Retention in Higher Education: An Agent-Based Model

The effects of educational attainment on individuals and society have been the subject of much research. However, there is still a need to study what factors matter the most, and what is worth investing more time and resources into, and how new methods of analysis can provide additional ways of looking into some of the challenges faced by higher education. 

To this end at the 2024 International Conference of the Computational Social Science Society of the Americas (CSSSA), Amira Al-Khulaidy Stine and myself had a paper entitled "Retention in Higher Education: An Agent-Based Model of Social Interactions and Motivated Agent Behavior."  In the paper we introduce an agent-based model which explores retention where we focus on students and their levels of motivation (i.e., "grit"), their immediate connections (i.e., sense of belonging) and institutional support. At the same time we capture institutional locales (i.e., urban and rural) and their selectivity. Taken all together the model explores how these factors impact student success outcomes and retention.  We find students level of motivation is a reliable factor in determining student outcome which is inline with others, but the model also suggests that for certain student populations (i.e., those that have average motivation and mid range GPAs), their sense of belonging represented though their social connections with other students and institutional support (i.e., hubs of support)  can be more important. 

If this sounds of interest below we have the abstract to the paper, along with some images relating the data that was used to inform aspects of the model, the graphical user interface of model, its logic and some of the results that emerge from it. While at the bottom of the post you can find the full citation and a the link to the paper itself. The model itself which was developed in NetLogo, along with a detailed Overview, Design concepts, and Details (ODD) document can be found at https://www.comses.net/codebase-release/53302b0a-0a97-463a-8498-d604cb246e4c/.

Abstract:

In the United States, educational attainment and student retention in higher education are two of the main focuses of higher education research. Institutions are constantly looking for ways to identify areas of improvement across different aspects of the student experience on university campuses. This paper combines Department of Education data over a 10 year period, U.S. Census data, and higher education theory on student retention, to build an agent-based model of student behavior. Furthermore we model student social interactions with their peers along with considering environmental components (e.g., urban vs. rural campuses) and institution personnel to explore the elements that increase the likelihood of student retention. Results suggest that both social interactions and environmental components make a difference in student retention. Suggesting that higher education institutions should consider new ways to accommodate learning needs that promote better student outcomes.

Keywords: Agent-Based Model, College Campuses, Higher Education, Department of Education,  Social Interactions, Student Retention.

Student retention 2007-2021 by institutional support and urbanicity for Urban, Suburban, Town, and Rural areas.

Model Graphical User Interface.

Process Overview and Model Logic.

Retention results for Urban (left) and Rural (right) settings of low support and low sense of belonging while high motivation (grit).

Referece: 

Stine, A.A. and Crooks, A.T. (2024), Retention in Higher Education: An Agent-Based Model of Social Interactions and Motivated Agent Behavior, Proceedings of the 2024 International Conference of the Computational Social Science Society of the Americas, Santa Fe, NM. (pdf)

Using ABM to simulate Covid-19 vaccine uptake

In past blog posts we have discussed how one can use social media to study vaccine discussions and even tried to build a very simple disease model where vaccination rates were a factor in the spread of an outbreak. However, when it comes to vaccinations, especially that of Covid-19 vaccine there has been intense discussions in the physical (e.g., family), hybrid (e.g., work, school) and cyber (e.g., social media) spaces we inhabit. 

One thing that is unclear is how do these discussions in these various hybrid spaces impact our decision to get vaccinated or not? To this end, in a new paper published in the International Journal of Geographical Information Science with Fuzhen Yin, Li Yin and myself, entitled “How information propagation in hybrid spaces affects decision-making: using ABM to simulate Covid-19 vaccine uptake” we explore this. 

More specially we explore how through opinion dynamics modeling, how agents can chose to vaccinate or not and how much emphasis they place on physical, relational and cyber spaces Using Chautauqua County in New York State as a case study our model results captures the temporal dynamics of vaccination progress with small errors but we also find that different age groups demonstrate various preferences for different spaces to receive vaccine related information. 

