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)

Evaluating the incentive for soil organic carbon sequestration from carinata production

Over the years we have developed several agent-based models that have explored various aspects of farming, ranging from farmers selling their land for development to that of water reuse. Keeping with this theme, we have a new paper with Kazi Ullah and Gbadebo Oladosu in the "Journal of Environmental Management" entitled "Evaluating the incentive for soil organic carbon sequestration from carinata production in the Southeast United States". 

 

In the paper we developed an agent-based model to evaluate what incentives might be needed for farmers to sequester soil organic carbon (SOC) when adopting a new bioenergy crop namely carinata. We simulated two carinata management scenarios: business as usual and climate-smart (no-till). The model finds that SOC sequestration incentives reduce the seed price needed to reach maximum adoption rates. While incentives lead to higher adoption rates, SOC sequestration, and profitability with no-till farming. 

 

If this sounds of interest, below you can read the abstract to the paper, get a sense of the agent logic and see some of the results. While at the bottom of the page, you can find the full reference and a link to the paper. The model (created in NetLogo) and data needed to run it is available on Kazi's GitHub page: https://github.com/KaziMaselUllah/Incentive_SOC_Carinata.

Abstract: Soil organic carbon (SOC) can be increased by cultivating bioenergy crops to produce low-carbon fuels, improving soil quality and agricultural productivity. This study evaluates the incentives for farmers to sequester SOC by adopting a bioenergy crop, carinata. Two agricultural management scenarios – business as usual (BaU) and a climate-smart (no-till) practice – were simulated using an agent-based modeling approach to account for farmers’ carinata adoption rates within their context of traditional crop rotations, the associated profitability, influences of neighboring farmers, as well as their individual attitudes. Using the state of Georgia, US, as a case study, the results show that farmers allocated 1056 × 10³ acres (23.8%; 2.47 acres is equivalent to 1 ha) of farmlands by 2050 at a contract price of $6.5 per bushel of carinata seeds and with an incentive of $50 Mg⁻¹ CO2e SOC sequestered under the BaU scenario. In contrast, at the same contract price and SOC incentive rate, farmers allocated 1152 × 10³ acres (25.9%) of land under the no-till scenario, while the SOC sequestration was 483.83 × 10³ Mg CO2e, which is nearly four times the amount under the BaU scenario. Thus, this study demonstrated combinations of seed prices and SOC incentives that encourage farmers to adopt carinata with climate-smart practices to attain higher SOC sequestration benefits.

Keywords: Agent-based model, Bioenergy, Climate-smart agriculture, Soil organic carbon, Incentives, Sustainable aviation fuel.

 

Process, overview and scheduling of the model

An example simulation output of a model run (SOC incentive = $50 Mg⁻¹ CO2e, Carinata contract price = 6.5, Expanded diffusion, Low initial willingness scenario).

The total number of farmers who adopted carinata over the years for two farming scenarios at five levels of incentives for SOC sequestration and at the four price levels.

The mean land allocation area for four scenarios and their associated standard deviations (error bar).

 

Full Reference:  

Ullah, K.M., Gbadebo G.A., and Crooks, A.T. (2023), Evaluating the Incentive for Soil Organic Carbon Sequestration from Carinata Production in the Southeast United States, Journal of Environmental Management, 348: 119418. Available at https://doi.org/10.1016/j.jenvman.2023.119418 (pdf)

Modeling Farmers’ Adoption Potential to New Bioenergy Crops

Close on the heals of the last post on farming, we have a new paper co-authored with Kazi Masel entitled "Modelling Farmers’ Adoption Potential to New Bioenergy Crops: An Agent-based Approach" which was presented at the 2022 Computational Social Science Society of the Americas (CSS 2022) Annual Conference. In the paper we explore the potential of farmers to adopt carinata in the state of Georgia. Carinata in an oilseed crop which could be used as a sustainable aviation fuel. Through our agent-based model our results suggest that a viable contract price made by investors could persuade farmers to adopt carinata. If this sounds of interest, below we provide the abstract to the paper along with a movie showing the model running along with some figures of the model logic and an example of one of the results. At the bottom of the post you can find the full reference to the paper and a link to a pdf of it. Similar to our other papers a detailed Overview, Design concepts and Details (ODD) protocol along with the model and the data needed to run the model has been made available at https://www.comses.net/codebase-release/5c2c06f0-3f6d-4f8d-b198-ce24b55feb2f/. This additional material allows for a more in-depth description of the model, as well as facilitates the replication of results or extension of the model.

