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Modeling, Social Science, and Computational Social Science

Link to Models:

About Modeling, Social Science, and Social Complexity

While it is not necessarily true that information must be easy to understand in order to be useful, clarity and ease of access do ensure that the greatest amount of information can be made available to the greatest number of individuals for the greatest number of uses. So, clarity and ease of access have become de facto goals for data management professionals. Computer generated images, or “models,” can represent thousands, even millions, of bits of discrete information in order to simulate behavior, identify patterns, and anticipate outcomes. The software used to process the data and the model chosen to represent it are dependent on a number of things, including the topic and purpose of the study, the field and expertise of the researcher, the unique features of the software and the equipment, and the anticipated use of the data.

Social science supports the scientific study of society and the relationships among individuals and/or groups within a society. Anthropology, economics, archaeology, history, psychology, law, social studies, geography, linguistics, and political science are considered to be disciplines in the “social sciences.” Applying scientific principles to the study of people and society makes it possible to perceive, measure, and chart behavior and patterns of behavior, leading to deeper understanding of society and social agents and providing us with tools that we can use to identify needs and effect change.

Computers give us the capacity to collect, manage, and analyze vast amounts of information about individuals and groups. This information is increasingly valuable to various institutions and groups of individuals, from economists to advertisers to retailers to health care providers to government agencies – the list is nearly endless. Computational Social Science is the science of complex social systems and their investigation through computational modeling and related techniques. The gathering of data and the presentation of it in meaningful ways is the purview of the Computational Social Scientist; the interpretation of data and the use that is made of it is determined by whatever issue or discipline has driven the research. An over-time study of adverse weather conditions in a given part of the world, for example, could have relevance for issues such as population sustenance, plant and animal survival, migration patterns, economic fluctuations, need for support and emergency services, education, employment, urban planning – again, the list could go on and on.

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Caption: An agent-based model of migration: top: the spatial environment, where the lines represent migration pathways, and the nodes represent number of migrants. Purple nodes represent final destination sites, red nodes show migrant deaths, and green nodes show migrants en route.

Read paper: Curry, T., Croitoru, A., Crooks, A.T., and Stefanidis, A. (2018), Generating and Analyzing Spatial Social Networks. Crowdsourcing Geographical and Social Trails of Mass Migration.

Given the nature of migration processes, it is possible to explore them across two key dimensions: geographical and situational. The geographical dimension is associated with the physical migration pathways migrants take from a country of origin to a destination site (often through a number of intermediate “stop” sites). The situational dimension is associated with the social connectivity of moving migrant populations, the conditions on the ground, and the activities that take place as part of migration efforts (including the root conditions, proximate conditions and triggering events). Click here for full information on the paper.

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Slide 2: Mason’s Heatbugs, Flockers, and CampusWorld Models

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This image is from a paper describing Distributed MASON, a distributed version of the MASON agent-based simulation tool. Distributed MASON is architected to take advantage of well known principles from Parallel and Discrete Event Simulation, such as the use of Logical Processes (LP) as a method for obtaining scalable and high performing simulation systems. We first explain data management and sharing between LPs and describe our approach to load balancing. We then present both a local greedy approach and a global hierarchical approach. Finally, we present the results of our implementation of Distributed MASON on an instance in the Amazon Cloud, using several standard multi-agent models. The results indicate that our design is highly scalable and achieves our expected levels of speed-up.

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Slide 3: Overall Methodology to Analyze BOT Evidence across Multiple Twitter OSN Conversations
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The emergence of social bots within online social networks (OSNs) to diffuse information at scale has given rise to many efforts to detect them. While methodologies employed to detect the evolving sophistication of bots continue to improve, much work can be done to characterize the impact of bots on communication networks. In this study, we present a framework to describe the pervasiveness and relative importance of participants recognized as bots in various OSN conversations. Specifically, we harvested over 30 million tweets from three major global events in 2016 (the U.S. Presidential Election, the Ukrainian Conflict and Turkish Political Censorship) and compared the conversational patterns of bots and humans within each event. We further examined the social network structure of each conversation to determine if bots exhibited any particular network influence, while also determining bot participation in key emergent network communities. The results showed that although participants recognized as social bots comprised only 0.28% of all OSN users in this study, they accounted for a significantly large portion of prominent centrality rankings across the three conversations. This includes the identification of individual bots as top-10 influencer nodes out of a total corpus consisting of more than 2.8 million nodes.

Read paper:
Bots in Nets: Empirical Comparative Analysis of Bot Evidence in Social Networks. Authors:Ross Schuchard, Andrew Crooks, Anthony Stefanidis, Arie Croitoru

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Slumulation: An Agent-based Modeling Approach to Slum Formations

A. Patel, A.T. Crooks, N. Koizumi

Slums provide shelter for nearly one third of the world’s urban population, most of them in the developing world. Slumulation represents an agent-based model which explores questions such as i) how slums come into existence, expand or disappear ii) where and when they emerge in a city and iii) which processes may improve housing conditions for urban poor.

Read paper:
Slumulation: an Agent-based Modeling Approach to Slum Formation, Patel, A., Crooks, A.T. and Koizumi, N. (2012).

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Read paper:
Tian, Q., Brown, D.G., Bao, S, Qi, S. (2015). Assessing and mapping human well-being for sustainable development amid flood hazards: Poyang Lake Region of China. Applied Geography, 63, 66-76.

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Slide 6: Topology of Labor Flow Network from Finland

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Guerrero, O.A. and Axtell, R.L. (2013)

Read Paper:
Employment Growth through Labor Flow Networks.

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Slide 7: Flooding Disasters

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Read paper:
Towards Representing Disasters in Computational Social Simulations, The Computational Social Science Society of America Conference (2013), Santa Fe, NM.

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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.

Read paper:
Employment Growth through Labor Flow Networks.

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A sample of agent-based models linked to geographical information.

Read paper:
Crooks, A.T. (2015)Agent-based Models and Geographical Information Systems in Brunsdon, C. and Singleton, A. (eds.), Geocomputation: A Practical Primer, Sage, London, UK, pp. 63-77

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Real-life smuggling corridors and a sample of simulated movement trails used for face validity and qualitative model checking. To find out more information:

Read paper:
Łatek, M.M., Mussavi Rizi, S.M., Crooks, A.T. and Fraser, M. (2012), ‘Social Simulations for Border Security’, Workshop on Innovation in Border Control 2012, Co-located with the European Intelligence and Security Informatics Conference (EISIC 2012), Odense, Denmark.

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