If this sounds of interest, below you can read the abstract of the paper, see a flow chart of the model logic and some of the results. While at the bottom of the post you can find the full reference and link to the paper. Furthermore, Fuzhen has also provided a detailed Overview, Design Concepts and Details Protocol (ODD) document along with the source code and data needed to run the model at CoMSES Net https://www.comses.net/codebase-release/8967d4ca-9199-4ca8-be49-cab6d14db12c/. 

Abstract 

The notion of physical space has long been central in geographical theories. However, the widespread adoption of information and communication technologies (ICTs) has freed human dynamics from purely physical to also relational and cyber spaces. While researchers increasingly recognize such shifts, rarely have studies examined how the information propagates in these hybrid spaces (i.e., physical, relational, and cyber). By exploring the vaccine opinion dynamics through agent-based modeling, this study is the first that combines all hybrid spaces and explores their distinct impacts on human dynamics from an individual’s perspective. Our model captures the temporal dynamics of vaccination progress with small errors (MAE=2.45). Our results suggest that all hybrid spaces are indispensable in vaccination decision making. However, in our model, most of the agents tend to give more emphasis to the information that is spread in the physical instead of other hybrid spaces. Our study not only sheds light on human dynamics research but also offers a new lens to identifying vaccinated individuals which has long been challenging in disease-spread models. Furthermore, our study also provides responses for practitioners to develop vaccination outreach policies and plan for future outbreaks. 

Keywords: Agent-based modeling, hybrid space, opinion dynamics, Covid-19, vaccination. 

Flowchart of the modeling process. 

Comparing predicted and observed vaccination rates of all populations by giving physical, relational, cyber spaces different weights. Mean absolute error (MAE) and root mean square error (RMSE) are reported to evaluate the quality of predictions.

Comparing predicted and observed vaccination rates among different age groups by using the weight combination 3 (physical), 1 (relational), 1 (cyber) for hybrid spaces. 

Comparing predicted and observed vaccination rates by varying weights of hybrid spaces for different age groups.

Spatial distribution of Covid-19 vaccines. (a)-(d) Point density of vaccination allocation at different time steps. (e) Predicted vaccination rates at census block group level.

Full Referece:

Yin, F., Crooks, A.T. and Yin, L. (2024), How information propagation in hybrid spaces affects decision-making: using ABM to simulate Covid-19 vaccine uptake, International Journal of Geographical Information Science, https://doi.org/10.1080/13658816.2024.2333930 (pdf)

Creating Intelligent Agents

Continuing our work on machine learning and agent-based modeling,  at the upcoming Computational Social Science (CSS 2020) annual conference, Dale Brearcliffe and myself have a paper entitled: "Creating Intelligent Agents: Combining Agent-Based Modeling with Machine Learning."  In the paper we discuss how advances in computational availability and power have permitted a rapid increase in the development and use of machine learning (ML) solutions in a wide variety of applications (some examples we have already shown on this website), including within agent-based models. 

One thing however, is that while within the ML community at large, it is common to compare different approaches and take the one that gives the best result (e.g., like we did in the Communities, Bots and Vaccinations paper), this is not the case within the social simulation community. There has been little written with respect to why one ML method was chosen over another, or how the simulation results might be different if different ML methods were used. To address this gap we demonstrate the integration of three machine learning methods (i.e., Evolutionary Computing, Q Learning, and State→Action→Reward→State→Action (SARSA)) into the well-known agent based model: Sugarscape (in this instance we modified NetLogo's "Sugarscape 2 Constant Growback"). Our rationale for choosing the Sugarscape model was that it is well known within the social sciences and as the purpose of this paper was not to solve or explore a specific social issue, but to show how different ML methods can be used within the same agent-based model and to show how different methods impact the results of a model.

If this type of research is of interest, below we provide the abstract to the paper, a flow chart of the model execution along with some results. At the bottom of the past you can find the full reference and a link to the paper. Supplementary material can also be found at https://tinyurl.com/ML-Agents. At this link, the model presented in this paper along with a full description of it following the Overview, Design concepts, and Details (ODD) protocol can be found. We do this to allow others to replicate the results and adapt the ML methods for their own applications if they so desire.  

