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)

 

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.

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)

A Semester of Spatial Agent-based Models

So draws an end of another semester and as it is becoming a bit of tradition, here is a post highlighting some of the class projects from my graduate class  entitled "Spatial Agent-based Models of Human-Environment Interactions". As with many of my courses, students were expected to complete a end of semester project, in this case, develop an agent-based model that explores some aspect of related to the course theme of human-environment interactions.  

For several of the students this was their first exposure to either agent-based modeling or utilizing geographical information in the modeling process. In the movie below a selection of these projects can be seen. The projects ranged from exploring how farming practices impact erosion, water reuse practices within agriculture, to the spread of diseases, deciding to evacuate during a disaster, to that of war gaming or how  zooplankton impacts Basking Shark shoaling behavior. As can be seen the movie, the models ranged from abstract spatial representations to those utilizing geographical information as a foundation of their artificial worlds. Many of the models where created using NetLogo (including one using LevelSpace) while others chose to utilize MASON or Mesa.

I would like to thank the Students of CSS 645: Spatial Agent-based Models of Human-Environment Interactions for their participation both in person and virtually in the class.  

Class Model Examples

Avid readers of this blog (if there are any) might have noticed at the end of each semester I do a post pertaining to class models from the various courses I teach. This often involves a short movie of some of these models like the one below.

Often I get asked about these models are, so finally I have complied a selection of them on GitHub: https://github.com/acrooks2/ClassModels. These are only NetLogo  models (for the time being) as I use it as a way of introducing students to agent-based modeling and programing.  As noted on the readme of the repository these models come as is. What explanations there are is given in the readme file for each model (these mainly come in the form of abstracts from the papers that were submitted with the models). No further explanations, support etc. will be given and are only provided to show the range of problems agent-based models can be used to explore. I also need to acknowledge all the students who submitted the models, you know who you are! This project would not be possible without you!

Maybe one day I will also get around to containerizing some of these models. For those interested containerization and how to do this for NetLogo models, https://www.comses.net/ has a great tutorial on this (click here for further details).

Examples of the types of GIS and agent-based modeling projects.

Models from Teaching CSS Fall 2019

Avid readers of this blog (if there are any) may be familiar with my routine of combing end of semester projects into a short movie and blogging about it. Well its that time again. Last semester I gave a class entitled Introduction to Computational Social Science and instead of setting a final exam, I ask the students to carryout an end of semester research project. The aim of this exercise is to cement what the students have (hopefully) learnt during the semester. I.e.: 

• to understand the motivation for the use of computational models in social science theory and research; 
• to learn about the variety of CSS research programs across the social science disciplines; 
• to understand the distinct contribution that CSS can make by providing specific insights about society, social phenomena at multiple scales, and the nature of social complexity.

Below you can see some of the outputs from these projects this last fall. These models ranged in type from agent-based models, microsimulation to system dynamics models applied to a variety of topics from how machine learning can be utilized within agent-based models to applications such as the courts, common pool resources, public goods, economic growth, supply chains, heath care issues (e.g. patient diagnosis, fungi infections within hospitals), team performance, labor markets, voting, and several other topics along the way.

Computational Social Science of Disasters: Opportunities and Challenges

Figure 1: Relation of computational social science of
disasters (CSSD) with other fields.

Past posts have discussed or demonstrated how  computational social science (CSS) (i.e. the study of social science through computational methods) can be utilized explore disasters or diseases but this has not really been  formalized.  To this end, Annetta Burger, Talha Oz, William Kennedy and myself have just had a paper published in Future Internet entitled "Computational Social Science of Disasters: Opportunities and Challenges". In the paper we introduce computational social science of disasters (CSSD). CSSD is defined as an approach to explain the social dynamics of disasters via computational means by adopting the relevant parts of CSS, social sciences in disaster, and crisis informatics as depicted in Figure 1. Specifically, we briefly review the domains and the approaches of each of the traditional social science disciplines to disasters (e.g. sociology, psychology, anthropology, political science, and economics). Next we describe the fields of CSS and crisis informatics before discussing the components of CSSD. We highlight some exemplar studies which capture certain elements of CSSD along with the challenges and opportunities it brings to the study of disasters. If you would like to find out more, below is the abstract to the paper along with the full reference and link to the paper.

