Organizing Theories for Disasters into a Complex Adaptive System Framework

In past posts we have discussed or demonstrated how computational social science (CSS) (i.e. the study of social science through computational methods) and complexity theory can be utilized explore disasters or diseases but this has not really been  formalized.  To this end, Annetta Burger, William Kennedy and myself have a new review paper in Urban Science entitled "Organizing Theories for Disasters into a Complex Adaptive System Framework." In the paper we review over a century of disaster research and demonstrate the properties and dynamics of complex adaptive systems in such studies and argue how complexity theory is integral to understanding human behavior in disasters by addressing the interactions across systems (i.e., physical, social, and individual systems). We discuss the characteristics of a complex adaptive system (e.g., heterogeneity, webs of connections, relationships and interactions, and adaptations arising from individual actions, decisions, and learning) and how such characteristics can be applied to disaster research and explore implications for future disaster research with an eye on sustainable and resilient cities. If this sounds of interest, and you want to find out more, below we provide the abstract to the paper and a  link to the the paper itself.

Abstract: Increasingly urbanized populations and climate change have shifted the focus of decision1makers from economic growth to the sustainability and resilience of urban infrastructure and communities, especially when communities face multiple hazards and need to recover from recurring disasters. Understanding human behavior and its interactions with built-environments in disasters requires disciplinary crossover to explain its complexity, therefore we apply the lens of complex adaptive systems (CAS) to review disaster studies across disciplines. Disasters can be understood to consist of three interacting systems: 1) the physical system, consisting of geological, ecological, and human-built systems; 2) the social system, consisting of informal and formal human collective behavior; and 3) the individual actor system. Exploration of human behavior in these systems shows that CAS properties of heterogeneity, interacting subsystems, emergence, adaptation, and learning are integral, not just to cities, but to disaster studies and connecting them in the CAS framework provides us with a new lens to study disasters across disciplines. This paper explores the theories and models used in disaster studies, provides a framework to study and explain disasters, and discusses how complex adaptive systems can support theory-building in disaster science for promoting more sustainable and resilient cities.

Keywords: Cities; Complex Adaptive Systems; Computational Social Science, Disasters; Human Behavior.

Framework for Understanding the Intersecting Complex Adaptive Systems of Disaster.

Full Reference:

Burger, A., Kennedy, W.G. and Crooks A.T. (2021), Organizing Theories for Disasters into a Complex Adaptive System Framework, Urban Science, 5(3), 61; https://doi.org/10.3390/urbansci5030061 (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.

Diversity from Emojis and Keywords in Social Media

Building on our initial work on emojis  use and and how one can carry out a systematic comparison of emojis across individual user profiles and communication patterns within social media, we have a new paper entitled: "Diversity from Emojis and Keywords in Social Media" which was presented at the 11th International Conference on Social Media and Society. 

In the paper we present a novel method using a diversity language model to associate diversity related attributes to social media user accounts and content by analyzing the emojis and keywords used (in this case from Twitter). We used this diversity language model to shed light on the groups of social media users and content with similar diversity attributes related to American politics (specifically the 2018 U.S. midterm elections). Our results revealed topics of interest and patterns of social media engagement across political lines among the diverse populations that otherwise would not have been apparent if we only analyzed the key political campaign phrases and slogans (i.e. “Blue Wave” and “Make America Great Again”) without taking diversity into account.

For interested readers, below we provide the abstract to the paper along with some figures from the paper. These include our workflow for diversity analysis of social media content, a high level overview of our diversity language model. These are followed by some of our results. Specifically the presence of diversity keywords and emojis in user profiles, and the composition of users in our collection based on gender for two political campaigns. If this peaks you interest as the conferce was virtual we have also prepared a short movie of the paper. While at the bottom of the post you find the full reference to the paper along with a link to the paper itself.

Abstract:

Social media is a popular source for political communication and user engagement around social and political issues. While the diversity of the population participating in social and political events in person are often considered for social science research, measuring the diversity representation within online communities is not a common part of social media analysis. This paper attempts to fill that gap and presents a methodology for labeling and analyzing diversity in a social media sample based on emojis and keywords associated with gender, skin tone, sexual orientation, religion, and political ideology. We analyze the trends of diversity related themes and the diversity of users engaging in the online political community during the lead up to the 2018 U.S. midterm elections. Our results reveal patterns along diversity themes that otherwise would have been lost in the volume of content. Further, the diversity composition of our sample of online users rallying around political campaigns was similar to those measured in exit polls on election day. The diversity language model and methodology for diversity analysis presented in this paper can be adapted to other languages and applied to other research domains to provide social media researchers a valuable lens to identify the diversity of voices and topics of interest for the less-represented populations participating in an online social community.