Abstract: The use of fossil fuels is the primary source of greenhouse gas emissions but there are alternatives to these especially in the form of biofuels, fuels derived from bioenergy crops. This paper aims to determine farmers’ potential adoption rates of newly introduced bioenergy crops with a specific example of carinata in the state of Georgia. The determination is done using an agent-based modeling technique with two principal assumptions – farmers are profit maximizer and they are influenced by neighboring farmers. Two diffusion parameters (traditional and expansion) are followed along with two willingness (high and low) scenarios to switch at varying production economics to carinata and other prominent traditional field crops (cotton, peanuts, corn) in the study region. The paper finds that a contract prices around $9, $8 and $7 can be a viable option for encouraging farmers to adopt carinata in low, average, and high profit conditions, respectively. Expansion diffusion (that diffuses all over the geographical area), rather than centered to the few places like traditional diffusion at the early stage of adoption in conjunction with higher willingness conditions influences higher adoption rates in the short-term. As such, the model can be used to understand the behavioral economics of carinata in Georgia and beyond, as well as offering a potential tool to study similar bioenergy crops.

Keywords: Adoption, Agent-based modeling, Bioenergy Crops, Farming.

County-wise land availability for carinata production

Process, overview and scheduling of the model

Number of farmers who adopt carinata in the rotation years with high profit condition  (carinata yield = 60 bu/acre, carinata production cost = $260/acre)

Full Reference:

Ullah, K. and Crooks A.T., (2022), Modelling Farmers’ Adoption Potential to New Bioenergy Crops: An Agent-based Approach, The 2022 Computational Social Science Society of Americas Conference, Santa Fe, NM. (PDF)

Water reuse adoption by farmers & the impacts on local water resources using an ABM

In the past we heave explored a how farmers might sell their land but not how they might adapt new technologies or farming practices such as water reuse. But this has now changed with a new paper co-authored with Farshid Shoushtarian and  Masoud Negahban-Azar entitled "Investigating the micro-level dynamics of water reuse adoption by farmers and the impacts on local water resources using an agent-based model" which was recently published in the journal Socio-Environmental Systems Modelling. In the paper we introduce the WRAF  (water  reuse  adoption  by  farmers) model which explores how farmers might adopt water recycled water (reuse) practices. Using the model, results suggest that it might be possible through freshwater shortage or groundwater withdrawal regulations could increase recycled water use by farmers. If this sounds of interest, below we provide an abstract to the model, some figures from the agent logic (i.e., decision making), an overview of simulation results and the  full reference to the paper. Along with the paper, we have also provided more details  about the WRAF  model following the Overview, Design concepts, Details, and Decision-making (ODD) protocol along with the  NetLogo source code which can be found at https://www.comses.net/codebase-release/cc6d551e-cf0f-472e-a54b-28591cd39b4d/.

Abstract: Agricultural water reuse is gaining momentum to address freshwater scarcity worldwide. The main objective of this paper was to investigate the micro-level dynamics of water reuse adoption by farmers at the watershed scale. An agent-based model was developed to simulate agricultural water consumption and socio-hydrological dynamics. Using a case study in California, the developed model was tested, and the results showed that agricultural water reuse adoption by farmers is a gradual and time-consuming process. In addition, results also showed that agricultural water reuse could significantly decrease the water shortage (by 57.7%) and groundwater withdrawal (by 74.1%). Furthermore, our results suggest that recycled water price was the most influential factor in total recycled water consumption by farmers. Results also showed how possible freshwater shortage or groundwater withdrawal regulations could increase recycled water use by farmers. The developed model can significantly help assess how the current water reuse management practices and strategies would affect the sustainability of agricultural water resources.