Abstract

Disaster events and their economic impacts are trending, and climate projection studies suggest that the risks of disaster will continue to increase in the near future. Despite the broad and increasing social effects of these events, the empirical basis of disaster research is often weak, partially due to the natural paucity of observed data. At the same time, some of the early research regarding social responses to disasters have become outdated as social, cultural, and political norms have changed. The digital revolution, the open data trend, and the advancements in data science provide new opportunities for social science disaster research. We introduce the term computational social science of disasters (CSSD), which can be formally defined as the systematic study of the social behavioral dynamics of disasters utilizing computational methods. In this paper, we discuss and showcase the opportunities and the challenges in this new approach to disaster research. Following a brief review of the fields that relate to CSSD, namely traditional social sciences of disasters, computational social science, and crisis informatics, we examine how advances in Internet technologies offer a new lens through which to study disasters. By identifying gaps in the literature, we show how this new field could address ways to advance our understanding of the social and behavioral aspects of disasters in a digitally connected world. In doing so, our goal is to bridge the gap between data science and the social sciences of disasters in rapidly changing environments.

Keywords: Disasters; Computational Social Science; Crisis Informatics; Disaster Modeling, Web 2.0; Social Media; Big Data; Volunteered Geographical Information; Crowdsourcing.

Figure 2: Interactions of data analysis, computational models, and social theory
in computational social science of disasters.

Full Reference:

Burger, A., Oz, T., Kennedy, W.G. and Crooks, A.T. (2019), Computational Social Science of Disasters: Opportunities and Challenges, Future Internet, 11(5): 103; https://doi.org/10.3390/fi11050103. (pdf)

Models from Teaching CSS Fall 2018

Most of the time when I teach a class instead of setting a final exam, I ask the students to carryout an end of semester research project. In my Introduction to Computational Social Science class this project entails the development of a computational model in an area of  interest to the student . The aim of this exercise is to cement what the students have (hopefully) learnt during the semester. I.e.: 

• to understand the motivation for the use of computational models in social science theory and research; 
• to learn about the variety of CSS research programs across the social science disciplines; 
• to understand the distinct contribution that CSS can make by providing specific insights about society, social phenomena at multiple scales, and the nature of social complexity.

Below you can see some of the outputs from these projects this last fall. The models range in type from agent-based models, microsimulation to system dynamics models applied to a variety of topics from voting and political parties, the peer effects of students, urban decline, employment growth and rise and fall of civilizations and many other topics along the way.

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.

 

The Future of GEOINT: Data Science Will Not Be Enough

In the 2018 State and Future of GEOINT Report published by the The United States Geospatial Intelligence Foundation (USGIF) we had a paper accepted entitled "The Future of GEOINT: Data Science Will Not Be Enough". In the paper we discuss how there has been a deluge of spatial-temporally enabled data in the last several years with no signs of slowing down (e.g. by the year 2020, many experts predict the global universe of accessible data to be on the order of 44 zettabytes—44 trillion gigabytes). With this growth in data there has been steady uptake of data scientists in the GEOINT community because of their  ability to navigate petabytes of raw and unstructured data, then clean, analyze, and visualize the data. However,  we argue that we must go beyond just statistically analyzing data collected on the world around us to truly gain an understanding of the people who inhabit the world.  In order to do this, we suggest that future GEOINT analysts should not only have skills in data science but also be able to apply advanced computational methods, such as agent-based modeling, social network analysis, geographic information science, and deep learning algorithms (i.e. geospatial computational social science) to explore and test hypotheses based on social and geographic theory to truly achieve an understanding of human interactions.  

Full Reference: 

Parrett, C.M., Crooks, A.T. and Pike, T. (2018), The Future of GEOINT: Data Science Will Not Be Enough. The State and Future of GEOINT 2018 Report, The United States Geospatial Intelligence Foundation, Herndon, VA. pp 12-15. (pdf)

As always, any thoughts or comments are welcome. 

MAIS in CSS

It is always a great pleasure to teach and work with students, and see them complete their academic program. Over this last academic year, I supervised 3 masters students and served on the committee of another one, who all successfully completed their Master of Arts in Interdisciplinary Studies (MAIS) with a concentration in Computational Social Science (CSS). To quote from the MAIS in CSS website:

"Computational Social Science (CSS) is an interdisciplinary science in which social science questions are investigated with modern computational tools. Our program was the first CSS MA in the world, and continues to advance the study of social science through computational methods (e.g. agent-based modeling, social network analysis, and big data).

