Artificial Life Journal Special Issue: Agent-Based Modelling of Human Behaviour
Call for papers
IMPORTANT DATES
Expression of Interest: 1 Nov 2021
Paper Submission Deadline: 11 March 2022
For expressions of interest, please send an email to Soo Ling Lim at [log in to unmask] with a provisional title of your paper.
The Special Issue seeks to bring together ideas, approaches, concepts, and perspectives from agent-based modelling and human social systems. The aim of the SI is to bring together researchers from these connected fields, to engage across the disciplines, to inform of latest findings, to transfer discoveries and concepts from one field to another, and to inspire new ideas and new collaborations across the theme. Contributions will be invited in the following areas:
* Agent-based modelling of human behaviour and organisational behaviour
* ALife models of human personality and emotions
* ALife models of human communication, trust, conflict, and conflict resolution
* ALife models of collaboration, cooperation, competition
* Agent-based modelling of economic paradigms such as negotiation and bargaining, games, auctions, markets
* Agent-based modelling of location behaviour, spatial patterns, geographical systems, urban evacuation, driver route choices, traffic flows, transport logistics
* Agent-based modelling of human systems such as smart grids, app stores, economies, energy networks
* ALife models of the emergent effect and propagation of communication in human systems and social networks
* Use of agent-based modelling to evaluate or understand existing findings in behavioural science and psychology
* Incentives, reward structures, reinforcement learning
* Collective intelligence, group performance, teamwork, coalition, distributed problem solving
* Social simulation, interactive simulation and emergent behaviour
* The use of agent-based modelling to understand complex systems such as spread of diseases, climate change
This Special Issue aims to bring together a collection of high quality research in using agent-based modelling to understand human behaviour. Agent-based modelling has a long history of success in many related fields from economics and cooperative behaviours, to social conflict, civil violence and revolution. It is a combination of computational modelling, social science and behavioural science, which is a growing area of research.
The aim is to improve our understanding of collective human behaviour and address significant issues that are affecting the human population today, such as climate change, pandemic and misinformation. Alife models offer the capability to create realistic laboratories for which to conduct experiments and progress our understanding in the area.
We aim to encourage Artificial Life practitioners to use behavioural modelling to assess, challenge or even replace competing theories of human behaviour. We anticipate that by introducing rigorous computational modelling to this contentious area, it will help strengthen the field of behavioural sciences as well as bring new understanding to complex human behaviours.
GUEST EDITORS
Dr Soo Ling Lim
Department of Computer Science
University College London (UCL)
Professor Peter J. Bentley
Department of Computer Science
University College London (UCL)
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