Geosimulations for Addressing Societal Challenges @ AAG 2024

At the upcoming American Association of Geographers (AAG) Annual Meeting in Honolulu, Hawaii, we (Alison Heppenstall, Na (Richard) Jiang, Gary Polhill, Andrew Crooks, Raja Sengupta, Suzana Dragicevic, Sarah Wise, Jeon-Young Kang) have organized 3 sessions around the theme of Geosimulations for Addressing Societal Challenges. This is part of the 10th Anniversary Symposium on Human Dynamics Research. If you are at the AAG on Tuesday the 16th of April and have the time it would be great if you could stop by and see the talks. Details are below.

Sessions Abstract: 

There is an urgent need for research that promotes sustainability in an era of societal challenges ranging from climate change, population growth, aging and wellbeing to that of pandemics. These need to be directly fed into policy. We, as a Geosimulation community, have the skills and knowledge to use the latest theory, models and evidence to make a positive and disruptive impact. These include agent-based modeling, microsimulation and increasingly, machine learning methods. However, there are several key questions that we need to address which we seek to cover in this session. For example, What do we need to be able to contribute to policy in a more direct and timely manner? What new or existing research approaches are needed? How can we make sure they are robust enough to be used in decision making? How can geosimulation be used to link across citizens, policy and practice and respond to these societal challenges? What are the cross-scale local trade-offs that will have to be negotiated as we re-configure and transform our urban and rural environments? How can spatial data (and analysis) be used to support the co-production of truly sustainable solutions, achieve social buy-in and social acceptance? And thereby co-produce solutions with citizens and policy makers.

Session 1 (Date: 4/16/2024; Time: 10:40 AM - 12:00 PM; Room: 312 (Ni`ihua), Third Floor, Hawai'i Convention Center)

Chair: Na (Richard) Jiang

Presentions:

• Bright Addae and Suzana Dragicevic,
• Title: Spherical geosimulation model for multi land-use/land-cover change
• Hui-Chun Lee and Tzai-Hung Wen:
• Title: Building Player-Based Model in Multiplayer Online Games –– the Impact of Exposure Networks on Vaccine Decisions
• Sean C Ahearn,
• Title: Generative deep learning: next steps in movement simulation/analysis?
• Alison Heppenstall, Gary Polhill, Mike Batty, Ricardo Colsanti, Matt Hare, Richard Milton and Doug Salt,
• Title: Exascale agent-based modelling for policy evaluation in real-time (ExAMPLER)
• Jacqueline Kazil, Boyu Wang and Thomas Pike,
• Title: Mesa, agent-based modeling in Python

Session 2 (Date: 4/16/2024; Time: 1:20 PM - 2:40 PM;  Room: 312 (Ni`ihua), Third Floor, Hawai'i Convention Center)

Chair: Suzana Dragicevic

Presentions:

• Yenny Andrea Cuellar Roncancio and Liliana Perez,
• Title: Geosimulation for Urban Wetland Preservation in Latin America
• Farzin Lotfi-Jam and Jenni Minner
• Title: Agent-Based Modeling and 3D Visualization for Building Preservation, Deconstruction, and Reuse in Ithaca
• Duo Zhang, Raja Sengupta and Antonia Gieschen
• Title: Multi-Agent Based Modeling of Retail Choices in Montreal: Integrating Offline-to-Online Reinforcement Learning on Synthetic Population
• Na (Richard) Jiang and Andrew Crooks
• Title: Populating Digital Twins with Humans: A Framework Utilizing Artificial Agents
• Yuan Feng, Ying Miao, Ed Turner and Yu Jia
• Title: Engaging Participatory Methods for Agent-Based Modelling of Residential Heat Network Uptake: A Case Study with an Energy Services Co-operative in Brighton and Hove, England, UK

Session 3 (Date: 4/16/2024; Time: 3:00 PM - 4:20 PM;  Room: 312 (Ni`ihua), Third Floor, Hawai'i Convention Center)

