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

Flaminio Squazzoni
Andreas Flache
Bill Rand

}

13:30 – 15:00

This workshop aims to present research that integrates experimental and computational approaches to examine the emergence of large-scale, complex social patterns from agent interaction. Social norms, collective opinions and institutions are emergent features of agent interaction, and the analysis of these patterns requires in-depth integration of different methods, observation scales and disciplines. While perspectives and findings from different disciplines are essential to understand social behaviour, a unique combination of analytical instruments of detection is necessary to connect composite and plural approaches. The workshop is addressed to scholars in the experimental and social simulation communities who are interested in exploring this integration and discussing different lines for cross-methodological developments such as: a) agent-based models calibrated with experimental data to perform what-if analysis, robustness and sensitivity tests; b) experimental tests of simulation findings to corroborate causal explanations and dissect alternative behavioural explanations; c) use of agent-based models on experimental data to explore counterfactual analysis and optimization analysis; d) full-cycle analysis of lab experiments to computational models and back to lab experiments; e) integration of artificial and human agents in lab/online experiments.

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

Flaminio Squazzoni
Andreas Flache
Bill Rand

}

15:15– 16:45

This workshop aims to present research that integrates experimental and computational approaches to examine the emergence of large-scale, complex social patterns from agent interaction. Social norms, collective opinions and institutions are emergent features of agent interaction, and the analysis of these patterns requires in-depth integration of different methods, observation scales and disciplines. While perspectives and findings from different disciplines are essential to understand social behaviour, a unique combination of analytical instruments of detection is necessary to connect composite and plural approaches. The workshop is addressed to scholars in the experimental and social simulation communities who are interested in exploring this integration and discussing different lines for cross-methodological developments such as: a) agent-based models calibrated with experimental data to perform what-if analysis, robustness and sensitivity tests; b) experimental tests of simulation findings to corroborate causal explanations and dissect alternative behavioural explanations; c) use of agent-based models on experimental data to explore counterfactual analysis and optimization analysis; d) full-cycle analysis of lab experiments to computational models and back to lab experiments; e) integration of artificial and human agents in lab/online experiments.

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.