Our faculty members are internationally recognized for their pioneering work in CSS, including authoring the first textbook in the field, and have written numerous books and articles on topics such as growing artificial societies, modeling geographical systems, and sustainability. Research in the program is and has been funded by the National Science Foundation, United States Agency for International Development, National Geospatial-Intelligence Agency, the Defense Threat Reduction Agency, the Defense Advanced Research Projects Agency, and NASA.

Besides taking introductory classes in theories and practices of social, geo-social, economic, and network modeling, you will have the opportunity to work one-on-one with faculty on your project or thesis of interest, as well as directed readings. Additionally, Mason’s proximity to the Washington, D.C., area provides an excellent opportunity to attend seminars offered by NGOs, visiting professors, and government employees.

Students range from recent college graduates to mid-career professionals who bring diverse knowledge that enhances the classroom experience. Graduates have gone on to pursue their doctorates at Mason and other Carnegie Classification Research 1 universities. Others have pursued or continued their career in government or the private sector, in organizations such as the U.S. Army, MapR Technologies, CACI, Logistics Management Institute, and Ninja Analytics, Inc.

To get the latest information on our program, visit us on Facebook or our program page."

Below is a selection of projects from this academic year. Eric Hansen project was entitled "An Agent-Based Model of British And Boer Small Arms and Tactics During the Second Anglo-Boer War" in which he explored different military technology had an impact on the military victories.

In another project, Paul Cummings  explored different strategies for combating radicalism (i.e. Security Risk model and Socio-Economic Hardship model) via an agent-based model under the title of "Modeling the Characteristics of Radical Ideological Growth using an Agent based Model Methodology"

Marta Hansen's final project was entitled "Positive Affect And Prospect Theory In Agent_Zero: A Model Extension" which extends Joshua Epstein’s Agent_Zero model to allow for cooperative events to take place.

Just to highlight that not all students opt for  agent-based models. Devin Bright undertook a project entitled "Mapping the Human Terrain of a Modern Megacity with the use of Social Media." In which he explored how a years worth of social media data can be mined and analyzed via GIS and social network analysis (SNA) to to give insights into the dynamics of New York City in the United States and Lagos in Nigeria.

A semester with CSS

This last semester I gave both a graduate and undergraduate course in Computational Social Science (CSS). Both courses survey computational approaches such as system dynamics, social network analysis, machine learning, cellular automata, discrete event simulation, agent-based modeling, and microsimulation to study social phenomena with emphasis on complexity theory.

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.

New Paper: Close, But Not Close Enough

At the 2016 The 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.

Keywords: workplace design, supervision, leadership, management, employee performance, virtual teams, leader distance, collaboration, agent-based modeling, ABM

Full Reference:

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)

Click here to download the model.

CSS Phds and Masters 2016

One of the great rewards with working within a university is the interaction with students and seeing them advance through their studies and carryout innovative research projects.

This last academic year the Computational Social Science Program here at Mason had a bumper crop of graduates both at the PhD and masters level.

 In the picture are newly hooded Drs Palmer, Rouly and Magallanes.
Along with the not so new Drs Axtell, Crooks and Cioffi.

Our recent PhD graduates included:

• Chris Shin whose dissertation was entitled “An Essay on Micro Heterogeneity and the Evolution of Inequality.” 
• Steve Scott whose dissertation was entitled “Computational Modeling For Marine Resource Management.” 
• David Masad whose dissertation was entitled “Agents In Conflict: Comparative Agent-Based Modeling Of Inter- National Crises And Conflicts”
• Holly Russo whose dissertation was entitled “Explaining Box Office Performance From the Bottom Up: Data, Theories and Models”
• Hugh McFarlane whose dissertation was entitled “An Agent Based Model of Community Authority Structure Resilience.”
• Tom Dover whose dissertation was entitled “Implementing a Complex Social Simulation of the Violent Offending Process: The Promise of a Synthetic Offender.”
• Cristina Metgher whose dissertation was entitled "A Computational Social Science Approach to the Social Determinants of Cancer."
• Jose Magallanes whose dissertation was entitled “Climate Change And The Potential For Conflict And Extreme Migration In The Andes: A Computational Approach For Interdisciplinary Modeling And Anticipatory Policy-Making”
• Nathan Palmer whose dissertation was entitled"Individual and Social Learning: Bounded Rationality from First Principles" 
•  Chris Rouly whose dissertation was entitled "Towards Emergent Social Complexity" 

 In the picture are newly hooded Drs Scott, Russo, Masad, Dover and Shin. Along with the not so new Drs Cioffi, Crooks,  Kennedy and Mrs. Underwood.

