<html xmlns:v="urn:schemas-microsoft-com:vml" xmlns:o="urn:schemas-microsoft-com:office:office" xmlns:w="urn:schemas-microsoft-com:office:word" xmlns:m="http://schemas.microsoft.com/office/2004/12/omml" xmlns="http://www.w3.org/TR/REC-html40"> <head> <meta http-equiv="Content-Type" content="text/html; charset=utf-8"> <meta name="Generator" content="Microsoft Word 15 (filtered medium)"> <style></style> </head> <body lang="EN-GB" link="blue" vlink="purple"> <div class="WordSection1"> ***Apologies for cross-posting.***<o:p></o:p> <o:p> </o:p> Dear colleagues,<o:p></o:p> <o:p> </o:p> If you are working with qualitative data and agent-based modelling, please consider submitting your research (poster, extended abstract, full paper) to the special track “Using qualitative data to inform behavioural rules in agent-based models”, which is described below the signature and is to be organized during the <a href="https://ssc2022.behavelab.org/">Social Simulation Conference 2022</a>, 12-16.09, Milan, Italy (hybrid).<o:p></o:p> <o:p> </o:p> Important dates: <a href="https://ssc2022.behavelab.org/submissions/">https://ssc2022.behavelab.org/submissions/</a><o:p></o:p> <o:p> </o:p> All the best,<o:p></o:p> <o:p> </o:p> Melania and Bruce.<o:p></o:p> _______<o:p></o:p> <o:p> </o:p> Melania Borit, Ph.D.<o:p></o:p> <o:p> </o:p> Associate professor,<o:p></o:p> Leader Research Group <a href="https://uit.no/research/craft">CRAFT</a> – Knowledge Integration and Blue Futures<o:p></o:p> <o:p> </o:p> Norwegian College of Fishery Science,<o:p></o:p> Faculty of Biosciences, Fisheries and Economics,<o:p></o:p> UiT The Arctic University of Norway (University of Tromsø),<o:p></o:p> Tromsø, Norway.<o:p></o:p> <o:p> </o:p> E-mail: <a href="mailto:melania.borit@uit.no">melania.borit@uit.no</a><o:p></o:p> Web: <a href="http://uit.no/ansatte/organisasjon/ansatte/person?p_document_id=139029&p_dimension_id=88166">uit.no/melania.borit</a><o:p></o:p> <o:p> </o:p> _____<o:p></o:p> <o:p> </o:p> Title of the special track: Using qualitative data to inform behavioral rules in agent-based models<o:p></o:p> <o:p> </o:p> Organised: <a href="http://www.essa.eu.org/">ESSA</a> Special Interest Group <a href="http://cfpm.org/qual2rule/essa-sig/">Qual2Rule</a><o:p></o:p> <o:p> </o:p> Session chairs: <o:p></o:p> Melania Borit, CRAFT Research Lab, UiT The Arctic University of Norway, Norway<o:p></o:p> Bruce Edmonds, Centre for Policy Modelling, Manchester Metropolitan University, UK<o:p></o:p> <o:p> </o:p> Description:<o:p></o:p> Many academics consider qualitative evidence (e.g. texts gained from transcribing oral data 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 behavior of individuals and is thus a natural source for behaviors to inform the specification of corresponding agent behavior within simulations. The texts will not give a complete picture, but will provide some of “menu” of behaviors people use. During this session we hope to further the understanding of how to improve this. We are particularly interested in accounts of the procedures or structures people used to bridge between qualitative and formal realms based in reported modelling experiences. Thus, those interested to present their work in this session have to make sure that their submission explicitly addresses the use of qualitative data in their modelling endeavour. The session is open to all approaches (full papers or extended abstracts; see details <a href="https://ssc2022.behavelab.org/submissions/">here</a>) that seek to move from qualitative evidence towards a simulation in a systematic way. These include, but are not limited to:<o:p></o:p> <o:p> </o:p> * 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, pointing out assumptions and dangers. * Examples of where this approach has been tried.<o:p></o:p> <o:p> </o:p> <div> <div> <o:p> </o:p> </div> </div> </div> </body> </html>