For the undergraduate class, we met twice a week. In the first class of each week I would outline the topic, discuss a specific modeling approach and give a range of sample applications. While the second class of the week was devoted to hands on model development (I chose to use NetLogo for this), in order to cement what was discussed in the first class (a learning by doing if you like.). For example, one week we discussed cellular automata (CA) modeling, where in the first class, I outlined its evolution, its basic proprieties and applications (e.g. from simple voting models to that of urban growth). In the second class, the students then built a CA model from scratch. For the graduate class, more emphasis was placed on theory, critiques of modeling and discussion of key texts.
In both classes, all the students needed to carry out a project where they develop a
computational model that investigates a social science research question.
This exercise is often their first model (especially for the undergraduates) that many students ever create.
Below you can see some of this years models.
"explosive growth of agent modeling over the past two decades in the
social sciences, in business and government, and related areas, and
offer a tour d’horizon of its present state and myriad
applications. Looking forward, we will identify challenges and
opportunities — Hilbert Problems, if you will — to shape the future of
agent-based computational modeling."
Joshua Epstein and Robert Axtell presenting their works.
Josh and Rob each gave really impressive talks entitled “Agent-based modeling: From Napkins to Nations” and "The Adoption of Agent Computing over Time by Social Scientists as Compared to Game Theory and Experimental/ Behavioral Economics" respectively. Which reflected how agent computing has evolved over the last 20 years with plenty of funny anecdotes along the way including references and critiques of their works such as "masculine gods of their cyberspace creations" and where the field is going.
What really impressed me about the congress was the atmosphere. That of like minded individuals from many different disciplines coming together and discussing agent computing, complexity and modeling more generally. Some of this can be seen via photos and tweets of the event.
Alison Heppenstall, Nick Malleson and myslef also participated at the congress with a talk entitled "ABM for Simulating Spatial Systems: How are we doing?" which assessed how has agent-based modeling within the geographical sciences advanced over the last 20 years. Below one can read a brief outline of the talk and a movie of presentation.
Abstract:
While great advances in modeling have been made, one of the greatest challenges we face is that of understanding human behavior and how people perceive and behave in physical spaces. Can new sources of data (i.e. “big data”) be used to explore the connections between people and places? In this paper we will review of the current state of art of modeling geographical systems. We highlight the challenges and opportunities through a series of examples that new data can be used to better understand and simulate how individuals behave within geographical systems.
Heppenstall, A., Crooks A.T. and Malleson, N. (2016), ABM for Simulating Spatial Systems: How are we doing? International Congress on Agent Computing, 29th-30th, November, Fairfax, VA.
The Growth of Geographical ABM (selected examples).
At the 2016The Computational Social Science Society of Americas Conference, Tom Briggs and myself had a paper accepted entitled "Close, But Not Close Enough: A Spatial Agent-Based Model of Manager-Subordinate Proximity". In the paper we present our preliminary effort to explore how workplace layout impacts on subordinates interactions with managers. We developed a spatial agent-based model to simulate how the physical seating locations of individuals with reporting
relationships might enhance or detract from an effective
manager-subordinate relationship. Below you can read the abstract of our paper and find out more information about the model.
Abstract:
Employees may be co-located with their manager or they may be separated by distances ranging from a short walk to across oceans, with many gradations in between. Some distances, such as those between floors of an office building, are physically short but may be psychologically quite far. The current project developed a spatial ABM to examine the likelihood of unplanned manager-subordinate encounters in an office setting with two floors. Early results suggest that subordinates located on a different floor than their manager are substantially less likely to have even a single spontaneous encounter with their manager in a work day, despite a relatively short physical separation. If leader-follower (i.e., manager-subordinate) relationships are influenced by spontaneous face-to-face encounters, this finding represents a challenge for organizations with managers having subordinates who are close, but not close enough. Additionally, attempting to impose top-down requirements to travel between floors (e.g., when scheduling meetings) may do surprisingly little to abate this problem. Implications of these findings for organizations are discussed, as are limitations and future research, including possibilities for future model verification and validation.
Briggs, T. and Crooks, A.T. (2016), Close, But Not Close Enough: A Spatial Agent-Based Model of Manager-Subordinate Proximity. The Computational Social Science Society of Americas Conference, Santa Fe, NM. (PDF)
As you might of guessed the paper is about riots but that is not all. In the paper we have a highly detailed cognitive model implemented through the PECS (Physical conditions, Emotional state, Cognitive capabilities, and Social status) framework based around identity theory. The purpose of the model (and paper) is to explore how the unique socioeconomic variables underlying Kibera, a slum in Nairobi, coupled with local interactions of its residents, and the spread of a rumor, may trigger a riot such as those seen in 2007.