Graphical User Interface of the “Creating Intelligent Agents” model. From left to right: input parameters, agents within their artificial world, and aggregate model outputs

 

Abstract. 

Over the last two decades with advances in computational availability and power, we have seen a rapid increase in the development and use of Machine Learning (ML) solutions applied to a wide range of applications, including their use within agent-based models. However, little attention has been given to how different ML methods alter the simulation results. Within this paper, we discuss how ML methods have been utilized within agent-based models and explore how different methods affect the results. We do this by extending the Sugarscape model to include three ML methods (evolutionary computing, and two reinforcement learning algorithms (i.e., Q Learning, and State→Action→Reward→State→Action (SARSA)). We pit these ML methods against each other and the normal functioning of the rule-based method (Rule M) in pairwise combat. Our results demonstrate ML methods can be integrated into agent-based models, that learning does not always mean better results, and that agent attributes considered important to the modeler might not be to the agent. Our paper's contribution to the field of agent-based modeling is not only to show how previous researchers have used ML but also to directly compare and contrast how different ML methods used in the same model impact the simulation outcome, which is rarely discussed thus, helping bring awareness to researchers who are considering using intelligent agents to improve their models. 

Keywords: Agent-based Modeling, Evolutionary Computing, Machine Learning, Reinforcement Learning, Sugarscape.

 

Model execution flowchart.

Mean result for vision for all rule combinations (50 model runs).

 

Full reference:

Brearcliffe, D.K. and Crooks, A.T. (2020), Creating Intelligent Agents: Combining Agent-Based Modeling with Machine Learning, The 2020 Computational Social Science Society of Americas Conference, Online. (pdf)

Utilizing ABMs for The Human Resource Management

In a previous post from a few years ago we looked at how the workplace the layout might impact subordinates interactions with managers. Now turning to work employee satisfaction within the workplace, at the forthcoming International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation (or SBP-BRiMS for short) we have a paper entitled "The Human Resource Management Parameter Experimentation Tool".

In this paper we have created a model called the "Human Resources Management-Parameter Experimentation Tool" or HRM-PET for brevity, which is based on Herzberg et al. (1959) Two-Factor Theory. This theory has been used and tested for decades in human resource management as it can capture the interaction between a work force’s motivation and their environment’s hygiene. Hygiene in this context relates to policies and administration, supervision-technical, relationship-superior, working conditions, and salary which together moderate job dissatisfaction. While the theory has been extensively used it has not been explored via an agent-based model until now. By utilizing agent-based modeling, it allows us to test the empirically found variations on the Two-Factor Theory and its application to specific industries or organizations.

If you are interested in finding out more about this work, below we provide the abstract to the paper, an annotated graphical user interface of the model along with the basic decision making process for the agents. This is followed by some of the results from the model and movie of a representative model run. At the bottom of the post we provide the full citation to the paper, along with that of Herzberg et al. (1959). The model itself which was created in NetLogo 6.1, can be found along with a detailed Overview, Design concepts, and Details plus Decision making (ODD+D) document at http://bit.ly/HMR-PET. The rationale for utilizing the ODD+D and for sharing the model is that it allows broader dissemination of the model and its methodology.

Abstract:

Human resource management (HRM) draws on the field of organizational theory (OT) to identify, quantify, and manage people-based phenomena that impact organizational operations and outcomes. OT research has long used computational methods and agent-based modeling to understand complex adaptive systems. Agent-based modeling methodologies within HRM, however, are still rare. Within the HRM and management science literature, Herzberg’s et al. (1959) Two-Factor Theory (TFT) is a framework that has been tested and used for decades. Its ability to capture the interaction between a work force’s motivation and their environment’s hygiene lends itself well to agent-based modeling as a method of study. Here, we present the development of the Human Resources Management-Parameter Experimentation Tool (HRM-PET) as the first explicit ABM instantiation of TFT, filling the gap between the study of HRM and computational OT tools like agent-based modeling. 

Keywords: Human Resources Management, Management Science, Workforce Dynamics, Agent-based Modeling.

HRM-PET graphical user interface.

Decision making process for the agents in HRM-PET.

Worker congregation to work units under three variations of work unit hygiene factor distributions and two variations of weighing worker satisfaction and dissatisfaction.