Along with our PhD graduates we also had a number of Masters students graduate in the MAIS with a Concentration in Computational Social Science Program. Well done to Rui Zhang, Justin Brandenburg, Matthew Oldham, Stefan McCabe, Craig Brown and Stefani Fournier.

Tenure-Track Assistant Professor, Computational Social Science

Readers of this blog might be interested in the following position.

Tenure-Track Assistant Professor, Computational Social Science 

The George Mason University Computational and Data Sciences (CDS) Department in the College of Science invites applicants for a full-time, tenure-track faculty position at the Assistant Professor level. 

Responsibilities:
Beginning Fall 2016, this position is intended to primarily support the Computational Social Science (CSS) Program within CDS, including support of the Ph.D. degree in CSS, a master’s degree in interdisciplinary studies, and a CSS certificate. This position will also support undergraduate programs that are currently under development.

Qualifications:
Potential for success in both research and teaching are the primary criteria for this position. Applicants should have a promising research record, with a deep knowledge of and interest in computation as applied to one or more of the social sciences. While we are open to expertise in all areas of computational social science, we are particularly interested in social network specialists interested in both theory and data. Applicants must have a Ph.D. (expected completion by August 2016 is acceptable) from an accredited institution.

About the Program:
Methodologically, the CSS Program focuses on data-driven social science models using social network and agent-based computational approaches from a complexity perspective. Current faculty members have domain expertise in economics and finance, political science and international relations, geography and geographic information systems, land use and cover change, and public policy. As one of the first programs of its type in the world, CSS has had significant success in both research and professional placement. Our students come from all over the world (the Americas, Europe, Africa, Asia and Australia) and have been placed at a variety of top universities (e.g., University of Oxford, University College London), at government agencies, as well as in the private sector, including start-up companies.

More Information: 

• Job Posting https://jobs.gmu.edu/postings/36295
• CSS Program Facebook page
• CSS Program Webpage 

Lipari School on Computational Social Science

If you are wondering what to do between July 26 and August 1, this summer, you might be interested in this years Lipari School on Computational Social Science which is focusing on Algorithms, Data, and Models for Social and Urban Systems. 

What will be taught at the summer school and why? To answer the these questions and to quote from the the homepage of the school:

 "Social and urban systems have been the focus of social science theory and research for centuries, but only until recently have computational approaches enabled novel explorations of challenging and enduring research questions and the opening of new frontiers for investigation. What is the role of Computational Social Science in advancing the science of social and urban systems? Which advanced algorithms and data structures play a key role in these investigations? In 2015 our Lipari Summer School in CSS will address questions such as the role of GIS (geographic/geospatial information systems), social media, big social data, agent-based models, network models, and their integration in the study, design, and implementation of social and urban systems. "

The speakers will be:

• Andrew Crooks, George Mason University who will speak about  GIS and Agent-Based Modeling. 
  
• Alexei Pozdnukhov from UC Berkeley who will speak about Location-Based Social Networks.
• Carlo Ratti from the MIT Senseable City Lab who will speak about Senseable Cities.

Special guest speakers will be:

• Paolo Ferragina from the University of Pisa, Italy.
• Wulf Daseking from the University of Freiburg, Germany.

To find out how to apply to attend the summer school click here. Students are encouraged to apply early because enrollment is competitive and limited.

Example Models from CSS600

Even after several years of teaching it is always amazing how quickly a semester passes. One of the courses I taught this semester was CSS 600: Introduction to Computational Social Science. This is often the first CSS class many students take here at George Mason University. We discuss a number of computational approaches which are used for social science research, coupled to  complexity theory. As an introduction to the subject, the course has the following objectives:

1. To understand the motivation for the use of computational models in social science theory and research, including some historical aspects (Why conduct computational research in the social sciences?).
2. To learn about the variety of CSS research programs across the social science disciplines, through a survey of social simulation models (What has CSS accomplished thus far?).
3. To understand the distinct contribution that CSS can make by providing specific insights about society, social phenomena at multiple scales, and the nature of social complexity (What is the relation between computational social science.
4. To provide foundations for more advanced work in subsequent courses or projects for those students who already have or will develop a long-term interest in CSS.

