Friday, April 09, 2021

Agent-Based Modeling and the City

Turning our attention back to agent-based modeling, in the recently open access edited volume by Wenzhong Shi, Michael Goodchild, Michael Batty, Mei-Po Kwan and Anshu Zhang entitled Urban Informatics, Alison Heppenstall, Nick Malleson, Ed Manley and myself have a chapter entitled "Agent-Based Modeling and the City: A Gallery of Applications.

In the chapter we discuss cities through the lens of complex systems comprised of composed of people, places, flows, and activities. Moreover, we make the argument that as cities contain large numbers of discrete actors interacting within space and with other systems from nature, predicting what might happen in the future is a challenge. We base this argument on the fact that human behavior cannot be understood or predicted in the same way as in the physical sciences such as physics or chemistry. The actions and interactions of the inhabitants of a city, for example, cannot be easily described in in a physical science theory such as that of Newton’s Laws of Motion. This notion is captured quite aptly by a quote by Nobel laureate Murray Gell-Mann: “Think how hard physics would be if particles could think.” Building on these arguments we introduce readers to agent-based modeling as it offers a way to explore the processes that lead to patterns we see in cities from the bottom up but also allows us to incorporate ideas from complex systems (e.g., feedbacks, path dependency, emergence) along with providing a gallery of applications of geographically explicit agent-based models. 

We then discuss how agent-based models can incorporate various decision-making processes within them and  how we can integrate data within such models with a specific emphasis on geographical and social information. This leads us to a discussion on how agent-based modelers are utilizing machine learning (such as genetic algorithms, artificial neural networks, Bayesian classifiers, decision trees, reinforcement learning, to name but a few) and data mining (i.e. finding patterns in the data) within their models: from the design of the model, the execution of the model to that of the evaluation of the model. Finally,  we conclude the chapter with a summary and discuss new opportunities with respect to agent-based modeling and the city. One such opportunity is dynamic data assimilation which could be transformative for the ways that some systems, for example “smart” cities, are modeled. Our argument is that agent-based models are often used to simulate the behavior of complex systems, these systems often diverge rapidly from initial starting conditions. One way to prevent a simulation from diverging from reality would be to occasionally incorporate more up-to-date data and adjust the model accordingly (i.e., data assimilation). Data, especially streaming data produced through near real time observational datasets (e.g., social media, vehicle routing counters) could be utilized in such a case. If what we have written above is of interest, below we provide the abstract to chapter along with some figures which we use to illustrate some key points or concepts (such as dynamic data assimilation). Finally at the bottom post, we provide the full reference and a link to the chapter.

Agent-based modeling is a powerful simulation technique that allows one to build artificial worlds and populate these worlds with individual agents. Each agent or actor has unique behaviors and rules which governs their interactions with each other and their environment. It is through these interactions that more macro phenomena emerge: for example, how individual pedestrians lead to the emergence of crowds. Over the last two decades, with the growth of computational power and data, agent-based models have evolved into one of the main modeling paradigms for urban modeling and for understanding the various processes which shape our cities. Agent-based models have been developed to explore a vast range of urban phenomena from that of micro-movement of pedestrians over seconds to that of urban growth over decades and many other issues in between. In this chapter we will introduce readers to agent-based modeling from simple abstract applications to those representing space utilizing geographical data not only for the creation of the artificial worlds but also for the validation and calibration of such models through a series of example applications. We will then discuss how big data, data mining, and machine learning techniques are advancing the field of agent-based modeling and demonstrate how such data and techniques can be leveraged into these models, giving us a new way to explore cities.

Key Words: Agent-based Modeling, Geographical Information Systems, Machine Learning, Urban Simulation.
Using geographical information as a foundation for artificial worlds.
A selection of GeoMason models across various spatial and temporal scales.
Dynamic data assimilation and agent-based modeling.

Full Reference:
Crooks, A.T., Heppenstall, A., Malleson, N. and Manley, E. (2021), Agent-Based Modeling and the City: A Gallery of Applications, in Shi, W., Goodchild, M., Batty, M., Kwan, M.-P., Zhang, A. (eds.), Urban Informatics, Springer, New York, NY, pp. 885-910. (pdf)