scholarly journals Computational Social Creativity

2015 ◽  
Vol 21 (3) ◽  
pp. 366-378 ◽  
Author(s):  
Rob Saunders ◽  
Oliver Bown
Keyword(s):  
Social Science ◽  
Computational Modeling ◽  
Artificial Life ◽  
Computational Models ◽  
Critical Role ◽  
Computational Social Science ◽  
Social Phenomena ◽  
Creativity Research ◽  
Social Creativity ◽  
Carry Over

This article reviews the development of computational models of creativity where social interactions are central. We refer to this area as computational social creativity. Its context is described, including the broader study of creativity, the computational modeling of other social phenomena, and computational models of individual creativity. Computational modeling has been applied to a number of areas of social creativity and has the potential to contribute to our understanding of creativity. A number of requirements for computational models of social creativity are common in artificial life and computational social science simulations. Three key themes are identified: (1) computational social creativity research has a critical role to play in understanding creativity as a social phenomenon and advancing computational creativity by making clear epistemological contributions in ways that would be challenging for other approaches; (2) the methodologies developed in artificial life and computational social science carry over directly to computational social creativity; and (3) the combination of computational social creativity with individual models of creativity presents significant opportunities and poses interesting challenges for the development of integrated models of creativity that have yet to be realized.

Computational Social Science, Big Data, and Networks

2020 ◽  
pp. 515-534
Author(s):  
Bruno Abrahao ◽  
Paolo Parigi
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Social Science ◽  
Social Life ◽  
Computational Models ◽  
Social Space ◽  
Computational Social Science ◽  
Social Processes ◽  
New Science ◽  
Building Models

The emergence of Big data and a quantified social space has prompted the birth of a new science, computational social science (CSS), whose roots are founded in research aiming to describe social processes using computational models. Big data now fuels rapid advancements in the field, providing the basis for building models and algorithms of human behavior. New sources of massive amounts of data fundamentally reflect interactions, and, in this context, networks are intuitive abstractions to model our social life, especially that mediated by technology. The chapter introduces several examples of empirical and theoretical CSS research employing network analysis, machine learning and online experiments. It concludes with a list of challenges confronting CSS practitioners, in and outside of academia.

Representing qualitative social science in computational models to aid reasoning under uncertainty: National security examples

2016 ◽  
Vol 14 (1) ◽  
pp. 57-78 ◽  
Author(s):  
Paul K Davis ◽  
Angela O’Mahony
Keyword(s):  
Social Science ◽  
Computational Modeling ◽  
National Security ◽  
Computational Models ◽  
Empirical Work ◽  
Empirical Testing ◽  
Special Issue ◽  
Reasoning Under Uncertainty ◽  
Social Science Knowledge ◽  
Qualitative Models

Representing causal social science knowledge in models is difficult: much of the best knowledge is qualitative and ambiguously conditional, unlike the knowledge in “physics models.” This paper describes a stream of RAND research that began with qualitative models providing a structured depiction of casual factors creating effects. That has subsequently been extended to an unusual kind of uncertainty sensitive computational modeling that enables exploratory reasoning and analysis. We illustrate the approach with applications to counterterrorism, detection of terrorists, and nuclear crises. We believe that the approach will complement other approaches that can reflect social science phenomena [see other papers in this special issue of JDMS] and that the approach has broad potential within and beyond the national security domain. We also believe that it has the potential to inform empirical work—encouraging a transition from the step-by-step empirical testing of simple discrete hypotheses to the testing and refinement of more comprehensive causal models.

Towards Computational and Behavioral Social Science

2016 ◽  
Vol 21 (2) ◽  
pp. 131-140 ◽  
Author(s):  
Rosaria Conte ◽  
Francesca Giardini
Keyword(s):  
Big Data ◽  
Social Science ◽  
Data Science ◽  
Behavioral Science ◽  
Big Data Analytics ◽  
Computational Social Science ◽  
Evolution Of Cooperation ◽  
Social Phenomena ◽  
Integrative View ◽  
Social Data Mining

Abstract. In the last few years, the study of social phenomena has hosted a renewal of interest in Computational Social Science (CSS). While this field is not new – Axelrod’s first computational work on the evolution of cooperation goes back to 1981 – CSS has recently resurged under the pressure of quantitative social science and the application of Big Data analytics to social datasets. However, Big Data is no panacea and the data deluge that it provides raises more questions than it answers. The aim of this paper is to present an overview in which CSS will be introduced and the costs of CSS will be balanced against its benefits, in an attempt to propose an integrative view of the new and the old practice of CSS. In particular, two routes to integration will be drawn. First, it will be advocated that social data mining and computational modeling need to be integrated. Second, we will introduce the generative approach, aimed to understand how social phenomena can be generated starting from the micro-components, including psychological mechanisms, and we will discuss the necessity of combining it with the anticipatory, data-driven objective. By these means, Computational Social Science will develop into a more comprehensive field of Computational Social and Behavioral Science in which data science, ICT, as well as the behavioral and social sciences will be fruitfully integrated.

“We Speak the Truth!”

2019 ◽  
Vol 12 (2) ◽  
pp. 53-80
Author(s):  
Sari Hanafi
Keyword(s):  
Social Sciences ◽  
Content Analysis ◽  
Social Science ◽  
The Political ◽  
Structured Interviews ◽  
Social Phenomena ◽  
The Social ◽  
Academic Qualifications

This study investigates the preachers and their Friday sermons in Lebanon, raising the following questions: What are the profiles of preachers in Lebanon and their academic qualifications? What are the topics evoked in their sermons? In instances where they diagnosis and analyze the political and the social, what kind of arguments are used to persuade their audiences? What kind of contact do they have with the social sciences? It draws on forty-two semi-structured interviews with preachers and content analysis of 210 preachers’ Friday sermons, all conducted between 2012 and 2015 among Sunni and Shia mosques. Drawing from Max Weber’s typology, the analysis of Friday sermons shows that most of the preachers represent both the saint and the traditional, but rarely the scholar. While they are dealing extensively with political and social phenomena, rarely do they have knowledge of social science

Computing Possible Futures

2019 ◽  
Author(s):  
William B. Rouse
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computational Modeling ◽  
Mental Model ◽  
Data Analytics ◽  
Computational Models ◽  
Senior Managers ◽  
Interactive Visualizations ◽  
Use Of Models ◽  
Better Than

This book discusses the use of models and interactive visualizations to explore designs of systems and policies in determining whether such designs would be effective. Executives and senior managers are very interested in what “data analytics” can do for them and, quite recently, what the prospects are for artificial intelligence and machine learning. They want to understand and then invest wisely. They are reasonably skeptical, having experienced overselling and under-delivery. They ask about reasonable and realistic expectations. Their concern is with the futurity of decisions they are currently entertaining. They cannot fully address this concern empirically. Thus, they need some way to make predictions. The problem is that one rarely can predict exactly what will happen, only what might happen. To overcome this limitation, executives can be provided predictions of possible futures and the conditions under which each scenario is likely to emerge. Models can help them to understand these possible futures. Most executives find such candor refreshing, perhaps even liberating. Their job becomes one of imagining and designing a portfolio of possible futures, assisted by interactive computational models. Understanding and managing uncertainty is central to their job. Indeed, doing this better than competitors is a hallmark of success. This book is intended to help them understand what fundamentally needs to be done, why it needs to be done, and how to do it. The hope is that readers will discuss this book and develop a “shared mental model” of computational modeling in the process, which will greatly enhance their chances of success.

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