Waves of novelties in the expansion into the adjacent possible

Abstract

The emergence of novelties and their rise and fall in popularity is an ubiquitous phenomenon in human activities. The coexistence of popular evergreens with novel and sometimes ephemeral trends pervades technological, scientific and artistic production. Though this phenomenon is very intuitively captured by our common sense, a comprehensive explanation of how waves of novelties are not hampered by well established old-comers is still lacking. Here we first quantify this phenomenology by empirically looking at different systems that display innovation at very different levels: the creation of hashtags in Twitter, the evolution of online code repositories, the creation of texts and the listening of songs on online platforms. In all these systems surprisingly similar patterns emerge as the non-trivial outcome of two contrasting forces: the tendency of retracing already explored avenues (exploit) and the inclination to explore new possibilities. These findings are naturally explained in the framework of the expansion of the adjacent possible, a recently introduced theoretical framework that postulates the restructuring of the space of possibilities conditional to the occurrence of innovations. The predictions of our theoretical framework are borne out in all the phenomenologies investigated, paving the way to a better understanding and control of innovation processes.

Network Dynamics of Innovation Processes

“…4(a) shows that the frequency distribution f (∆t) of interevent times ∆t between pairs of consecutive occurrences of the same concept is a power law, like the ones found for novelties in Wikipedia and in other data sets in Refs. [9,10]. Furthermore, the shape of f (∆t) in our model significantly differs from that obtained by reshuffling the sequences locally and globally (see [68]).…”

A Neuroeconomic Framework for Creative Cognition

“…In addition, the criteria defining the "correctness" of any given solution or idea might be relatively unclear and could even change over time. In this sense, discovering and stumbling upon better alternatives through exploration (triggered by the tonic mode) might be a particularly apt characterization of real-world creativity (Monechi et al, 2017). Consistent with these ideas, divergent thinking in the laboratory has been associated with selective attention, whereas creative real-world achievement has been associated with wider attentional focus and failures to inhibit seemingly irrelevant stimuli (Carson, Peterson, & Higgins, 2003;Zabelina, Colzato, Beeman, & Hommel, 2016;Zabelina, O'Leary, Pornpattananangkul, Nusslock, & Beeman, 2015;.…”

Zipf’s, Heaps’ and Taylor’s Laws are Determined by the Expansion into the Adjacent Possible

“…Here we wish to remark that the urn representation of the PUT model allows for straightforward generalizations where correlations can be explicitly taken into account (see for instance [ 10 ] for a first step in this direction). In addition, it can be easily rephrased in terms of walks in a complex space (for instance a graph), allowing to consider more complex underlying structures for the space of possibilities (see for instance the SI of [ 10 , 34 , 35 ]).…”