WEDNESDAY, 17 March

Day 3 – WEDNESDAY, March, 17

TRACK ONE

Transformative social innovation

Gary Polhill, The James Hutton Institute
Patrycja Antosz
Wander Jager
Roman Seidl
Tatiana Filatova

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09:30– 11:00

This workshop will discuss the challenges and potential benefits of modelling ‘Transformative Social Innovation’: “social innovation that challenges, alters or replaces dominant institutions in the social context,” and ‘Social Innovation’: “new ways of doing, organising, framing and knowing” (Avelino et al. 2019). Social innovation poses particular challenges to agent-based modeling through the potential requirement for agents to innovate the novelty the process entails. Making behavioral theory computable is discussed as supporting the simulation of social innovation dynamics. Social innovation (transformative or otherwise) is also expected and theorized to play a critical role in transitioning societal systems such as neighborhoods and local communities to sustainable ways of living. When agent-based modeling is applied to the simulation of social innovations, some of these expectations and theorizations could be evaluated more comprehensively and systematically,.

TRACK TWO

Using games in the field – A method to inform agent-based models

Juan Ocampo, Lund University, Sweden

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09:30 – 11:00

The session will consist mainly on the simulation of a research design that combines a storytelling game and an ABM. In this interactive part of the session, assistants will play the roles of struggling merchants and imagine, create, and tell their market stories. The session will continue with a discussion about the Community Currency storytelling game, the ABM, the methodological setup, lessons learned from the simulation and how to further the exploration of Community Currencies.

11:00 – 11:15

Break

 

TRACK ONE

Transformative social innovation

Gary Polhill, The James Hutton Institute
Patrycja Antosz
Wander Jager
Roman Seidl
Tatiana Filatova

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11:15 – 12:45

This workshop will discuss the challenges and potential benefits of modelling ‘Transformative Social Innovation’: “social innovation that challenges, alters or replaces dominant institutions in the social context,” and ‘Social Innovation’: “new ways of doing, organising, framing and knowing” (Avelino et al. 2019). Social innovation poses particular challenges to agent-based modeling through the potential requirement for agents to innovate the novelty the process entails. Making behavioral theory computable is discussed as supporting the simulation of social innovation dynamics. Social innovation (transformative or otherwise) is also expected and theorized to play a critical role in transitioning societal systems such as neighborhoods and local communities to sustainable ways of living. When agent-based modeling is applied to the simulation of social innovations, some of these expectations and theorizations could be evaluated more comprehensively and systematically,.

TRACK TWO

Microsimulation for Health Modelling

Nik Lomax, University of Leeds
Chris Wu, University of Leeds
Alison Heppenstall, University of Leeds

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11:15 – 12:45

This session discusses and demonstrates the utility of an attribute rich individual UK level population created using spatial microsimulation techniques. The population has been created from a combination of census and survey data sources. The population has been created for public health modelling, but can be potentially used for a number of application areas where a detailed population is required. By the end of the session participants will understand what the dataset contains, how it was created and how it could be adapted for use in other research contexts.

Organizing committee:
Melania Borit, UiT The Arctic University of Norway
Gary Polhill, The James Hutton Institute
Geeske Scholz, Osnabrück University
Timo Szczepanska, UiT The Arctic University of Norway
Harko Verhagen, Stockholm University
Nanda Wijermans, Stockholm University

12.45 – 13:30

Lunch Break

 

13:30 – 15:00

Quantifying the uncertainty in agent-based models
(aka what the *$&^% is going on in my model and why?)

Agent-based modelling is maturing as a method for capturing and simulating individual behaviour and activity. Whilst there are a dazzling array of applications appearing in the literature, there is less work that focusses on important methodological issues such as the handling of uncertainty in these models. We discuss (and demonstrate) how approaches from the field of Uncertainty Quantification can be adapted for use in agent-based models so that models can become robust enough to be used in important policy decisions.

15:00– 15:15

Break

 

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15:15 – 16:45

TBC

SOCIAL EVENT

TBC