Keywords: Water reuse; agent-based modelling; agricultural water management; recycled water for irrigation

(a) WRAF framework; (b) Farmers' decision-making flowchart

(a) Water reuse adoption sub-model framework; (b) Wastewater treatment plants flowchart

Representative simulation results: farmers’ water resources distribution in year one (a) andyear84(b);  available recycled water in the storage ponds of Modesto (c) and Turlock (d)wastewater treatment plants; total recycled water used by farmers in year two (e) and year 84(f)

Full Reference:

Shoushtarian, F., Negahban-Azar, M. and Crooks A.T. (2022), Investigating the Micro-level Dynamics of Water Reuse Adoption by Farmers and the Impacts on Local Water Resources using an Agent-based Model, Socio-Environmental Systems Modelling, 4: 18148. Available at https://doi.org/10.18174/sesmo.18148. (pdf)

Drone strikes and radicalization

In the past we had posted on models of radicalization, but such models were rather abstract.  Building on this previous work Brandon Shapiro and myself have a new paper entitled "Drone Strikes and Radicalization: An Exploration Utilizing Agent-Based Modeling and Data Applied to Pakistan" which has recently been published in Computational and Mathematical Organization Theory journal. In the paper we develop and present an agent-based model informed by theory and calibrated using empirical data to explore the relationship between kinetic actions (i.e., drone strikes) and terrorist attacks in Pakistan from 2004 through 2018. 

The data itself came from the Bureau of Investigative Journalism data as our source for Pakistan drone strikes (i.e., kinetic actions) and the National Consortium for the Study of Terrorism and Responses to Terrorism( START)  Global Terrorism Database (GTD) as our source for terrorist incidents. Rather than try to pinpoint and define the motivating factors which might influence somebody down a path toward radicalization, our model that incorporated a distributed lag model to characterize the inter-dependencies between drone strikes and terrorist attacks observed in Pakistan. Based on parametric and validation tests, the model simulates a terrorist attack curve which approximates the rate and magnitude observed in Pakistan from 2007 through 2018. 

If this sounds of interest, below we provide the abstract to the paper, along with some images of model graphical user interface, the model logic and some of the results. The model itself was created in NetLogo and is available at: https://www.comses.net/codebase-release/30540ae3-486b-44e4-8ff0-785575433af0/  (along with the data and detailed ODD of the model). At the bottom of the page you can find the full citation and a link to the paper.

Abstract:

The employment of drone strikes has been ongoing and the public continues to debate their perceived benefits. A question that persists is whether drone strikes contribute to an increase in radicalization. This paper presents a data-driven approach to explore the relationship between drone strikes conducted in Pakistan and subsequent responses, often in the form of terrorist attacks carried out by those in the communities targeted by these particular counter terrorism measures. Our exploration and analysis of news reports which discussed drone strikes and radicalization suggest that government-sanctioned drone strikes in Pakistan appear to drive terrorist events with a distributed lag that can be determined analytically. We leverage news reports to inform and calibrate an agent-based model grounded in radicalization and opinion dynamics theory. This enabled us to simulate terrorist attacks that approximated the rate and magnitude observed in Pakistan from 2007 through 2018. We argue that this research effort advances the field of radicalization and lays the foundation for further work in the area of data-driven modeling and drone strikes.  

Keywords: Radicalization, Data-driven modeling, Drone strikes, Terrorism, Pakistan , Agent-based modeling.

Pakistan radicalization model’s graphical user interface. From left to right: model input param- eters, the agents’ social network and resulting model outputs

The agent-based model flow diagram.

Terrorist attacks simulated by Pakistan radicalization model qualitatively agree with real-world system.

