Link to Models:
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.
Read paper: Alizadeh, M., Cioffi-Revilla, C. and Crooks, A. (2016), Generating and Analyzing Spatial Social Networks. Computational and Mathematical Organization Theory, DOI: 10.1007/s10588-016-9232-2.
Read paper: Crooks, A.T. and Wise, S. (2013), GIS and Agent-Based models for Humanitarian Assistance, Computers, Environment and Urban Systems, 41: 100-111.
The article presents a network-driven approach to analyze communities as they are established through different forms of bottom-up (e.g., Twitter) and top-down (e.g., UNGA voting records and international arms trade records) IRs. By constructing and comparing different network communities, we were able to evaluate the similarities between state-driven and citizen-driven networks. In order to validate our approach we identified communities in UNGA voting records during and after the Cold War.
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.
Slumulation: an Agent-based Modeling Approach to Slum Formation, Patel, A., Crooks, A.T. and Koizumi, N. (2012).
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.
Guerrero, O.A. and Axtell, R.L. (2013)
Employment Growth through Labor Flow Networks.
Towards Representing Disasters in Computational Social Simulations, The Computational Social Science Society of America Conference (2013), Santa Fe, NM.
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.
Employment Growth through Labor Flow Networks.
A sample of agent-based models linked to geographical information.
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
Real-life smuggling corridors and a sample of simulated movement trails used for face validity and qualitative model checking. To find out more information:
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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