Introductory description

The significance of modelling and simulation spans many domains. From ecology to engineering, economics to cellular automata, computer simulations have unlocked new realms of prediction, knowledge, and control. For scientists they offer new kinds of opportunities for research and invite study of new problems. For philosophers they raise questions about emergence and the nature of scientific representation. For sociologists and psychologists, they raise questions about the nature of reasoning. In recent times, the development and application of computational modelling and simulation for studying social phenomena have gained increasing prominence, largely due to the widespread availability and accessibility of significant computing power and data. Further contributing to this trend is the availability of specialised software packages and tools that render “programming” for computational modelling and simulation accessible to programmers and non-programmers alike. Through modelling and simulation, we can perceive society and the social sphere as complex systems composed of multiple interacting elements which gives rise to macro-level phenomena. In doing so, models and simulations also act as boundary objects of the social systems they represent (i.e., as artefacts collectively shared by different users with varying understanding of the social systems), and thereby offer a means for interdisciplinary collaboration and reasoning.

The module takes a multi-disciplinary and multi-method approach to equip students with the knowledge and skills to develop and use computational models and simulations of social phenomena based on theory and data, and to interpret results and communicate findings in appropriate ways to address clearly defined problems in different domains. The module will help students understand (and undertake) the journey from data to modelling (sense-making about social systems using data) to simulation (exploring the behaviour of social systems across space and time).

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

An illustrative week-by-week module outline is provided below (and the practical sessions would involve some group work):

1. Modelling, Simulation, and Complex Systems • 1 – practical: Taster session on modelling and simulation to explore complex systems

2. Thinking, Selecting, Representation, and Reasoning through Modelling and Simulation • 2 – practical: Getting set up, running simple models, and discussion

3. Approaches to Simulation 1: Simulating Individuals • 3 – practical: Conceptualisation and design of a toy model

4. Approaches to Simulation 2: Synthetic Population • 4 – practical: Implementing a toy model

5. Approaches to Simulation 3: Exploring features of space and time in simulations • 5 – practical: Exploring features of space and time in simulations

6. Approaches to Simulation 4: Interactions between Individuals • 6 – practical: Exploring features of interactions in simulations

7. Modelling in Practice 1: Simulations for Real-World Scenarios • 7 – practical: Exploring calibration and parameterisation of models

8. Modelling in Practice 2: Simulations Fit for Purpose • 8 – practical: Exploring verification, validation, and scenario analysis in modelling and simulation

9. Summary and Recap • 9 – practical: Demonstration of toy models by students in groups

Learning outcomes

By the end of the module, students should be able to:

• By the end of this module, students should be able to: Apply interdisciplinary perspectives to the study, development, and application of computational models and simulations to explore complex systems.
• Approach problem solving using modelling and simulation, with sound understanding about relevant theoretical issues and practical challenges.
• Learn to use different computational approaches in different stages of the iterative modelling and simulation cycle (e.g., conceptualisation, design, operationalisation, development, calibration, verification, validation, scenario analysis, and interpretation of results).
• Understand the suitability of different modelling and simulation methods for different types of problems.
• Pursue modelling and simulation as a systematic and inherently collaborative and interdisciplinary activity.

Indicative reading list

Gilbert, N., & Troitzsch, K. (2005). Simulation for the social scientist. McGraw-Hill Education (UK).

Morgan, M. S., & Morrison, M. (Eds.). (1999). Models as mediators: Perspectives on natural and social science (No. 52). Cambridge University Press.

Gilbert, N. (2008). Advances in agent-based modeling. In Agent-Based Models (pp. 69-74). SAGE Publications, Inc., https://doi.org/10.4135/9781412983259

Calder, M., Craig, C., Culley, D., De Cani, R., Donnelly, C. A., Douglas, R., ... & Wilson, A. (2018). Computational modelling for decision-making: where, why, what, who and how. Royal Society open science, 5(6), 172096.

Turkle, S. (2009). Simulation and its discontents. MIT press.

Knuuttila, T. (2011). Modelling and representing: An artefactual approach to model-based representation. Studies in History and Philosophy of Science Part A, 42(2), 262-271.

Edwards, Paul N. A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming. Cambridge: The MIT Press, 2010.

Miller, J. H., & Page, S. E. (2009). Complex adaptive systems: an introduction to computational models of social life: an introduction to computational models of social life. Princeton university press.

Smaldino, P. (2023). Modeling social behavior: Mathematical and agent-based models of social dynamics and cultural evolution. Princeton University Press.

Research element

Students will develop the knowledge and skills to create and use computational models and simulations based on theory and data, and to interpret results and communicate findings in appropriate ways to address clearly defined problems in different domains.

Interdisciplinary

The module is fundamentally interdisciplinary. Societies have systems comprised of heterogenous interacting entities which causes the systems to display characteristics and behaviours that are more intricate than the sum of the parts. Such systems are known as complex systems, examples of which include ecosystems, markets, and networks. By using computational modelling and simulation, we can create virtual replicas of these complex systems, which then allows us to study their features, behaviour, and how they evolve over time.

Subject specific skills

(a) Subject Knowledge and Understanding: • Understand and critically reflect on the characteristics of complex systems (emergence, path dependence, non-simple interactions between autonomous entities, feedback, etc.) • Understand and critically reflect on the suitability of models and simulations to study complex systems. • Understand and apply relevant data-driven and AI techniques at various stages of the modelling cycle (e.g., conceptualisation, design, operationalisation, development, calibration, verification, validation, scenario analysis, interpretation of results, and communication of findings) • Engage in critical reflection on issues such as reasoning, decision-making, and forecasting based on models and simulations. • Critically examine the role of models and simulations in a range of knowledge practices. • Analyse philosophical or theoretical issues arising from models and simulations. • Apply multiple disciplinary perspectives to the study of models and simulations.

(b) Subject-Specific/Professional Skills: • Practical understanding of the development and application of data-driven models and simulation • Understanding and applying relevant methods at various stages of the modelling cycle (e.g., conceptualisation, design, operationalisation, development, calibration, verification, validation, scenario analysis, interpretation of results, and communication of findings) • Problem solving using models and simulation

Transferable skills

(a) Key Skills • Think critically, creatively, and independently in relation to a particular thematic area of the student’s choice. • Meet regular deadlines. • Demonstrate time-management skills. • Demonstrate problem solving skills. • Demonstrate independent learning skills. • Participate in class discussions. • Demonstrate and practice presentation skills. • Present and report on group discussions. • Experience and participate in both individual and team-based activities.

(b) Cognitive Skills • Evaluate, interpret, and judge core subject related material critically. • Develop and demonstrate independent thinking. • Apply concepts gained in one area to another area. • Take initiative in their own learning. • Design and develop a structured essay plan. • Think critically about a particular area of study. • Systematically evaluate and synthesise research in a particular area of study. • Demonstrate their learning through written and structured essays.