Full Reference: 

Shapiro, B. and Crooks, A.T. (2022) Drone Strikes and Radicalization: An Exploration Utilizing Agent-Based Modeling and Data Applied to Pakistan, Computational and Mathematical Organization Theory. Available at https://doi.org/10.1007/s10588-022-09364-1. (pdf)

Delineating a ‘15-Minute City’: An Agent-based Modeling Approach

With more and more people living in urban areas and the current COVID pandemic, human mobility within cities has changed. With this change there is a a growing debate about what it would take to make cities more accessible.  For example, what would it take for the inhabitants of cities be able to access most of their daily essentials (e.g., shopping, work, education, entertainment) within 15 minutes, commuting from their own doorstep either via walking, cycling, or other modes of transportation (e.g., bus, rail)?

To explore this notion of a 15 minute city, at the GeoSim'21: the 4th ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, Qingqing Chen  and myself had a paper entitled "Delineating a ‘15-Minute City’: An Agent-based Modeling Approach to Estimate the Size of Local Communities." While below we provide the abstract to the paper, being an online workshop, the talks were recorded so if you don't want to read the paper, you can watch Qingqing introduce the paper and see an example model run below. If this is of interest, at the bottom of the post we provide a link to the paper, while the actual model along with data needed to run the model can be found at https://github.com/chenqingqing/delineating-a-15minsCity.  

Abstract:

With progressively increased people living in cities, and lately the global COVID-19 outbreak, human mobility within cities has changed. Coinciding with this change, is the recent uptake of the ‘15-Minute City’ idea in urban planning around the world. One of the hallmarks of this idea is to create a high quality of life within a city via an acceptable travel distance (i.e., 15 minutes). However, a definitive benchmark for defining a ‘15- Minute City’ has yet to be agreed upon due to the heterogeneous character of urban morphologies worldwide. To shed light on this issue, we develop an agent-based model named ‘D-FMCities’ utilizing realistic street networks and points-of-interest, in this instance the borough of Queens in New York City as a test case. Through our modeling we grow diverse communities from the bottom up and estimate the size of such local communities to delineate 15-minute cities. Our findings suggest that the model could be helpful to detect the flexibility of defining the extent of a ‘15-minute city’ and consequently support uncovering the underlying factors that may affect its various definitions and diverse sizes throughout the world. 

Keywords: 15-minute city, Agent-based modelling, Local communities, Street networks, Point-of-interests, COVID-19.

Model Demo:

Full Reference:

Chen, Q and Crooks, A.T. (2021). Delineating a ‘15-Minute City’: An Agent-based Modeling Approach to Estimate the Size of Local Communities. In GeoSim '21: Proceedings of the 4th ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, November 2, 2021, Beijing, China, pp 29-37.  (PDF)

 

 

An Agent-based Model of Interactional Theory of Delinquency

While agent-based modeling is growing within many areas (e.g., geography, ecology) one area that has not seen many applications is that of social work. For example how can we explore what may cause an increase or a decrease in delinquency and recidivism within a given population? To this end, JoAnn Lee and myself recently had a paper published in the  Journal of Artificial Societies and Social Simulation entitled "Youth and their Artificial Social Environmental Risk and Promotive Scores (Ya-TASERPS): An Agent-based Model of Interactional Theory of Delinquency." In the paper we explore how one can test the interactional theory of delinquency via and agent-based model and as such provides a means of increasing our understanding of delinquency.

If this sounds of interest, below we provide the abstract to the paper and some of the figures (including the graphical user interface of the model, the conceptual model of interactions and how the model actually works. At the bottom of the post you can find the full citation of the paper and a link to it. The model itself was created in NetLogo and a detailed Overview, Design concepts, and Details plus Decision (ODD + D) protocol document is available at: https://bit.ly/YaTASERPS. We provide this documentation in order to provide more details about the model and aid others in replicating the results presented in the paper along with extending the model if so desired. 