References:

Herzberg, F.I., Mausner, B. and Snyderman, B. (1959),  The Motivation to Work (2nd ed.). New York: John Wiley.

Iasiello, C., Crooks, A.T. and Wittman, S. (2020), The Human Resource Management Parameter Experimentation Tool, in Thomson, R., Bisgin, H., Dancy, C., Hyder, A. and Hussain, M. (eds), 2020 International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation, Washington DC., pp. 298-307. (pdf)

Utilizing Agents To Explore Urban Shrinkage

While more people are living in urban areas than ever before, and this is expected to grow in the coming decades, this growth is not equal. Some cities are actually shrinking, such as Detroit in the United States. The causes of urban shrinkage have been the source of much debate but can be broadly attributed to a combination of factors relating to deindustrialization, suburbanization (i.e., urban sprawl), and demographic withdrawal. The result of shrinking cities, especially in and around the traditional downtown core of the city results in many problems, such as population loss, economic depression (due to loss in tax revenue), a growth in vacant properties, and the contraction of the land and housing markets.

To explore this phenomena, at the upcoming 2020 Spring Simulation Conference we have a paper entitled "Utilizing Agents To Explore Urban Shrinkage: A Case Study Of Detroit." The motivation for this paper is to explore the housing market in a shrinking city from the micro-level, specifically based on individuals trading interactions via an agent-based model stylized on spatially explicit data of Detroit Tri-county area. Our agent-based model demonstrates the potential of simulation to explore urban shrinkage and potentially offers a means to test polices to alleviate this issue. For readers wishing to know more about this work, below we provide the abstract to the paper, some figures sketching out some of model logic,  a sample of results and a movie of a representative model run. Similar to our other works, we have a more detailed description of the model following the Overview, Design concepts, and Details (ODD) protocol along with the source code and data needed to run the model at: http://bit.ly/UrbanShrinkage. We do this to aid replication and for others to extend if they see fit. As normal, any thoughts or comments are most welcome.

Abstract:

While the world’s total urban population continues to grow, this growth is not equal. Some cities are declining, resulting in urban shrinkage which is now a global phenomenon. Many problems emerge due to urban shrinkage including population loss, economic depression, vacant properties and the contraction of housing markets. To explore this issue, this paper presents an agent-based model stylized on spatially explicit data of Detroit Tri-county area, an area witnessing urban shrinkage. Specifically, the model examines how micro-level housing trades impact urban shrinkage by capturing interactions between sellers and buyers within different sub-housing markets. The stylized model results highlight not only how we can simulate housing transactions but the aggregate market conditions relating to urban shrinkage (i.e., the contraction of housing markets). To this end, the paper demonstrates the potential of simulation to explore urban shrinkage and potentially offers a means to test polices to alleviate this issue.

Keywords: Urban Shrinkage, Housing Markets, Detroit, Agent-based Modeling, GIS 

Agents Decision Making Process.

The sequences of all function events in the model are displayed by this UML diagram, which demonstrates the model flow, dynamic and interaction among the different components of the model.

Average Results where: (a) demand exceeds supply; (b) equal demand and supply; (c) supply exceeds demand for each different housing sub market.

Reference:

Jiang, N. and Crooks, A.T. (2020), Utilizing Agents to Explore Urban Shrinkage: A Case Study of Detroit, 2020 Spring Simulation Conference (SpringSim’20), Fairfax, VA. (pdf)

Exploring the Effects of Link Recommendations on Social Networks

Most people today are actively engaged on at least one social networking site, enabling individuals to keep in touch with old friends, connect with new people, and rapidly disseminate information to all. The method by which users find and link up with others online is often assisted by recommendation systems. A common technique utilized by online social networking sites (e.g., LinkedIn, Facebook) is to make link recommendations based upon friends of friends, or shared mutual connections. This method exploits a user’s social network structure, and specifically transitivity, to predict that a user will be interested in connecting with an individual who is also connected with that user’s friends, (i.e., “I am more likely to like someone who several of my friends like, than someone chosen at random”).