Chair: Alison Heppenstall

Presentions:

• Fuzhen Yin, Na (Richard) Jiang and Andrew Crooks
• Title: Modeling Covid Vaccination uptake in New York State: An agent-based modeling perspective.
• David O'Sullivan, Adam Daigneault and Fraser Morgan
• Title: A spatially explicitly agent-based model of on-farm environmental interventions
• Alysha van Duynhoven and Suzana Dragicevic
• Title: Mitigating imbalanced geospatial data for Deep Learning in geosimulation models
• Liliana Perez, Nico Coallier, Yenny Cuellar and Julien Vadnais
• Title: Climate Change, Bees and AI: Giving a Voice to the Bees!

Addressing equifinality in agent-based modeling

In the past we have blogged about the challenges of agent-based modeling but one thing we have not written much about is the challenge of uncertainty especailly when it comes to model calibration. This uncertainty is a challenge when it when it comes to situations where various parameter sets fit observed data equally well. This is known as equifinality which is a principle or phenomenon in system theory that implies that different paths can lead to the same final state or outcome. 

In a new paper with paper with Moongi Choi, Neng Wan, Simon Brewer, Thomas Cova and Alexander Hohl entitled "Addressing Equifinality in Agent-based Modeling: A Sequential Parameter Space Search Method Based on Sensitivity Analysis" we explore this issue. More specifically we introduce an Sequential Parameter Space Search (SPS) algorithm to confront the equifinality challenge in calibrating fine-scale agent-based simulations with coarse-scale observed geospatial data, ensuring accurate model selection using a pedestrian movement simulation as a test case.  

If this sounds of interest and you want to find out more, below you can read the abstract to the paper, see the logic of our simulation and some of the results. At the bottom of the page, you can find a link to the paper along with its full reference. Furthermore, Moongi has made the data and codes for indoor pedestrian movement simulation and Sequential Parameter Space search algorithm openly available at https://zenodo.org/doi/10.5281/zenodo.10815211 and https://zenodo.org/doi/10.5281/zenodo.10815195.

Abstract 

This study addresses the challenge of equifinality in agent-based modeling (ABM) by introducing a novel sequential calibration approach. Equifinality arises when multiple models equally fit observed data, risking the selection of an inaccurate model. In the context of ABM, such a situation might arise due to limitations in data, such as aggregating observations into coarse spatial units. It can lead to situations where successfully calibrated model parameters may still result in reliability issues due to uncertainties in accurately calibrating the inner mechanisms. To tackle this, we propose a method that sequentially calibrates model parameters using diverse outcomes from multiple datasets. The method aims to identify optimal parameter combinations while mitigating computational intensity. We validate our approach through indoor pedestrian movement simulation, utilizing three distinct outcomes: (1) the count of grid cells crossed by individuals, (2) the number of people in each grid cell over time (fine grid) and (3) the number of people in each grid cell over time (coarse grid). As a result, the optimal calibrated parameter combinations were selected based on high test accuracy to avoid overfitting. This method addresses equifinality while reducing computational intensity of parameter calibration for spatially explicit models, as well as ABM in general. 

Keywords: Agent-based modeling equifinality calibration sequential calibration approach sensitivity analysis.

Detail model structures and process of the simulation.

Pedestrian simulation ((a) Position by ID, Grouped proportion – (b) 0.1, (c) 0.5, (d) 0.9).

Multiple sub-observed data ((a) # grid cells passed by each individual, (b) # individuals in 1x1 grid, (c) # individuals in 2x2 grid cells).

Validation results with train and test dataset ((a) Round 1, (b) Round 2, (c) Round 3).

Full Reference: 

Choi, M., Crooks, A.T., Wan, N., Brewer, S., Cova, T.J. and Hohl, A. (2024), Addressing Equifinality in Agent-based Modeling: A Sequential Parameter Space Search Method Based on Sensitivity Analysis, International Journal of Geographical Information Science. https://doi.org/10.1080/13658816.2024.2331536. (pdf)