Abstract: Risk assessments are designed to measure cumulative risk and promotive factors for delinquency and recidivism, and are used by criminal and juvenile justice systems to inform sanctions and interventions. Yet, these risk assessments tend to focus on individual risk and often fail to capture each individual’s environmental risk. This paper presents an agent-based model (ABM) which explores the interaction of individual and environmental risk on the youth. The ABM is based on an interactional theory of delinquency and moves beyond more traditional statistical approaches used to study delinquency that tend to rely on point-in-time measures, and to focus on exploring the dynamics and processes that evolve from interactions between agents (i.e., youths) and their environments. Our ABM simulates a youth’s day, where they spend time in schools, their neighborhoods, and families. The youth has proclivities for engaging in prosocial or antisocial behaviors, and their environments have likelihoods of presenting prosocial or antisocial opportunities. Results from systematically adjusting family, school, and neighborhood risk and promotive levels suggest that environmental risk and promotive factors play a role in shaping youth outcomes. As such the model shows promise for increasing our understanding of delinquency. 

Keywords: Agent-based Modeling, Antisocial Behaviors, Delinquency, Risk Factors, Youth, Social Work.

Graphical user interface of the model at model initialization. The model environment (right) shows youths (grey) at home (blue) and their neighborhood (green) and their school (brown).

Conceptual model of interactions.

Flow diagram of the overall model logic during the simulation.

 Full reference:

Lee, J. and Crooks A.T. (2021), Youth and their Artificial Social Environmental Risk and Promotive Scores (Ya-TASERPS): An Agent-Based Model of Interactional Theory of Delinquency, Journal of Artificial Societies and Social Simulation. 24 (4) 2. Available at: https://www.jasss.org/24/4/2.html (pdf)

 

Kinetic Action and Radicalization

In the past we had posted on models of radicalization, but such models were rather abstract.  However in a recent paper entitled "Kinetic Action and Radicalization: A Case Study of Pakistan" which was presented at the  International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation (or SBP-BRiMS for short) we take such work a step further. 

In the paper, Brandon Shapiro and myself develop and present a simple agent-based model informed by theory and calibrated using empirical data to explore the relationship between kinetic actions (i.e., drone strikes) and terrorist attacks in Pakistan from 2004 through 2018. The data itself came from the Bureau of Investigative Journalism data as our source for Pakistan drone strikes (i.e., kinetic actions) and the National Consortium for the Study of Terrorism and Responses to Terrorism( START)  Global Terrorism Database (GTD) as our source for terrorist incidents. 

Rather than try to pinpoint and define the motivating factors which might influence somebody down a path toward radicalization, our model that incorporated a distributed lag model to characterize the inter-dependencies between drone strikes and terrorist attacks observed in Pakistan. Based on parametric and validation tests, the model simulates a terrorist attack curve which approximates the rate and magnitude observed in Pakistan from 2007 through 2018. If this sounds of interest, below we provide the abstract to the paper, along with some images of model graphical user interface, the model logic and some of the results. The model itself was created in NetLogo and is available at: https://bit.ly/3qLJynv  (along with the data and detailed ODD of the model). At the bottom of the page you can find the full citation and a link to the paper.

Abstract. Drone strikes have been ongoing and there is a debate about their benefits. One major question is what is their role with respect to radicalization. This paper presents a data-driven approach to explore the relationship between drone strikes in Pakistan and subsequent responses, often in the form of terrorist attacks carried out by those in the communities targeted by these counter-terrorism measures. Our analysis of news reports which dis-cussed drone strikes and radicalization suggests that government-sanctioned drone strikes in Pakistan appear to drive terrorist events with a distributed lag that can be determined analytically. We then utilize these news reports to inform and calibrate an agent-based model which is ground-ed in radicalization and opinion dynamics theory. In doing so, we were able to simulate terrorist attacks that approximated the rate and magnitude ob-served in Pakistan from 2007 through 2018. We argue that this research effort advances the field of radicalization and lays the foundation for further work in the area of data-driven modeling and kinetic actions.

Keywords: Radicalization, Data-driven modeling, Drone strikes, Terrorism, Pakistan, Agent-based modeling.

Radicalization model’s graphical user interface.

