We often relate to innovation as if it could be an answer to our questions and needs. But what if innovations are actually the questions? Each advance raises an increasing number of open questions. On one side they unlock a wider range of opportunities, possibly leading to new discoveries (both at a collective and individual level). On the other side, each creative exploit can have a potentially huge impact on humans, at cultural, societal, and psychological level. The focus of my research is twofold: the study of the exploration of the space of the unknown (in particular the so-called Adjacent Possible) and the assessment of the impact of this exploration on humans. I try to understand exploration behaviours in cultural systems, how creativity can emerge in such processes and the impact of innovations. The aim is to learn how we can use technology to “augment” our exploration and to improve our creativity.
Railways are a key infrastructure for any modern country. The reliability and resilience of this peculiar transportation system may be challenged by different shocks such as disruptions, strikes and adverse weather conditions. These events compromise the correct functioning of the system and trigger the spreading of delays into the railway network on a daily basis. Despite their importance, a general theoretical understanding of the underlying causes of these disruptions is still lacking. In this work, we analyse the Italian and German railway networks by leveraging on the train schedules and actual delay data retrieved during the year 2015. We use these data to infer simple statistical laws ruling the emergence of localized delays in different areas of the network and we model the spreading of these delays throughout the network by exploiting a framework inspired by epidemic spreading models. Our model offers a fast and easy tool for the preliminary assessment of the effectiveness of traffic handling policies, and of the railway network criticalities.
Creative industries constantly strive for fame and popularity. Though highly desirable, popularity is not the only achievement artistic creations might ever acquire. Leaving a longstanding mark in the global production and influencing future works is an even more important achievement, usually acknowledged by experts and scholars. ‘Significant’ or ‘influential’ works are not always well known to the public or have sometimes been long forgotten by the vast majority. In this paper, we focus on the duality between what is successful and what is significant in the musical context. To this end, we consider a user-generated set of tags collected through an online music platform, whose evolving co-occurrence network mirrors the growing conceptual space underlying music production. We define a set of general metrics aiming at characterizing music albums throughout history, and their relationships with the overall musical production. We show how these metrics allow to classify albums according to their current popularity or their belonging to expert-made lists of important albums. In this way, we provide the scientific community and the public at large with quantitative tools to tell apart popular albums from culturally or aesthetically relevant artworks. The generality of the methodology presented here lends itself to be used in all those fields where innovation and creativity are in play.
The complex organization of syntax in hierarchical structures is one of the core design features of human language. Duality of patterning refers for instance to the organization of the meaningful elements in a language at two distinct levels: a combinatorial level where meaningless forms are combined into meaningful forms and a compositional level where meaningful forms are composed into larger lexical units. The question remains wide open regarding how such a structure could have emerged. Furthermore a clear mathematical framework to quantify this phenomenon is still lacking. The aim of this paper is that of addressing these two aspects in a self-consistent way. First, we introduce suitable measures to quantify the level of combinatoriality and compositionality in a language, and present a framework to estimate these observables in human natural languages. Second, we show that the theoretical predictions of a multi-agents modeling scheme, namely the Blending Game, are in surprisingly good agreement with empirical data. In the Blending Game a population of individuals plays language games aiming at success in communication. It is remarkable that the two sides of duality of patterning emerge simultaneously as a consequence of a pure cultural dynamics in a simulated environment that contains meaningful relations, provided a simple constraint on message transmission fidelity is also considered.
It is common opinion that many innovations are triggered by serendipity whose notion is associated with fortuitous events leading to unintended consequences. One might argue that this interpretation is due to the poor understanding of the dynamics of innovations. Very little is known, in fact, about how innovations proceed and samples the space of potential novelties. This space is usually referred to as the adjacent possible, a concept originally introduced in the study of biological systems to indicate the set of possibilities that are one step away from what actually exists. In this paper we focus on the problem of defining the adjacent possible space, and analyzing its dynamics, for a particular system, namely the cultural system of the network of movies. We synthesized to this end the graph emerging from the Internet Movies Database (IMDb) and looked at the static and dynamical properties of this network. We deal, in particular, with the subtle mechanism of the adjacent possible by measuring the expansion and the coverage of this elusive space during the global evolution of the system. Finally, we introduce the concept of adjacent possibilities at the level of single node and try to elucidate its nature by looking at the correlations with topological and user annotation metrics.
The dynamics of political votes has been widely studied, both for its practical interest and as a paradigm of the dynamics of mass opinions and collective phenomena, where theoretical predictions can be easily tested. However, the vote outcome is often influenced by many factors beyond the bare opinion on the candidate, and in most cases it is bound to a single preference. The voter perception of the political space is still to be elucidated. We here propose a web experiment (laPENSOcos’i) where we explicitly investigate participant’s opinions on political entities (parties, coalitions, individual candidates) of the Italian political scene. As a main result, we show that the political perception follows a Weber-Fechner-like law, i.e., when ranking political entities according to the user expressed preferences, the perceived distance of the user from a given entity scales as the logarithm of this rank.