Despite the wide use of recommendation algorithms, little is known about the way in which recommendation systems impact the structure of online social networks. To address this problem at the upcoming (now virtual) 2020 Spring Simulation Conference we have a paper entitled "Exploring the Effects of Link Recommendations on Social Networks: An Agent-Based Modeling Approach."

This paper contributes to this limited area of (publicly available) research by demonstrating how a stylized agent-based model can be used to explore societal, network-level effects of commonly used online link recommendations from the bottom up. Below we provide the abstract to the paper, the steps the model takes to generate the online social network, the types of metrics outputted by the model and a selection of some of the results. While at the bottom of the post we provide the full reference to the paper. Further details about the model, in the Overview, Design Concepts, and Details (ODD) format along with the Python source code can be found at https://www.comses.net/codebase-release/3203a44a-fcb0-4957-a3b7-8323f829c0c4/. 

Abstract:

The vast majority of recommender system research has focused on improving performance accuracy, while limited work has explored their societal, network level effects. This paper demonstrates how simulation can be used to investigate macro level effects of online social network link recommendations, such as whether these technologies may be fragmenting or bridging communities of individuals. An agent-based model is presented that generates stylized online social networks with different percentages of real world contacts and link recommendations. Results show that networks with higher percentages of recommendation-based links produce more clustered, distinct, and dispersed communities, suggesting that these technologies could fragment society. Furthermore, scale-free network properties diminished with higher percentages of recommendations, suggesting that these technologies could be contributing to recent findings that social networks are at most ‘weakly’ scale-free. Building upon this research, further simulation work could inform the design of link recommendation algorithms that help connect both individuals and communities.

Keywords: online social network, social network analysis, mutual connection link recommendation system, friend-of-friend recommender, agent-based modeling.

Online network generation process

Social Network Analysis definitions for metrics output by this model

The effect of link recommendations on mean clustering coefficient and modularity. Error bars represent one standard deviation.

Probability density functions showing the degree distribution of Online networks (beginning top left and increasing left to right, top to bottom) with link recommendation percentage levels: 0, 10, 20, 30, 40, and 50. The blue line represents the empirical data, and red and green dotted lines represent fit lines corresponding with the power-law and lognormal distributions, respectively.

Full Reference: 

Sibley, C. and Crooks, A.T. (2020), Exploring the Effects of Link Recommendations on Social Networks: An Agent-Based Modeling Approach, Spring Simulation Conference (SpringSim’20), Fairfax, VA. (pdf)

Update: Our paper was selected as runner-up for best paper.

Modeling Homeowners Post-flood Reconstruction Decisions

In the past we have developed agent-based models to explore a wide variety of applications and even to explored at humanitarian assistance after a natural disaster, however we have not explored how people might decide to rebuild or not after a natural disaster. Well that was until now. In a new paper with Kim McEligot, Peggy Brouse and myself entitled "Sea Bright, New Jersey Reconstructed: Agent-Based Protection Theory Model Responses to Hurricane Sandy" which was published as part of the 2019 Winter Simulation Conference. In the paper we use a hindcast (aka backtesting) of Hurricane Sandy’s damage to Sea Bright, NJ and explore homeowners post-flood reconstruction decisions. Below we provide the abstract to the paper, a short movie of the model running, along with a link to access the source code and data of the model, and finally a link to the full paper.

Abstract:

Coastal flooding is the most expensive type of natural disaster in the United States. Policy initiatives to mitigate the effects of these events are dependent upon understanding flood victim responses at an individual and municipal level. Agent-Based Modeling (ABM) is an effective tool for analyzing community-wide responses to natural disaster, but the quality of the ABM’s performance is often challenging to determine. This paper discusses the complexity of the Protective Action Decision Model (PADM) and Protection Motivation Theory (PMT) for human decision making regarding hazard mitigations. A combined (PADM/PMT) model is developed and integrated into the MASON modeling framework. The ABM implements a hind-cast of Hurricane Sandy’s damage to Sea Bright, NJ and homeowner post-flood reconstruction decisions. It is validated against damage assessments and post-storm surveys. The contribution of socio-economic factors and built environment on model performance is also addressed and suggests that mitigation for townhouse communities will be challenging.