The agent-based model flow diagram.

Terrorist attacks simulated by radicalization model qualitatively agree with real-world system.

Full Reference:

Shapiro, B. and Crooks, A.T. (2021), Kinetic Action and Radicalization: A Case Study of Pakistan, in Thomson, R., Hussain, M.N., Dancy, C.L. and Pyke, A. (eds), Proceedings of 2021 International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation, Washington DC., pp 321-330. (pdf)

A Semester of Spatial Simulation

While at Mason, it was a tradition of mine to make  a post of some of the models developed by students in my classes as part of their end of semester projects.  So while I am not at Mason anymore, I thought I would keep this tradition when teaching agent-based modeling classes. To that end, this semester at UB I taught a class entitled “Spatial Simulation” (and the course description is below for these who are interested). 

 

For many, this was their first exposure to agent-based and cellular automata (CA) modeling. As part of the class the students were expected to complete an end of semester project, in this case, develop an agent-based or CA model that explores some aspect of the course themes.   The movie below shows a selection of these projects which ranged from what a exploring what 15 minute city would look like, to that of land use change over years and several other  topics in-between. Many of the astute readers might notice these models where created using NetLogo which was used in class to teach the basics of spatial simulation but at the same time could leverage our book  "Agent-based Modelling and Geographical Information Systems: A Practical Prime" and associated resources on GitHub.

Coarse Description:

This graduate course will introduce students in the geographical and environmental sciences to the use of spatial simulation methods (e.g., cellular automata, agent-based modeling) to explore complex geographical phenomena from the bottom up. For example, how the micro-movement of pedestrians lead to the emergence of crowds or how individuals buying and selling houses lead to property markets forming. We will cover geographical applications in areas such as agriculture, biodiversity, interactions between human populations and nonhuman species and cities. Emphasis will be placed on the notion that geographical systems are constantly changing at various spatiotemporal scales and how through spatial simulation we can gain an understanding of the processes that lead to patterns that we can observe through data. The course will combine taught classes, literature reviews and discussions with hands-on spatial simulation modeling. The format of the class will consist of both lecture and discussion, with substantial emphasis on student participation.

A Busy Day: A Talk and NetLogo Tutorial

It is not often that I get to give a talk in one country and tutorial in another country, but thanks to COVID and the internet, that was today. First up I was invited to give a talk to the GIScience Research Group (GIScRG) at the Royal Geographical Society with IB, in the UK. The talk was entitled "Analyzing and Modeling Urban Environments Utilizing Computational Social Science: Opportunities, Examples and Challenge" which covered many of the topics that have been blogged here over the last few year. Below is the abstract to the talk and if this peaks your interest, the talk was recorded and is embedded here.

Abstract: The beginning of this century marked a milestone in human history. For the first time, more than half of the world’s population lived in urban areas. This trend is expected to continue into the foreseeable future with 6.7 billion people projected to live in cities by 2050. This rapid urbanization will place unprecedented pressures on urban systems and their ability to provide basic of services. To plan for this future, we need to better understand the inherent complexity of urban systems from social, economic and environmental perspectives. In this talk, I will explore how such understanding can be gained through the lens of computational social science (CSS): the interdisciplinary science of complex social systems and their investigation through computational modeling (e.g. agent-based models) and related techniques. Through a series of example applications, I will demonstrate how new forms of geographical data (e.g. crowdsourced, social media etc.) not only provide us with a novel way of analyzing urban environments but how such data can be integrated into geographically explicit agent-based models. In addition, I will highlight that by focusing on individual, or groups of individuals, leads to more aggregate patterns emerging and show how model outcomes can be validated by such datasets. After these demonstrations, I will outline the challenges associated with this program of research, as using such data is not without its difficulties. Together, this work provides a brief overview of the current state of analyzing and modeling urban environments through the lens of CSS. 

I would like to thanks those who joined this webinar, especially those who asked questions. On a side note, the RGS-IBG GIScience Research Group YouTube Chanel also has a great number of talks relating to GIScience and Geographic Data Science which are well worth watching. 