Keywords: Social media, emoji, diversity, elections, political campaigns

Workflow for diversity analysis of social media content

Diversity Language Model

Presence of diversity keywords and emojis in user profiles

Composition of users in our collection based on gender for two political campaigns

Full Reference:

Swartz, M., Crooks, A.T. and Kennedy, W.G. (2020), Diversity from Emojis and Keywords in Social Media, in Gruzd, A., Mai, P., Recuero, R., Hernández-García, A., Lee, C.S., Cook, J., Hodson, J., McEwan, B and Hopke, J. (eds.), Proceedings of the 11th International Conference on Social Media & Society, Toronto, Canada, pp 92-100. (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)

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.

Communities, Bots and Vaccinations

Following on from our work on bots and health discussions in relation to online social networks (OSNs), Xiaoyi Yuan, Ross Schuchard and myself have just published a paper entitled "Examining Emergent Communities and Detecting Social Bots within the Polarized Online Vaccination Debate in Twitter" in Social Media + Society. Within the paper we explore the communication patterns of vaccine discussions in Twitter. More specifically we ask three questions:

1. Do vaccine discussions on Twitter show a highly clustered pattern in the sense that users tend to communicate more often with those who have same opinions towards vaccination than those who do not? 
2. If the communication is highly clustered, to what extent do pro-vaccine users reach out to anti-vaccine users and vice versa? 
3. How much do social bots, computer algorithms designed to mimic human behavior and interact with humans in an automated fashion, contribute to the conversation as previous research has shown that social bots can have certain impact on human communication in social media?

In order to answer these questions, we use a variety of machine learning techniques (e.g.  logistic regression, support vector machine (e.g. linear and non-linear kernel), k-nearest neighbors, nearest centroid, and Naïve Bayes) trained with labeled data (which is available at https://github.com/XiaoyiYuan/vaccination_online_discussion) to categorize each user’s vaccination stance. By exploring a combination of opinion groups and retweet networks we discovered that pro- and anti-vaccine users retweet predominantly from their own opinion group, while users with neutral opinions are distributed across communities. In addition, our bot analysis (using the  open-source DeBot detection platform) discovered that 1.45% of the corpus users were identified as likely bots and these produced 4.59% of all tweets within our data set. If you wish to find out more about our paper, below you can read the abstract along with seeing some figures including a sketch of our methodology, a selection of results and a link to the paper.

Abstract:

Many states in the United States allow a “belief exemption” for measles, mumps, and rubella (MMR) vaccines. People’s opinion on whether or not to take the vaccine can have direct consequences in public health. Social media has been one of the dominant communication channels for people to express their opinions of vaccination. Despite governmental organizations’ efforts of disseminating information of vaccination benefits, anti-vaccine sentiment is still gaining momentum. Studies have shown that bots on social media (i.e., social bots) can influence opinion trends by posting a substantial number of automated messages. The research presented here investigates the communication patterns of anti- and pro-vaccine users and the role of bots in Twitter by studying a retweet network related to MMR vaccine after the 2015 California Disneyland measles outbreak. We first classified the users into anti-vaccination, neutral to vaccination, and pro-vaccination groups using supervised machine learning. We discovered that pro- and anti-vaccine users retweet predominantly from their own opinion group. In addition, our bot analysis discovers that 1.45% of the corpus users were identified as likely bots which produced 4.59% of all tweets within our dataset. We further found that bots display hyper-social tendencies by initiating retweets at higher frequencies with users within the same opinion group. The article concludes that highly clustered anti-vaccine Twitter users make it difficult for health organizations to penetrate and counter opinionated information while social bots may be deepening this trend. We believe that these findings can be useful in developing strategies for health communication of vaccination.

Keywords: Anti-vaccine Movement, Twitter, Social Media, Opinion Classification, Bot Analysis

Full Reference:

Yuan, X.,  Schuchard, R.  and Crooks, A.T. (2019), Examining Emergent Communities and Detecting Social Bots within the Polarized Online Vaccination Debate in Twitter, Social Media + Society. https://doi.org/10.1177/2056305119865465 (pdf)

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.

Models from Teaching CSS

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 classes (both at the graduate and undergraduate level), this project entails the development of a computational model in an area of  interest to the student (or at the undergraduate level, students can opt to systematically explore someone else's model). 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, cellular automata models to discrete event simulations (aka. queuing models) applied to a variety of topics from elephant poaching, artists and patrons, inheritance and wealth accumulation, the spread of religion, to that of looking at serving times at a Chipotle Mexican Grill.

 

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.

Spatial Agent-based Models of Human-Environment Interactions: Spring 2016

During the past spring semester I taught a 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. Below is a selection of these projects, which ranged from hiking along the Application trail,  to that of exploring the ride-sharing economy, to the spread of diseases, ecosystem recovery modeling and the origins of social complexity. 