The model source code (utilizing MASON Version 17) and data is available on CoMSES.net: http://bit.ly/SEABrightABM.

Our adaptation of the Protection Motivation Theory and Protective Action Decision Model.

Full Reference:

McEligot, K. Brouse, P. and Crooks A.T. (2019), Sea Bright, New Jersey Reconstructed: Agent-Based Protection Theory Model Responses to Hurricane Sandy, in Mustafee, N., Bae, K.-H.G., Lazarova-Molnar, S., Rabe, M., Szabo, C., Haas, P. and Son, Y-J. (eds.), Proceedings of the 2019 Winter Simulation Conference, National Harbor, MD, pp 251-262 (pdf)

Drafting Agent-Based Modeling into Basketball Analytics

Readers of this blog might find this post a little  out of left field (sorry I could not find a better analogy) as it about basketball and therefore the ball is in your court if you want to keep reading.

At the upcoming SpringSim conference Matthew Oldham and myself  just had a paper accepted entitled "Drafting Agent-Based Modeling into Basketball Analytics" where we take a shot at modeling basketball. Why you might ask? The rational is that sports analytics (SA) is a multi-million dollar industry but to date little attention has been given to agent-based modeling (ABM) even though sports can be viewed as a complex adaptive system (Matthew on his site has a great write up of this). To explore this notion we built an agent-based model (utilizing NetLogo 3D) which captures the basic dynamics of a basketball game. In order to calibrate the processes within the model we utilized 17 seasons (2000 to 2016) of individual game data from the National Basketball Association (NBA). The data collected included; game scores, winning margins, field goal attempts, the percentage of field goals made, rebounds, steals, and turnovers. From the NBA game data, density functions were calculated to aid calibrating certain aspects of the model.  Via a set of experiments, the model indicates that an increased belief in the franchise player (think Michael Jordan) leads to increased scoring action, but a belief in the hot-hand had a minor effect. This results comes from the ability of agent-based models to identify the micro-interaction of agents responsible for generating system level outcomes and thereby, demonstrating the utility of ABM to SA.

Below, you a can read the abstract of the paper, along with some figures outlining the play cycle in the model, some some results of the varying NBA game metrics compared to the model, a movie of the graphical user interface of the model during a representative game. Finally at the end of the post you can find link to the model and the actual paper. 

The growth of sports analytics (SA) has raised numerous research topics across a variety of sports, including basketball. Agent-based modeling (ABM) has great potential to assist and inform SA, but to date it has not been utilized. To support the use of ABM in SA, a model of a basketball game, which considers most fundamentals of play, is presented. Additionally, player behavior is partially predicated on assessing the length of a player’s shooting streak (testing the “hot-hand” effect) and the consideration a team gives to a streak and their franchise player. The model’s output is used to calibrate and validate it against statistics from the National Basketball Association (NBA). Via a set of experiments, the model indicates that an increased belief in the franchise player leads to increased scoring action, but a belief in the hot-hand a minor effect. Thereby, demonstrating the utility of ABM to SA, thus opening a new research field.

Keywords: agent-based modeling, sports analytics, hot-hand effect.

Figure 2: The play cycle of the model.

Figure 3: Distribution of the varying NBA game metrics compared to the model.

The model along with a detailed Overview, Design concepts, and Details (ODD) document can be found at: https://tinyurl.com/ABMBasketBall.

Full Reference:

Oldham, M and Crooks, A.T. (2019) Drafting Agent-Based Modeling into Basketball Analytics, 2019 Spring Simulation Conference (SpringSim’19), Tucson, AZ. (pdf)

An Agent-Based Model of Rural Household Adaptation to Climate Change

Geographical location of the South Omo Zone of Ethiopia

While many of the recent posts on the site have focused on social media, social networks and volunteered geographical information, we have not forgotten or moved away from agent-based modeling (as you can probably gather from the title of this post).  To this end, Ates Hailegiorgis, Claudio Cioff-Revilla and myself recently had a paper published in the Journal of Artificial Societies and Social Simulation entitled: An Agent-Based Model of Rural Household Adaptation to Climate Change. 

