Afreen Siddiqi Photo

Afreen Siddiqi

Appointment
Adjunct Lecturer in Public Policy
Office Address
79 John F. Kennedy St. Belfer Bldg L-2A

IGA-565

This course introduces theory and methods for quantitative analysis of complex, sociotechnical systems. The course will introduce complex adaptive systems theory and approaches for ‘systems thinking’ for analyzing modern systems that embody technological and social elements and operate within a changing environment. The methods will include Monte Carlo simulations, System Dynamics, and Agent-based Modeling. The focus of applications will be on water, energy, and transportation systems.

Complex adaptive systems theories provide a useful lens to understand why policy interventions in sociotechnical systems may produce delayed results, fail, or lead to unintended consequences. Key concepts of time lags in cause and effect, feedbacks, and adaptive behavior will be introduced and implications for policy and planning will be discussed. Applications will include analyzing infrastructure planning and capacity expansion under uncertainty, modeling future technology diffusion, and analyzing connections between water, energy, and food systems.

The assignments will step through the processes of problem definition, scoping, formulation, model creation, validation, analysis, and results presentation. The students will work in small teams for conducting in-depth analysis of system behavior, performance, and simulated outcomes.

The overall goal of the course is to enable students to: (1) Understand key properties of complex adaptive systems that can limit impact of interventions and policies; (2) Gain familiarity with methods and tools for studying system behavior and understand their strengths and limitations; and (3) Systematically conceptualize and build simulation models for conducting quantitative analysis.

This class requires no programming experience. Students should understand introductory-level statistics (such as API-201 or equivalent courses), and should understand probability distributions. Students should also have basic knowledge of using MS Excel.