Later in the day, Sara Metcalf and myself were invited to give a tutorial entitled "Introduction to Agent Based Models" as part of the University at Buffalo's Computational and Data-enabled Science and Engineering (CDSE) day. In this tutorial we introduced agent-based modeling, discussed a variety of applications and ran through a tutorial, that of creating the Schelling Segregation model in NetLogo.

Abstract: This session will introduce the method of agent-based modeling, give a tutorial, and discuss a range of applications. Agent-based models facilitate dynamic simulation of multi-scalar feedback mechanisms and interactions between heterogeneous individual agents and their environments. Agents may represent people, animals, organizations, or other kinds of discrete decision-making entities. Participants who wish to practice developing the agent-based models demonstrated in this session should install the free NetLogo software

For those who are interested, the tutorial as a PDF can be found at https://tinyurl.com/CDSEnetlogo and you can follow along by watching the movie below.

Simulating Urban Shrinkage in Detroit via Agent-Based Modeling

While we are witnessing a growth in the world-wide urban population, not all cities are growing equally and some are actually shrinking (e.g., Leipzig in Germany; Urumqi in China; and Detroit in the United States). Such shrinking cities pose a significant challenge to urban sustainability from the urban planning, development and management point of view due to declining populations and changes in land use. To explore such a phenomena from the bottom up, Na (Richard) Jiang, Wenjing Wang, Yichun Xie and myself have a new paper entitled "Simulating Urban Shrinkage in Detroit via Agent-Based Modeling" published in Sustainability. 

This paper builds on our initial efforts in this area which was presented in a previous post. In that post we showed how a stylized model could not only simulate housing transactions but the aggregate market conditions relating to urban shrinkage (i.e., the contraction of housing markets). In this new paper, we significantly extend our previous work by: 1) enlarging the study area; 2) introducing another type of agent, specially, a bank type agent; 3) enhancing the trade functions by incorporating agents preferences when it comes to buying a house; 4) adding additional household dynamics, such as employment status change. These changes will are discussed extensively in the methodology section of the paper.

If this is of interest to you, below we provide the abstract of the paper along with some figures of the study area, graphical user interface, model logic and results. At the bottom of the post you can see the full reference to the paper along with a link to it. The model itself was created in NetLogo and a 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/ExploreUrbanShrinkage.

Abstract

While the world’s total urban population continues to grow, not all cities are witnessing such growth, some are actually shrinking. This shrinkage causes several problems to emerge, including population loss, economic depression, vacant properties and the contraction of housing markets. Such issues challenge efforts to make cities sustainable. While there is a growing body of work on studying shrinking cities, few explore such a phenomenon from the bottom-up using dynamic computational models. To fill this gap, this paper presents a spatially explicit agent-based model stylized on the Detroit Tri-County area, an area witnessing shrinkage. Specifically, the model demonstrates how the buying and selling of houses can lead to urban shrinkage through a bottom-up approach. The results of the model indicate that along with the lower level housing transactions being captured, the aggregated level market conditions relating to urban shrinkage are also denoted (i.e., the contraction of housing markets). As such, the paper demonstrates the potential of simulation to explore urban shrinkage and potentially offers a means to test policies to achieve urban sustainability.

Keywords: Agent-based modeling; housing markets; Urban Shrinkage; cities; Detroit; GIS

Study Area.

 

Model graphical user interface, including input parameters, monitors (left) and the study area (middle) and charts recording key model properties.

Unified modeling language (UML) Diagram of the Model.

Household Decision-Making Process for Stay or Leave Current Location.

Heat Maps of Median (A) and Average (B) House Prices at the End of the Simulation where Demand equals Supply.

Full Reference: 

Jiang, N., Crooks, A.T., Wang, W. and Xie, Y. (2021), Simulating Urban Shrinkage in Detroit via Agent-Based Modeling, Sustainability, 13, 2283. Available at https://doi.org/10.3390/su13042283. (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)