In order to explore this question from the "bottom up" we have developed a novel agent-based model that integrates social network analysis (SNA) and geographic information systems (GIS) for this purpose. In the paper we argue that this integration facilitates the modeling of dynamic social networks created through the agents’ daily interactions. The GIS is used to develop a realistic environment for agents to move and interact that includes a road network and points of interest which impact their daily lives.
Below is the abstract and a summary of its highlights in order to give you a sense of what our research contribution is. In addition to this we also provide some images either from the paper itself or the from Overview, Design Concepts, and Details (ODD) protocol. Finally at the bottom of this post you can see one of the simulation runs, details of where the model can be downloaded along with the full citation.
Paper Abstract:
Immediately after the 2007 Kenyan election results were announced, the country erupted in protest. Riots were particularly severe in Kibera, an informal settlement located within the nations capital, Nairobi. Through the lens of geosimulation, an agent-based model is integrated with social network analysis and geographic information systems to explore how the environment and local interactions underlying Kibera, combined with an external trigger, such as a rumor, led to the emergence of riots. We ground our model on empirical data of Kibera’s geospatial landscape, heterogeneous population, and daily activities of its residents. In order to effectively construct a model of riots, however, we must have an understanding of human behavior, especially that related to an individual’s need for identity and the role rumors play on a person’s decision to riot. This provided the foundation to develop the agents’ cognitive model, which created a feedback system between the agents’ activities in physical space and interactions in social space. Results showed that youth are more susceptible to rioting. Systematically increasing education and employment opportunities, however, did not have simple linear effects on rioting, or even on quality of life with respect to income and activities. The situation is more complex. By linking agent-based modeling, social network analysis, and geographic information systems we were able to develop a cognitive framework for the agents, better represent human behavior by modeling the interactions that occur over both physical and social space, and capture the nonlinear, reinforcing nature of the emergence and dissolution of riots.
Keywords: agent-based modeling; geographic information systems; social network analysis; riots; social influence; rumor propagation.
Paper Highlights:
An agent-based model integrates geographic information systems and social network analysis to model the emergence of riots.
The physical environment and agent attributes are developed using empirical data, including GIS and socioeconomic data.
The agent’s cognitive framework allowed for modeling their activities in physical space and interactions in social space.
Through the integration of the three techniques, we were able to capture the complex, nonlinear nature of riots.
Results show that youth are most vulnerable, and, increasing education and employment has nonlinear affects on rioting.
The high-level UML diagram of the model
A high-level representation of the model’s agent behavior incorporated into the PECS framework
An example of the evolution of social networks of ten Residents across the first two days of a simulation run.
The movie below shows the agent-based model which explores ethnic clashes in the Kenyan slum. The environment is made up of households, businesses, and service facilities (such data comes from OpenStreetMap). Agents within the model use a transportation network to move across the environment. As agents go about their daily activities, they interact with other agents - building out an evolving social network. Agents seek to meet their identity standard. Failure to reach their identity standard increases the agents frustration which can lead to an aggressive response (i.e. moving from blue to red color) such as rioting.
As with many of our models, we provide the data, model code and detailed model description in the form of the ODD protocol for others to use, learn more or to extend. Click here for more information.
Full Reference:
Pires, B. and Crooks, A.T. (2017), Modeling the Emergence of Riots: A Geosimulation Approach, Computers, Environment and Urban Systems, 61: 66-80. (pdf)
As normal, any thoughts or comments are most appreciated.
In the chapter we trace the rise in agent-based modeling within geographical systems with a specific emphasis of cities. We briefly outline how thinking and modeling cities has changed and how agent-based models align with this thinking along with giving a selection of example applications. We discuss the current limitations of agent-based models and ways of overcoming them and how such models can and have been used to support real world decision-making.
Conceptualization of an agent-based model where people are connected to each other and take actions when a specific condition is met
Full Reference:
Heppenstall, A. and Crooks, A.T. (2016). Agent-based Modeling in Geographical Systems, AccessScience, McGraw-Hill Education, Columbus, OH. DOI: http://dx.doi.org/10.1036/1097-8542.YB160741. (pdf)
