spread of memes, 3) misinformation and disinformation, and 4) interactions and feedbacks between contagious systems. Example topics will include why misinformation and disinformation may sometimes spread more widely than true information, the development of the anti-vax movement, and an exploration of the dynamics of masking behavior during the COVID-19 pandemic.

Meets MSA, BS. and QR/1 requirements

CMPLXSYS 335: Introduction to Network Science

There are networks in every part of our lives: the Internet and the web, social networks, neural networks, the power grid, the road network, ecological networks, biochemical networks, and many others. The field of network science deals with mathematical and computational methods for the analysis and understanding of networked systems like these. This course will introduce the fundamental tools of the field through lectures and hands-on experience with analysis, modeling, and interpretation of networks and network data.  Students will be introduced to fundamental concepts of network science such as centrality measures (“Which of these web sites is most relevant to your search query?”), network connectivity (“How can we design the network of airline routes to get passengers to their destinations as quickly as possible?”), and dynamics (“How far and how fast will this disease spread within the community?”).  Students will learn the mathematical fundamentals of the field and then employ them in the form of computational and mathematical models and develop software for processing large-scale network data to extract new knowledge and engage in network problem solving in a wide range of areas.

Meets MSA, Interdisciplinary (ID), BS and QR/1 requirements

CMPLXSYS 351 Mini-Course | Introduction to Social Science Data

This course will provide an introduction to the programming language Python as a tool for loading, manipulating, and exploring social science datasets. We will begin with basic Python syntax and progress to more complicated tasks such as parsing, cleaning, imputing, and visualizing data, and we will pay particular attention to the skills needed and challenges faced when working with social science data. The bulk of the work in this course will be reading and coding a set of five lab assignments and a final project. Prior experience with Python is not required. 

Course Aims: 

• Develop familiarity with basic Python syntax, including lists, dictionaries, loops, and function definitions 

• Perform basic data exploration tasks using the standard Python modules Pandas and Seaborn

• Distinguish common file formats for storing data, and merge related datasets

• Incorporate course content into an independent final project 

• Hopefully have some fun too :) 

Meets distribution Requirement: MSA

CMPLXSYS 391 Modeling Political Processes 

In this course, we study the science and art of modeling political, economic, and social systems. The models that we construct contain actors that represent individuals, groups, organizations, nations, and even ideas themselves. Understanding, interpreting, and applying these models requires some facility with mathematical representations and algebraic manipulations and a desire to use mathematics to leverage understanding. We use models to think logically, to try to make sense of empirical regularities in the world and to think about how to design institutions and actions to beneficially intervene in processes involving social actors. This class will differ from other classes in that we won't focus on a particular subject. Instead, we are learning frameworks through which we can view many subjects and become better thinkers.

Meets SS, QR/1 requirements

CMPLXSYS 399 Complex Systems Independent Study

CMPLXSYS 401 History and Philosophy of Complex Systems

This course covers a broad range of fundamental topics relevant to the study of complex systems. The course work involves weekly readings that 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 and its historical context in science. Topics to be covered will include information, computation, feedback, interactions, dynamics, evolution, and systemic risk. We will explore these core ideas in context: when, and how, did these perspectives emerge in different fields? This course will emphasize communicating across fields, generating research ideas, and connecting concepts across fields. Grading will be based primarily on discussions, video presentations, and a term paper.

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. 

CMPLXSYS 435 Ecological Networks 

Networks have revolutionized the way we understand, represent and analyze complex systems. In particular, Ecology has greatly benefited from network theory to analyze the (inherently complex) structure and dynamics of ecological systems. This course introduces fundamental concepts and recent ecological theory on the structure and dynamics of networks composed by species connected via antagonistic (e.g. who eats whom) and/or mutualistic (e.g. plant-pollinator) interactions. These concepts and theories will be introduced via lectures and regular reading of primary literature, and actively learned via individual and group analysis of empirical data, mathematical models and computational tools. We will also elucidate how to use ecological networks to inform real-world problems such as the current environmental crisis.

CMPLXSYS 445/BIOPHYSICS 445/PHYSICS 445: Entropy and Information: Concepts and Applications

This course introduces the basic tools of Information Theory. Entropy, Relative Entropy, and Information, and highlights their utility with applications drawn from various disciplines. After introducing the basics of probability theory and information theory, we explore topics including coding, data compression, channel capacity, thermodynamics, population dynamics, gene transcriptions, network science and more. Cross listed with Biophysics and Physics.

Meets General Requirement: BS

EEB 466/CMPLXSYS 466/MATH 466: Mathematical Ecology 

Ecology and Evolutionary Biology is the home of this course.

The study of ecology is the study of complex systems that are intrinsically dynamic and quantitative. Ecologists formulate mathematical models to describe this complexity; the equations that result are interesting both for their biological predictions and their mathematical form. Full analytical solution of model equations is typically impossible, yet to the mathematically prepared mind, they can yield up their secrets. This course is intended to provide students with the tools needed to formulate and analyze ecological models. It is an overview of the major categories of models and the mathematical techniques available for their analysis. Although the focus is on ecological dynamics, students in other disciplines will find the methods readily applicable to their own fields.