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Regular version of the site

Computational Modeling for Social and Political Studies

2021/2022
Academic Year
ENG
Instruction in English
3
ECTS credits
Course type:
Compulsory course
When:
1 year, 3 module

Instructor

Course Syllabus

Abstract

Agent-based modeling (ABM) is a simulation method where the mass behavior of different interacting actors determines the dynamics of the system as a whole. Scientists describe the rules of behavior of agents at the individual level, in particular how these agents decide to act and interact under certain conditions, while the general dynamics of the system arises as a result of the local choices interactions. Such models allow to overcome many limitations in management analysis and policy assessment. Where it is impossible to conduct a field experiment introducing certain measures, agent-based modeling is used for a realistic assessment of the consequences, since it is possible to take into account the heterogeneity of decisions and attitudes of individuals, their resistance to change, the evolution of attitudes, capabilities and changes in norms.
Learning Objectives

Learning Objectives

  • To learn computational methods of experimental policy assessment
Expected Learning Outcomes

Expected Learning Outcomes

  • A student can formulate a micro-macro conceptual model explaining a complex empirical phenomenon
  • A student can formalize a decision-making model of agents in NetLogo code
  • A student can implement the computational agent-based model
  • A student can conduct what-if analysis, examine the results of political interventions under different assumptions on how people decide
Course Contents

Course Contents

  • Modeling complex phenomena
    The notion of complexity. Emergent phenomena: mechanics of Schelling model. Agent-based modeling: upgrading Coleman bath. Showcase of policy applications of ABM research.
  • Individual decision-making
    Descriptive vs. formal decision-making models. Utility maximization. Fast-and-frugal heuristics. Information update for dynamic modeling.
  • Designing an agent-based model
    Modeling goals. Agents and environments. Basic NetLogo language. Coding up decision-making. Embedding agents in networks and space. NetLogo interface features.
  • Computational experiments
    Implementing a macro-level intervention. Scenario exploration. BehaviorSpace and statistical analysis of experimental results.
Assessment Elements

Assessment Elements

  • non-blocking Formatives
    Each formative consists of several questions graded with 1-3 points. Late work is graded with half the points. At the end of the course, the formative with the lowest grade can be resubmitted. In total, the max. amount of points received through formatives will equal 40.
  • non-blocking Model
    The model is submitted in two steps: first as a draft for the peer review, and then as a final submission. In total, the max. amount of points received through model will equal 40.
  • non-blocking Review
    In total, the amount of points received through review will equal 20.
Interim Assessment

Interim Assessment

  • Interim assessment (3 module)
    Gfinal = (Gform + Gmodel + Greview) / 10 The grade will be rounded arithmetically
Bibliography

Bibliography

Recommended Core Bibliography

  • Uri Wilensky, & William Rand. (2015). An Introduction to Agent-Based Modeling : Modeling Natural, Social, and Engineered Complex Systems with NetLogo. The MIT Press.

Recommended Additional Bibliography

  • The Oxford handbook of analytical sociology ed. by Peter Hedström . (2011).