TUESDAY, 16 March
Day 2 – Tuesday March, 16 (CET)
TRACK ONE
Simulation in times of crisis
Amineh Ghorbani, Delft University of Technology, the Netherlands
Frank Dignum, Umeå University, Sweden
Harko Verhagen, Stockholm University, Sweden
Fabian Lorig, Malmö University, Sweden
Paul Davidsson, Malmö University, Sweden
Lois Vanhee, Umeå University, Sweden
09:30 – 11:00
During the COVID-19 pandemic a group of self-driven researchers came together to build an agent-based model named ASSOCC that could contribute to the control of this devasting world crisis. The model was a great success in providing societal and policy insights, but perhaps a more important outcome was the learning process and the hurdles that were not known beforehand. The goal of this workshop is to share lessons learned by all researchers that have been involved in modelling the COVID-pandemic to make our social simulation community more prepared for such events in the future. Challenges that will be addressed include: How to build fast development of empirically grounded simulation model of human behaviour, How to build general models of human decision-making and behaviour that can easily be adapted and integrated. We will have two sessions, each one divided into two 45 minute parts, staring with a 10 minute presentation followed by breakout rooms on a specific challenge. We will use Mural to discuss collectively and build content for further publication/research.
TRACK TWO
Sensitivity Analysis Made Easy with the EMA Workbench
Mikhail Sirenko, Delft University of Technology, the Netherlands
Patrick Steinmann, Wageningen University & Research, the Netherlands
Raphaël Klein, Delft University of Technology, the Netherlands
09:30 – 11:00
Agent-based models are a common tool for studying social and socio-environmental systems. Such models are never perfect surrogates of the target system – facing incomplete knowledge or unresolvable uncertainties, modellers must resort to simplifications and hypotheses. But what are the consequences of these unverifiable assumptions on the model’s behaviour? One way to deal with these problems is Sensitivity Analysis (SA). In this session, we will first do a quick review of the SA theory and then dive into the practice of it. We will work on a case model Virus on a Network with Mesa, EMA Workbench and SALib Python packages. By the end of this session, you will be able to perform a simple SA and be aware of the main approaches and their drawbacks.
11:00 – 11:15
Break
TRACK ONE
Simulation in times of crisis
Amineh Ghorbani, Delft University of Technology, the Netherlands
Frank Dignum, Umeå University, Sweden
Harko Verhagen, Stockholm University, Sweden
Fabian Lorig, Malmö University, Sweden
Paul Davidsson, Malmö University, Sweden
Lois Vanhee, Umeå University, Sweden
11:15 – 12:45
During the COVID-19 pandemic a group of self-driven researchers came together to build an agent-based model named ASSOCC that could contribute to the control of this devasting world crisis. The model was a great success in providing societal and policy insights, but perhaps a more important outcome was the learning process and the hurdles that were not known beforehand. The goal of this workshop is to share lessons learned by all researchers that have been involved in modelling the COVID-pandemic to make our social simulation community more prepared for such events in the future. Challenges that will be addressed include: How to build fast development of empirically grounded simulation model of human behaviour, How to build general models of human decision-making and behaviour that can easily be adapted and integrated. We will have two sessions, each one divided into two 45 minute parts, staring with a 10 minute presentation followed by breakout rooms on a specific challenge. We will use Mural to discuss collectively and build content for further publication/research.
TRACK TWO
Sensitivity Analysis Made Easy with the EMA Workbench
Mikhail Sirenko, Delft University of Technology, the Netherlands
Patrick Steinmann, Wageningen University & Research, the Netherlands
Raphaël Klein, Delft University of Technology, the Netherlands
11:15 – 12:45
Agent-based models are a common tool for studying social and socio-environmental systems. Such models are never perfect surrogates of the target system – facing incomplete knowledge or unresolvable uncertainties, modellers must resort to simplifications and hypotheses. But what are the consequences of these unverifiable assumptions on the model’s behaviour? One way to deal with these problems is Sensitivity Analysis (SA). In this session, we will first do a quick review of the SA theory and then dive into the practice of it. We will work on a case model (Virus on a Network) with Mesa, EMA Workbench and SALib Python packages. By the end of this session, you will be able to perform a simple SA and be aware of the main approaches and their drawbacks.
12.45 – 13:30
Lunch Break
TRACK ONE
The Lab in the Model, the Model in the Lab: The New Frontiers of Experimental and Computational Research on Social Behaviour
13:30 – 15:00
13:30 – 14:00 The lab in the model, the model in the lab: An introduction
Andreas Flache (University of Groningen, NL)
William Rand (NC State University, USA)
Flaminio Squazzoni (University of Milan, Italy)
14:00 – 14:30 Data-driven reactive and cognitive agent models replicate differently a long-term experiment on social norms and cooperation under risk
Giulia Andrighetto
Mario Paolucci (CNR, Rome, Italy)
Guillaume Deffuant
Omid Roozmand (Université Clermont Auvergne, France)
14:30-15:00 From the lab to the model: Exploring the dynamics of a social norm of honesty in a participative budgeting setting
Lucia Bellora-Bienengräber (University of Groningen, NL)
Kai G. Mertens
Matthias Meyer (Hamburg University of Technology, Germany)
visit us and learn more
TRACK TWO
Agent-based modeling in Python with Agentpy (tutorial)
Joël Foramitti, Institute of Environmental Science and Technology, Universitat Autònoma de Barcelona, Spain. Institute for Environmental Studies, Vrije Universiteit Amsterdam, The Netherlands.
13:30– 15:00
Agentpy is an open-source library for the development and analysis of agent-based models in Python. The framework integrates the tasks of model design, numerical experiments, and data analysis within a single environment, and is optimized for interactive computing with IPython and Jupyter. This workshop will provide a guided tutorial on how to build an agent-based model with the agentpy package. Participants will learn how to design their own model, run an experiment with multiple iterations, visualize their output, and perform a sensitivity analysis.
15:00– 15:15
Break
TRACK ONE
The Lab in the Model, the Model in the Lab: The New Frontiers of Experimental and Computational Research on Social Behaviour
15:15– 16:45
15:15-15:45 When laboratory experiment confirms agent based simulations: Positivity bias without self-enhancement, Guillaume Deffuant, T. Roubin, Silvye Huet, A. Nugier, S. Guimond (Université Clermont Auvergne, France)
15:45-16:15 Micro-foundations of polarization In online social media: A model In the lab fed back into the model
Marijn A. Keijzer (University of Groningen, NL)
Michael Mäs (Karlsruhe Institute of Technology, Germany)
Andreas Flache (University of Groningen, N)
16:15-16:45 Discussion
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visit us and learn more
TRACK TWO
Agent-based modeling in Python with Agentpy (tutorial)
Joël Foramitti, Institute of Environmental Science and Technology, Universitat Autònoma de Barcelona, Spain. Institute for Environmental Studies, Vrije Universiteit Amsterdam, The Netherlands.
15:15 – 16:45
Agentpy is an open-source library for the development and analysis of agent-based models in Python. The framework integrates the tasks of model design, numerical experiments, and data analysis within a single environment, and is optimized for interactive computing with IPython and Jupyter. This workshop will provide a guided tutorial on how to build an agent-based model with the agentpy package. Participants will learn how to design their own model, run an experiment with multiple iterations, visualize their output, and perform a sensitivity analysis.