Part of the students final grade comes from the development of a computational model in an area of their interest (e.g., microeconomics, international relations, environmental policy, economic development, historical dynamics, finance etc..). Often, this is the first compuational model that the students have ever developed. Below you can see a number of models developed using NetLogo as part of the class.

To find out more about our program see: http://www.css.gmu.edu/

Employment Growth through Labor Flow Network

Omar Guerrero and Robert Axtell from the Department of Computational Social Science at GMU have recently published a paper in PLoS ONE entitled "Employment Growth through Labor Flow Networks." The work uses "newly available micro-data and the ability to work with large-scale, complex networks computationally, to study labor dynamics." Below is the abstract from the paper:

It is conventional in labor economics to treat all workers who are seeking new jobs as belonging to a labor pool, and all firms that have job vacancies as an employer pool, and then match workers to jobs. Here we develop a new approach to study labor and firm dynamics. By combining the emerging science of networks with newly available employment micro-data, comprehensive at the level of whole countries, we are able to broadly characterize the process through which workers move between firms. Specifically, for each firm in an economy as a node in a graph, we draw edges between firms if a worker has migrated between them, possibly with a spell of unemployment in between. An economy's overall graph of firm-worker interactions is an object we call the labor flow network (LFN). This is the first study that characterizes a LFN for an entire economy. We explore the properties of this network, including its topology, its community structure, and its relationship to economic variables. It is shown that LFNs can be useful in identifying firms with high growth potential. We relate LFNs to other notions of high performance firms. Specifically, it is shown that fewer than 10% of firms account for nearly 90% of all employment growth. We conclude with a model in which empirically-salient LFNs emerge from the interaction of heterogeneous adaptive agents in a decentralized labor market.

Communities of firms.

To find more latest news from CSS check out our Facebook page.

GeoSocial Gauge

Over the last couple of months we have been working on getting our GeoSocial Gauge system up and running. The idea behind the website is to bring together social media and geographical analysis to monitor and explore people’s views, reactions, and interactions through space and time. It takes advantage of the emergence of social media to observe the human landscape as the living, breathing organism that it is: we can witness the explosion-like dissemination of information within a society, or the clusters of individuals who share common opinions or attitudes, and map the locations of these clusters. This is an unprecedented development that broadens drastically our understanding of the way that people act, react to events, and interact with each other and with their environment. We refer to this novel approach to study the integration of geography and society as GeoSocial Analysis.

The GeoSocial Gauge has several live streams ranging from exploring the political issues (e.g. Sequester) to to see what people are tweeting about TV (The Walking Dead).

Screen shot of GeoSocial Gauge of the Sequester. Showing the location of tweets, the most frequent words and whether or not the messages are positive (green) or negative (red).

Screen shot of GeoSocial Gauge of The Walking Dead.

Some of our initial work on this type of analyis can be found at:

• Stefanidis, T., Crooks, A.T. and Radzikowski, J. (2013), Harvesting Ambient Geospatial Information from Social Media Feeds, GeoJournal, 78, (2): 319-338.
• Crooks, A.T., Croitoru, A., Stefanidis, A. and Radzikowski, J. (2013), #Earthquake: Twitter as a Distributed Sensor System, Transactions in GIS, 17(1): 124-147.

Compuational Social Science @ GMU

The Department of Computational Social Science (CSS) at George Mason University is the first of its kind. It has active PhD, Master and Certificate programs in CSS. If readers are wondering what CSS is hopefully the quote from our Facebook page should help:

Computational Social Science is the interdisciplinary science of complex social systems and their investigation through computational modeling and related techniques. The field is at the intersection of social science and computer science and spans anthropology, economics, political science, sociology, and social psychology - as well as allied disciplines such as geography, history, organization theory, regional science, communication, and linguistics. We additionally utilize developments in psychology, cognitive science, neuroscience, and related branches of behavioral science for understanding social phenomena.

Computational approaches utilized and taught within the department include agent-based social simulation models (multi-agent systems), social network analysis, mathematical analysis based on complexity theory, social geospatial modeling methods (GIS), and automated information and content analysis methods. Through such computational methods we provide our students with a unique toolset to investigate social phenomena.

If you are interested in finding out what the Department of CSS is doing or want to view some of our models you might like to check out our Facebook page.