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

A semester of CSS

For the last few years one of the classes that I have given is the Introduction to Computational Social Science (CSS). This is often the first class many students take within our program and as such its objectives are:

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 computational social science.

The course surveys 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. 

On thing that all the students need to do during the semester is create a computational model investigating a social science research question. This exercise is often their first model that many students ever create. Below you can see some of this years models. Most of the models where created in NetLogo.  

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 

A Semester with Spatial Agent-based Models

With the spring semester over, I thought I would show some of the final agent-based modeling projects that were carried out in CSS 645: Spatial Agent-based Models of Human-Environment Interactions. As always I was quite impressed the models and we had a plethora of topics ranging from mobile agent-based models, shopping, pick pocketing, route finding, travel to work, the spatial spread of information, deer management, urban growth etc... What is interesting is that while the majority of models are implemented in NetLogo, more and more are being done in Python.

Something new for this semester is we also tried to reproduce a published model. Below you can see 3 examples of such work. Click here to see a previous post on reproduction and replication. 

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/

Comparing the spatial characteristics cyber and physical communities

Readers of the blog know that I have an interest in social media, and how through it we can gain an understanding of society at large. The question is how does the cyber community reflect the corresponding physical community? To this end, papers from 6th ACM SIGSPATIAL International Workshop on Location-Based Social Networks which was held in conjunction with the 21st ACM SIGSPATIAL conference have just come out on the  ACM Digital Library. We presented a paper at the conference entitled "Comparing the Spatial Characteristics of Corresponding Cyber and Physical Communities: A Case Study" The abstract of the paper is as follows:

"The proliferation of social media over the past few years is presenting us with unique opportunities to sample opinions and interests at spatial and temporal resolutions previously unheard of. In order to make best use of this information though, we need a better understanding of the degree to which the cyber community that is observed through them can serve as a proxy for the corresponding physical community. In this paper we are making a contribution towards this issue by presenting a case study in which we compare spatial characteristics of a community both in the physical and cyber spaces. The key findings of our analysis relate to the selection of an appropriate level of spatial aggregation for analyzing social media content, and on the effect in the level of participation of the distance from the point of interest."

We hope you enjoy it.

Full reference: 

• Lu, X., Croitoru, A., Radzikowski, J, Crooks, A. T. and Stefanidis, A. (2013), Comparing the Spatial Characteristics of Corresponding Cyber and Physical Communities: A Case Study, 6th ACM SIGSPATIAL International Workshop on Location-Based Social Networks, Orlando, FL, pp 11-14. (pdf)

Agent_Zero

Readers of the blog might be interested in reading Josh Epstein's new book "Agent_Zero: Toward Neurocognitive Foundations for Generative Social Science" To quote from the publisher:

"In this pioneering synthesis, Joshua Epstein introduces a new theoretical entity: Agent_Zero. This software individual, or "agent," is endowed with distinct emotional/affective, cognitive/deliberative, and social modules. Grounded in contemporary neuroscience, these internal components interact to generate observed, often far-from-rational, individual behavior. When multiple agents of this new type move and interact spatially, they collectively generate an astonishing range of dynamics spanning the fields of social conflict, psychology, public health, law, network science, and economics.

Epstein weaves a computational tapestry with threads from Plato, Hume, Darwin, Pavlov, Smith, Tolstoy, Marx, James, and Dostoevsky, among others. This transformative synthesis of social philosophy, cognitive neuroscience, and agent-based modeling will fascinate scholars and students of every stripe. Epstein's computer programs are provided in the book or on its Princeton University Press website, along with movies of his "computational parables."

Agent_Zero is a signal departure in what it includes (e.g., a new synthesis of neurally grounded internal modules), what it eschews (e.g., standard behavioral imitation), the phenomena it generates (from genocide to financial panic), and the modeling arsenal it offers the scientific community."

To get an idea of what to expect the movie below is from the JHGCCO Seminar Series on Systems Science and Obesity where Epstein introduces Agent_Zero.

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.

CulturalComplexity.net

I just been exploring culturalcomplexity.net, a website based on the research lab of Cultural Complexity at The University of Western Ontario, Canada.

The lab is interested in understanding processes related "...to the creation, transmission, and representation of culture, and how these processes shape the human experience" combing different academic disciplines (e.g. philosophy, mathematics, computer science, and economics). One thing that caught my attention is the Virtual Laboratory for the Study of Cultural Dynamics (VCL), a NetLogo model which explores how information changes when exchanged among individuals (click here to read more about the model).

The models are a nice example on how different .txt files can be loaded into NetLogo along with how altering certain parameters at run time alters the results. The VCL editor (used to define the world one wishes to simulate) can be found here, while the VCL machine (which then runs the simulation, generating results) can be found here.