The purpose of the model is to explore how climate change could impact rural societies in less developed countries whose livelihoods rely on subsistence agriculture. It has been suggested that climate change will place unprecedented stress on rural communities, as it will alter their resource base without giving them sufficient time for adaptation. While rural systems have developed various adaptive strategies over many generations in order to survive, the alteration of any resources can significantly affect even highly regarded and accepted customs, and may lead to the displacement of populations along with other severe humanitarian consequences.

In this paper we focus on the South Omo Zone of Ethiopia which covers an area of 2.3 million hectares and is located in the southern part of Ethiopia. Climate change is expected to play a significant role in shaping the future socio-ecological setting of the region and to explore this we devlepd a model  in the MASON simulation system, including its geographical information system (GIS) extension, GeoMASON called OMOLAND-CA (OMOLAND Climate Change Adaptation). Results from the model show that successive episodes of extreme events (e.g., droughts) can affect the adaptive capacity of households in the region, causing them to migrate from the region. While at the same time the rural communities manage to endure in spite of such harsh climatic change conditions.

Below you can read the abstract of the paper, see some of the figures including the models high-level architecture, along with the household decision-making process, some results from various scenarios and a link to the model and the full reference of the paper.

Abstract: 

Future climate change is expected to have greater impacts on societies whose livelihoods rely on subsistence agricultural systems. Adaptation is essential for mitigating adverse effects of climate change, to sustain rural livelihoods, and ensure future food security. We present an agent-based model, called OMOLAND-CA, which explores the impact of climate change on the adaptive capacity of rural communities in the South Omo Zone of Ethiopia. The purpose of the model is to answer research questions on the resilience and adaptive capacity of rural households with respect to variations in climate, socioeconomic factors, and land-use at the local level. Our model explicitly represents the socio-cognitive behavior of rural households toward climate change and resource flows that prompt agents to diversify their production strategy under different climatic conditions. Results from the model show that successive episodes of extreme events (e.g., droughts) affect the adaptive capacity of households, causing them to migrate from the region. Nonetheless, rural communities in the South Omo Zone, and in the model, manage to endure in spite of such harsh climatic change conditions.

Keywords: Climate Change Adaptation, Agent-Based Modeling, Socio-Cognitive Behavior

High-level architecture of the OMOLAND-CA model.

Household decision-making sequence for each time period in the model.

Population migration over time with different climatic conditions: a) 50% reduction, b) 70% reduction, c) 90% reduction of rainfall with different drought frequencies.

Livestock growth over time with different climatic conditions: a) 50% reduction, b) 70% reduction, c) 90% reduction of rainfall with different drought frequencies.

Simulation results of the frequency of crop planted per hectare.

In keeping with many of our agent-based models that we have created, a full description of the model (using the Overview, Design concepts, and Details plus Decision (ODD+D) protocol), along with its source code and data needed to run the model can be found at: https://www.openabm.org/model/5734/ .

Full Reference:

Hailegiorgis, A.B., Crooks, A.T. and Cioff-Revilla, C. (2018), An Agent-Based Model of Rural Households’ Adaptation to Climate Change, Journal of Artificial Societies and Social Simulation, 21 (4): 4. Available at http://jasss.soc.surrey.ac.uk/21/4/4.html.

 

Modeling the Emergence of Riots: A Geosimulation Approach

Bianica Pires and myself recently had a paper published in Computers, Environment and Urban Systems entitled "Modeling the Emergence of Riots: A Geosimulation Approach". 

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.
 

Exploring Creativity and Urban Development with Agent-Based Modeling

There is considerable debate about "creative cities" and relatively few agent-based models that explore such ideas from the bottom up. To that end we have recently published a paper in the Journal of Artificial Societies and Social Simulation entitled: "Exploring Creativity and Urban Development through Agent-Based Modeling"

In the paper we introduce the Creative City Model, an exploratory ABM to simulate the theoretical relationship between land-use regulation, urban mobility and societal tolerance on the economic performance of cities. The model is based on simplified assumptions from our empirically informed understanding of urban morphology, economic geography and the diffusion of creativity from human interactions.  It contributes to the growing literature exploring the dynamic socioeconomic processes underlying urban economic growth through computer simulation. Specifically the model offers a new lens to view the diffusion of creativity through knowledge spillovers under various scenarios from the bottom up. Through experimentation, the model suggests the existence of tradeoffs between the desire for social equity, estimated via rent affordability, and the rapid diffusion of creativity. Below you can find the abstract of the paper.

