Kavin Narasimhan

Kavin Narasimhan (Assistant Professor)

My research focuses on developing computational "sandbox" environments that allow policymakers and community stakeholders to explore and test potential social or policy interventions before implementing them in the real world. These tools represent complex systems, such as community-based water management or demand-side energy use, and allow users to adjust different intervention levers (e.g. policy rules, incentives, or behavioural assumptions). By running simulations, users can examine how changes may play out across the system as a whole, including unintended or longer-term effects. By enabling experimentation in silico, this work provides a practical alternative to real-world pilots, which can be costly, time-consuming, or in some cases impossible to carry out. Within this area, I am also interested in critical and participatory approaches to the design and use of computational tools. This work examines how modelling choices, assumptions, and underlying data inevitably simplify reality and may embed biases, omissions, or power asymmetries. By engaging technical experts alongside policymakers, practitioners, and affected communities, I explore how these design decisions shape the results produced by such tools, and what it means to rely on those results when informing real-world policy choices and interventions.

I have a PhD in Computer Science from Queen Mary University of London and Bachelor of Engineering (B.E.) in Computer Science and Engineering from Anna University, India. I was awarded Fellow of The Higher Education Academy (FHEA) in 2021. Between 2015-2023, I was a Research Fellow in the Centre for Research in Social Simulation (CRESS) Link opens in a new windowat the University of Surrey, and worked as a Data Social Scientist - Secondee at the Department for Energy Security and Net Zero (DESNZ) through an ESRC Policy FellowshipLink opens in a new window.

Supervision

I am happy to supervise postgraduate researchers interested in computational social science, social simulation, and computational modelling for public policy. If you're interested in exploring these areas, I encourage you to email me directly, with a 1-page research proposal, so we can discuss about potential collaboration opportunities.

Grants

[2024 - 2025]: Principal Investigator, Complexity Appropriate Participatory Techniques Utilised for Reimagining Energy policy Design (CAPTURED)Link opens in a new window, a Futures Study Project on the UK's green economy transition

[2023]: Co-Investigator, Re-Imagining AI with Afrofuturist speculative design, ESRC Digital Good Network

[2022 - 2023]: Principal Investigator, Digital or Data Social Science Approaches to Net Zero ESRC Policy Fellowship with the Department for Energy Security and Net Zero (DESNZ)

[2022]: National Centre for Research Methods (NCRM) Bursary for Training for attending the Reproducibility Bootcamp.

[2021]: Researcher Co-Investigator in the Natural Environment Research Council funded ‘EMPOWER: Empowering citizen and community adaptation to systemic risks from climate change’Link opens in a new window project

[2021]: ESRC Festival of Social Science (FOSS) funding for ‘Empowering Communities to Fight Climate Change: What is Amiss?’ – an interactive online workshop and public seminar to explore how communities manage external shocks for sustainable resource management

[2020]: Building Research Culture competition funding awarded by the Doctoral College at University of Surrey for organising and running the Surrey Research Dating Forum.

Computational Modelling

[2018-2021]: Led the development of theWATER user associations at the Interface of Nexus Governance (WATERING) modelin theFutureDAMSproject.

[2015 - 2017]: Led the development of theHOuseholds and Practices in Energy-use Scenarios (HOPES) modelin theWholeSEMproject.

Teaching

At CIM, I am the Module Convenor for 24/25 for:

• IM939: Data Science Across Disciplines: Principles, Practice and Critique

and I teach in the following modules:

• IM952: Big Data Research: Hype or Revolution?
• IM949: Data Visualisation in Science, Culture and Public Policy

Publications

• Narasimhan, K.P. (2025). Different Bottom-Up Simulation Methods and Policy Applications: A Short Review. In: Czupryna, M., Kamiński, B., Verhagen, H. (eds) Advances in Social Simulation. ESSA 2021. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-031-91782-0_28Link opens in a new window
• Vanhée L, Borit M, Siebers PO, Cremades R, Frantz C, Gürcan Ö, Kalvas F, Kera DR, Nallur V, Narasimhan K, Neumann M. Large Language Models for Agent-Based Modelling: Current and possible uses across the modelling cycle. arXiv preprint arXiv:2507.05723. 2025 Jul 8. https://doi.org/10.48550/arXiv.2507.05723Link opens in a new window
• Oliver, T. H., Bazaanah, P., Da Costa, J., Deka, N., Dornelles, A. Z., Greenwell, M. P., Nagarajan, M., Narasimhan, K., Obuobie, E., & Osei, M. A. (2023). Empowering citizen-led adaptation to systemic climate change risks. Nature Climate Change, 1–8.
• Johansson,E., Nespeca, V., Sirenko, M., van den Hurk, M., Thompson, J., Narasimhan, K., Belfrage, M., Giardini, F. and Melchior, A. (2023) A Tale of Three Pandemic Models: Lessons Learned for Engagement with Policy Makers Before, During, and After a Crisis. Review of Artificial Societies and Social Simulation, 15 Mar 2023. https://rofasss.org/2023/05/15/threepandemic
• Narasimhan, K., Leoni, S., Luckner, K., Carpentras, D. and Davis, N. (2022) ESSA@work: Reflections and looking ahead. Review of Artificial Societies and Social Simulation, 20 Feb 2023. https://rofasss.org/2022/02/20/essawork
• Narasimhan, K and Gilbert, N. (2022). Reusable Components for an Agent-based Model of Irrigation Management. International Congress on Environmental Modelling and Software. 24. https://scholarsarchive.byu.edu/iemssconference/2022/Online_and_Poster_Presentations/24
• Narasimhan, K., Gilbert, N., & Elsenbroich, C. (2022). WATERING Crop Growth Reusable Building Block (1.0). Zenodo. https://doi.org/10.5281/zenodo.6323653
• Narasimhan, K., Gilbert, N., & Elsenbroich, C. (2022). WATERING Irrigation Reusable Building Block (1.0). Zenodo. https://doi.org/10.5281/zenodo.6323633
• Narasimhan, K., Gilbert, N., & Elsenbroich, C. (2020). An Integrated Model to Assess the Impacts of Dams in Transboundary River Basins. 321–327.
• Gilbert, N., Ahrweiler, P., Barbrook-Johnson, P., Narasimhan, K. P., & Wilkinson, H. (2018). Computational modelling of public policy: Reflections on practice. Journal of Artificial Societies and Social Simulation, 21(1).
• Narasimhan, K., Gilbert, N., Hope, A., & Roberts, T. (2018). Demystifying energy demand using a practice centric agent-based model.
• Narasimhan, K., Roberts, T., Xenitidou, M., Gilbert, N. (2017). Using ABM to Clarify and Refine Social Practice Theory. In: Jager, W., Verbrugge, R., Flache, A., de Roo, G., Hoogduin, L., Hemelrijk, C. (eds) Advances in Social Simulation 2015. Advances in Intelligent Systems and Computing, vol 528. Springer, Cham. https://doi.org/10.1007/978-3-319-47253-9_27
• Narasimhan, K., Roberts, T., & Gilbert, N. (2016). Using agent-based modelling to understand the spread of energy consuming social practices in households. 13–15. http://www.demand.ac.uk/wp-content/uploads/2016/03/DEMAND2016_Full_paper_9-Narasimhan.pdf
• Narasimhan, K. P. (2016). Computational Proxemics: Simulation-based analysis of the spatial patterns of conversational groups. https://qmro.qmul.ac.uk/xmlui/handle/123456789/23843

Science Communication

I enjoy teaching computer science and agent-based modelling courses and actively engage in research outreach initiatives (watch about our research on household energy use and community based water governance on YouTube).