Computational Social Science, PhD

Program Overview

The Computational Social Science PhD program at George Mason University trains students to become professional computational social scientists in academia, government, or business. The program emphasizes interdisciplinary research and provides students with the skills to analyze and model complex social phenomena using computational methods. Graduates can pursue careers in data analysis, social network analysis, agent-based modeling, computational social science research, and more.

Program Outline

Degree Overview:

The Computational Social Science PhD program at George Mason University aims to train graduate students to become professional computational social scientists in academia, government, or business. The program provides a unique and innovative interdisciplinary academic environment for exploring, discovering, and developing skills in computational social science.

Outline:

The program requires a total of 72 credits and is structured as follows:

• Core Courses (12 credits):
• CSS 600: Introduction to Computational Social Science (3 credits)
• CSS 605: Object-Oriented Modeling in Social Science (3 credits)
• CSS 610: Agent-based Modeling and Simulation (3 credits)
• CSS 620: Origins of Social Complexity (3 credits)
• Extended Core Courses (6 credits):
• Students select 6 credits from the following:
• CSS 625: Complexity Theory in the Social Sciences
• CSS 635: Cognitive Foundations of Computational Social Science
• CSS 645: Spatial Agent-Based Models of Human-Environment Interactions
• CSS 665: Complex Adaptive Systems in Public Policy
• CSS 692: Social Network Analysis
• Discipline-based Courses (15 credits):
• Students select 15 credits of discipline-based social science courses in a specific area (e.g., anthropology, economics, geography, history, linguistics, political science, or sociology) as approved by their advisor.
• Electives (15 credits):
• Students select 15 credits of electives or independent research, as approved by their advisor, to provide further substantive or methodological specialization.
• Students with a strong computing background may use these electives for substantive social science courses, while students with a strong social science background may use them for computing courses.
• Candidacy Examination:
• Taken after completing core requirements and a majority of additional coursework (18 + 15 credits).
• Assesses the student's knowledge in CSS, their chosen focus area, ability to integrate materials, and potential for a successful dissertation.
• Consists of written and oral parts.
• Dissertation Proposal:
• Prepared and defended within a year after passing the candidacy examination.
• Written in the form of an extramural research grant proposal.
• Developed in consultation with the dissertation committee.
• Successful defense makes the student a PhD candidate.
• Dissertation Research (24 credits):
• Students select 24 credits from the following:
• CSS 998: Doctoral Dissertation Proposal
• CSS 999: Doctoral Dissertation
• Doctoral Dissertation Defense:
• The PhD dissertation is a detailed written report of original and significant research in computational social science.
• Defended before the dissertation committee in a forum open to students, faculty, and staff.
• Successful defense and completion of final revisions leads to the PhD degree.

Assessment:

• Candidacy Examination: Assesses the student's knowledge in CSS, their chosen focus area, ability to integrate materials, and potential for a successful dissertation.
• Dissertation Proposal: Evaluates the student's research proposal and its feasibility.
• Doctoral Dissertation: Assesses the originality, significance, and quality of the student's research contribution to computational social science.

Teaching:

• Faculty: The program is taught by faculty members with expertise in computational social science and related fields.
• Teaching Methods: The program utilizes a variety of teaching methods, including lectures, seminars, workshops, and independent research.

Careers:

• Potential Career Paths: Graduates of the program are prepared for careers in academia, government, and business.
• Opportunities: The program provides students with the skills and knowledge necessary to pursue careers in areas such as:
• Data analysis
• Social network analysis
• Agent-based modeling
• Computational social science research
• Policy analysis
• Data science

Other:

• Example Dissertation Areas:
• Agent-based computational economics
• Computational political economy
• Computational linguistics
• Social network analysis
• Computational anthropology
• Computational political science
• Computational sociology
• Complexity theory
• Computational methodology
• Agent-based computational geography
• the Outline section is limited to the overall program structure and course requirements.