Abstract:

Scholars and urban planners have suggested that the key characteristic of leading world cities is that they attract the highest quality human talent through educational and professional opportunities. They offer enabling environments for productive human interactions and the growth of knowledge-based industries which drives economic growth through innovation. Both through hard and soft infrastructure, they offer physical connectivity which fosters human creativity and results in higher income levels. When combined with population density, socioeconomic diversity and societal tolerance; the elevated interaction intensity improves productivity. In many developing country cities however, rapid urbanization is increasing sprawl and causing deteriorating in public service standards. We further explore these insights by creating a stylized agent-based model where heterogeneous and independent decision-making agents interact under the following scenarios: (1) improved urban transportation investments; (2) mixed land-use regulations; and (3) reduced residential segregation. We find that any combination of scenarios resulting in conditions of intense human interaction results in greater economic growth. However, model results also demonstrate a clear trade-off between rapid economic progress and socioeconomic equity mainly due to the crowding out of low- and middle-income households from clusters of creativity. 

Key Words: Agent-Based Modeling; Developing Countries; Urban; Segregation; Land-use; Transportation

The movie below shows a typical simulation run of the model.


Further details about the model along with its ODD is available from the OpenABM website (click here).

Full Reference:

Malik, A.A., Crooks, A.T., Root, H.L. and Swartz, M. (2015), Exploring Creativity and Urban Development through Agent-Based Modeling, Journal of Artificial Societies and Social Simulation. 18 (2): 12. Available at http://jasss.soc.surrey.ac.uk/18/2/12.html. 

New Paper: ABM Applied to the Spread of Cholera

Cholera transmission through the interaction
of host and the environment

We are pleased to announce we have just had a paper published in Environmental Modelling and Software entitled "An Agent-based Modeling Approach Applied to the Spread of Cholera"

Research highlights include:

• An agent-based model was developed to explore the spread of cholera.
• The progress of cholera transmission is represented through a Susceptible-Exposed-Infected-Recovered (SEIR) model. 
• The model integrates geographical data with agents’ daily activities within a refugee camp.
• Results show cholera infections are impacted by agents’ movement and source of contamination. 
• The model has the potential for aiding humanitarian response with respect to disease outbreaks.

Cholera dynamics when rainfall is introduced.

Spatial spread of cholera over the course of a year.

Study area

If the research highlights have not turned you off, the abstract to the paper is below:

"Cholera is an intestinal disease and is characterized by diarrhea and severe dehydration. While cholera has mainly been eliminated in regions that can provide clean water, adequate hygiene and proper sanitation; it remains a constant threat in many parts of Africa and Asia. Within this paper, we develop an agent-based model that explores the spread of cholera in the Dadaab refugee camp in Kenya. Poor sanitation and housing conditions contribute to frequent incidents of cholera outbreaks within this camp. We model the spread of cholera by explicitly representing the interaction between humans and their environment, and the spread of the epidemic using a Susceptible-Exposed-Infected-Recovered model. Results from the model show that the spread of cholera grows radially from contaminated water sources and seasonal rains can cause the emergence of cholera outbreaks. This modeling effort highlights the potential of agent-based modeling to explore the spread of cholera in a humanitarian context."

Finally to aide replication, experimentation or just explore how you can link raster and vector data in GeoMason, we have a dedicated website where you can download executables of the model along with the source code and associated data. Moreover we have provide a really detailed Overview, Design concepts, and Details (ODD) Protocol document of the model here.

Full Reference:

Crooks, A.T. and Hailegiorgis, A.B. (2014), An Agent-based Modeling Approach Applied to the Spread of Cholera, Environmental Modelling and Software, 62: 164-177
DOI: 10.1016/j.envsoft.2014.08.027 (pdf)