Abstract
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.
Highlights
Artificial Life (ALife) has contributed to our understanding of biological processes in real life and in “life as it could be” [43], with particular emphasis on understanding emergent processes: those processes by which something new comes about through the interaction of existing elements
This article has given a brief overview of computational social creativity
We have shown that Computational Social Creativity (CSC) is already a lively area of research with roots common to ALife and Computational Social Science (CSS), but have attempted to better define CSC as a unique field with independent concerns
Summary
Artificial Life (ALife) has contributed to our understanding of biological processes in real life and in “life as it could be” [43], with particular emphasis on understanding emergent processes: those processes by which something new comes about through the interaction of existing elements. A third related field of multi-agent modelling directly concerned with the study of creativity can be distinguished It is at an earlier stage of development, with work often falling under the scope of ALife, CSS or Computational Creativity (CC) [48]. The goal of CSC is to contribute to the understanding of human creativity as a social phenomenon using multi-agent computational models, and to contribute more generally to an understanding of creativity We present a number of opportunities provided by a CSC approach, and lay out a set of requirements for computational models of social creativity, based on similar theoretical discussions in ALife and CSS. Csikszentmihalyi proposed a systems views of creativity [19], which he later developed into the Domain Individual Field Interaction (DIFI) theory of creativity [28]. The goal of building artificial creative systems is the primary focus of CC research, to which we turn
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