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)

 

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)

 

Postdoctoral Associate Position Available

Come work with me!

The Department of Geography at the University at Buffalo (UB) invites applicants for a postdoctoral researcher position with broad interests in urban analytics with a particular emphasis on modeling and simulation. 

The postdoctoral researcher will be expected develop systems to collect and store social media data and use such data to inform and develop a portfolio of innovative agent-based models utilizing machine learning methods to study urban areas. The ideal candidate will have experience with developing agent-based models, social network analysis, GIS and machine learning.  

Key responsibilities:

• Collecting, storing and analyzing social media data for a variety of urban topics.
• Developing geographically explicit agent-based models.
• Presenting material to broader community (i.e., publications conference presentations).

Required knowledge skills and abilities: 

• PhD. in geography, computer science or a related field (or expect to have obtained their PhD. by the time the post commences).  
• Ability to work independently and in an interdisciplinary research team.
• Excellent written communication skills demonstrated by prior publications.
• Strong programming skills in Python or Java.

Preferred Qualifications

• Familiarity with GIS, machine learning and social media data.
• Experience with developing agent-based models.

This position is affiliated with the Department of Geography at the University at Buffalo (UB), a large R1 Research University on the shores of Lake Erie. 

Outstanding Benefits Package

Working at UB comes with benefits that exceed salary alone. There are personal rewards including comprehensive health and retirement plan options. We also focus on creating and sustaining a healthy mix of work, personal and academic pursuit – all in an effort to support your work-life effectiveness. Visit our benefits website to learn about our benefit packages. 

About UB

The University at Buffalo is SUNY’s most comprehensive public research university, and an outstanding place to work. UB amplifies ambition for faculty and staff by offering endless possibilities to achieve more. Here, people from all backgrounds and cultures challenge and inspire each other to discover, learn and succeed. Dedicated staff and engaged faculty collaborate to further knowledge and understanding, and develop tenacious graduates who are valued for their talents and their impact on global society. Visit our website to learn more about the University at Buffalo.

As an Equal Opportunity / Affirmative Action employer, the Research Foundation will not discriminate in its employment practices due to an applicant’s race, color, religion, sex, sexual orientation, gender identity, national origin and veteran or disability status.

 

Further Details and How to Apply: https://www.ubjobs.buffalo.edu/postings/29858

Generation of Reusable Synthetic Population and Social Networks

Building on our work on synthetic populations, Na (Richard) Jiang, Bill Kennedy, Hamdi Kavak, and myself have a new paper which was presented at the 2021 Annual Modeling and Simulation (ANNSIM) Conference entitled "Generation of Reusable Synthetic Population And Social Networks for Agent-Based Modeling." Rather than go into details about the paper, below is the abstract and as the conference was virtual, the presentation which accompanies the paper was prerecorded by Richard and is embedded below. If you want to find out more, at the bottom of the post there is a link to a draft of the paper.

Abstract: Within agent-based models, agents interact with each other (e.g., social networks) and their environment, and it is through such interactions more aggregate patterns emerge (e.g., disease outbreaks, traffic jams). While the popularity of agent-based modeling has grown, one challenge remains, that of creating and sharing realistic synthetic populations which incorporate social networks. To overcome this challenge, this paper introduces a new approach that creates a reusable synthetic population using the New York Metro Area as a study area. Our method directly incorporates social networks (i.e., connections within a family or workplace) when creating a synthetic population. To demonstrate the utility and reusability of the synthetic population and to highlight the role of social networks, we show two example applications: traffic dynamics and the spread of a disease. These applications demonstrate how our synthetic population method can be easily utilized for different modeling problems. 

Keywords: Synthetic Population, Agent-Based Modeling, New York, Traffic Dynamics, Disease Models.

  Full Reference: 

Jiang, N., Crooks, A.T., Kennedy, W.G., and Kavak, H. (2021), Generation of Reusable Synthetic Population And Social Networks for Agent-Based Modeling, 2021 Annual Modeling and Simulation Conference (ANNSIM). (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)