Abstract
Beauty is subjective, and as such it, of course, cannot be defined in absolute terms. But we all know or feel when something is beautiful to us personally. And in such instances, methods of statistical physics and network science can be used to quantify and to better understand what it is that evokes that pleasant feeling, be it when reading a book or looking at a painting. Indeed, recent large-scale explorations of digital data have lifted the veil on many aspects of our artistic expressions that would remain forever hidden in smaller samples. From the determination of complexity and entropy of art paintings to the creation of the flavour network and the principles of food pairing, fascinating research at the interface of art, physics and network science abounds. We here review the existing literature, focusing in particular on culinary, visual, musical and literary arts. We also touch upon cultural history and culturomics, as well as on the connections between physics and the social sciences in general. The review shows that the synergies between these fields yield highly entertaining results that can often be enjoyed by layman and experts alike. In addition to its wider appeal, the reviewed research also has many applications, ranging from improved recommendation to the detection of plagiarism.
Highlights
The past decade has seen data science emerge as the new buzzword in the world of research
We review research dedicated to culinary arts, visual arts and musical arts, and where appropriate we touch upon cultural history [26,27] and culturomics [28], and we describe the connections between physics and the social sciences
Haptic artworks depict objects as tangible discrete entities, isolated and circumscribed, whereas optic artworks represent objects as interrelated in deep space by exploiting light, colour and shadow effects to create the idea of an open spatial continuum. Relating this to complexity and entropy, it was shown that linear/haptic artworks are described by small values of entropy H and large values of complexity C, while painterly/optic artworks are expected to yield larger values of H and smaller values of C
Summary
The past decade has seen data science emerge as the new buzzword in the world of research. Visualization wizardry is likewise key for the data science to shine through, as opposed to rather one-dimensional and often dull representations one often finds in hardcore statistics books It is this deluge of digital data that is often the bridge between the social and natural sciences, and, as we will review in what follows, between art and natural sciences, and between art and physics and network science in particular. An excellent growth pattern over the past couple of decades, spurred on by advances in statistical and computational physics, the availability of data, and the coming of age of related fields of research such as network science [13,14,15,16] and computational social science [17] Looking at this development historically, it is, safe to claim that synergies between physical and social sciences have been around for centuries. We conclude with a discussion of the reviewed research and an outlook for future research
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