This blog is the official mouthpiece of the European Social Simulation Association‘s Special Interest Group on this topic.

Many academics consider qualitative evidence (e.g. texts gained from transcribing someone talking or observations of people) and quantitative evidence to be incommensurable.  However agent-based simulations are a possible vehicle for bridging this gap.  Narrative textual evidence often gives clues as to the in-context behaviour of individuals and is thus a natural source for behaviours to inform the specification of corresponding agent behaviour within simulations.  They will not give a complete picture of this, but they will provide some of “menu” of behaviours that people use.  Once specified, a simulation model can be run and the results measured – cross validating the result (Moss & Edmonds 2005).  The simulation provides the concrete instantiation of the connection between the mico-level behaviours and macro-level outcomes.  Thus such simulations are a well-founded route to integrating qualitative and quantitative evidence.

Some modellers have used informal conversations to inform their modelling (e.g. Moss 1998), but a key advance came with the theses of Taylor (2003) and Bharwani (2004).  They both transcribed interviews into textual records and then tried to systematically analyse those transcripts to inform their simulation specification.  Although the process of analysing natural language data is never going to be a completely formal or automatic process, relying as it does upon the understanding of the interviewer and/or analyst, formalising the “process” makes it more transparent and replicable.  The data can be made available and examined by subsequent researchers who may spot aspects that the original observer has missed, as well as seeing how the sense of the stakeholder might have been changed during the process.  Unlike an informal approach, which does not work from transcribed text, a subsequent researcher can follow the chain of analysis and understand it better.

Qual2Rule Diagram
In this SIG we hope to further understanding of how to do this better.  It is open to all approaches that seek to move from qualitative evidence towards a simulation in a systematic way.  This includes, but is not limited to:

*  Approaches based in Grounded Theory.
*  Tools for facilitating such a process.
*  Participatory processes that result in a simulation.
*  Frameworks for aiding the analysis of text into rules.
*  Elicitation techniques that would aid the capture of information in an appropriate structure.
*  Models and ideas from psychology to aid in the above process.
*  Insights and tools from Natural Language processing that may help this process.
*  Agent architectures that will facilitate the programming of agents from such analyses.
*  Philosophical or Sociological critiques of this project, pointing out assumptions and dangers.
*  Examples of where this approach has been tried.

Activities of this group of people have included: an informal workshop in Manchester in September 2012 (, special tracks on this topic at the ESSA Social Simulation Conferences 2013-2019, some of which are on-line ( and others published in the Springer book “Advances in Social Simulation” (, two workshops on integrating qualitative and quantitative eveidence (April 2019, the Netherlands; November 2019 Manchester),  and of course this blog.

We plan to organise special tracks at future conferences, to post any relevant news of papers, events here, organise a special issue in a specialty journal, and any other activities that its members suggest/organise.

For all queries associated with this SIG (for example wishing to join, or wanting the ability to post to this blog) please contact the SIG organiser: Melania Borit, UiT The Arctic University of Norway, Norway.


* Bhawani, S. (2004) Adaptive Knowledge Dynamics and Emergent Artificial Societies: Ethnographically Based Multi-Agent Simulations of Behavioural Adaptation in Agro-Climatic Systems. Doctoral Thesis, University of Kent, Canterbury, UK.

* Moss, S. (1998) Critical Incident Management: An Empirically Derived Computational Model. Journal of Artificial Societies and Social Simulation 1(4):1.

* Taylor, R.I. (2003). Agent-Based Modelling Incorporating Qualitative and Quantitative Methods: A Case Study Investigating the Impact of E-commerce upon the Value Chain. Doctoral Thesis, Manchester Metropolitan University, Manchester, UK.

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