Creativity is still lacking of a shared definition within the scientific community.
Nevertheless, a deep understanding of the mechanisms driving the exploration of the new and the emergence of innovations could have a strong impact in the way in which we think about science, arts and society. In my research, I exploit the typical tools of Complex Systems Physics trying to find for new approaches to the study of human creativity, considering creativity as the process of exploring spaces of possibilities looking for valuable novel elements. I am also interested in the modelization of techno-social systems (Urban Environments, Railway Systems, etc.), trying to involve citizens in the research process by means of engaging gamified social experiments.
Creativity is progressively acknowledged as the main driver for progress in all sectors of humankind’s activities: arts, science, technology, business, and social policies. Nowadays, many creative processes rely on many actors collectively contributing to an outcome. The same is true when groups of people collaborate in the solution of a complex problem. Despite the critical importance of collective actions in human endeavors, few works have tackled this topic extensively and quantitatively. Here we report about an experimental setting to single out some of the key determinants of efficient teams committed to an open-ended creative task. In this experiment, dynamically forming teams were challenged to create several artworks using LEGO bricks. The growth rate of the artworks, the dynamical network of social interactions, and the interaction patterns between the participants and the artworks were monitored in parallel. The experiment revealed that larger working teams are building at faster rates and that higher commitment leads to higher growth rates. Even more importantly, there exists an optimal number of weak ties in the social network of creators that maximizes the growth rate. Finally, the presence of influencers within the working team dramatically enhances the building efficiency. The generality of the approach makes it suitable for application in very different settings, both physical and online, whenever a creative collective outcome is required
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 understanding and the characterisation of individual mobility patterns in urban environments is important in order to improve liveability and planning of big cities. In relatively recent times, the availability of data regarding human movements have fostered the emergence of a new branch of social studies, with the aim to unveil and study those patterns thanks to data collected by means of geolocalisation technologies. In this paper we analyse a large dataset of GPS tracks of cars collected in Rome (Italy). Dividing the drivers in classes according to the number of trips they perform in a day, we show that the sequence of the travelled space connecting two consecutive stops shows a precise behaviour so that the shortest trips are performed at the middle of the sequence, when the longest occur at the beginning and at the end when drivers head back home. We show that this behaviour is consistent with the idea of an optimisation process in which the total travel time is minimised, under the effect of spatial constraints so that the starting points is on the border of the space in which the dynamics takes place.
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 emergence of novelties and their rise and fall in popularity is an ubiquitous phenomenon in human activities. The coexistence of always popular milestones with novel and sometimes ephemeral trends pervades technological, scientific and artistic production. By introducing suitable statistical measures, we demonstrate that different systems of human activities, i.e. the creation of hashtags in Twitter, the interaction with online program code repositories, the creation of texts and the listening of songs on an on-line platform, exhibit surprisingly similar properties.We then introduce a general framework to explain those regularities. We propose a simple mathematical model based on the expansion into the adjacent possible, that has been proven to be a very general and powerful mechanism able to explain many of the statistical patterns emerging in innovation dynamics, to which we add two crucial elements. On the one hand we quantify the idea that, while exploring a conceptual or physical space, inertia exists towards known already discovered elements. On the other hand, we highlight the role of the collective dynamics – where many users interact, in a direct or indirect way in the emergence and diffusion of novelties and innovations.
Studies in literature and narrative have begun to argue more forcefully for considering human evolution as central to understanding stories and storytelling more generally (Sugiyama, 2001; Hernadi, 2002). However, empirical studies in language evolution have focused primarily on language structure or the language faculty, leaving the evolution of stories largely unexplored (although see Von Heiseler, 2014). Stories are unique products of human culture enabled principally by human language. Given this, the dynamics of creativity in stories, and the traits which make successful stories, are of crucial interest to understanding the evolution of language in the context of human evolution more broadly. The current work aims to illuminate how stories emerge, evolve, and change in the context of a collaborative cultural effort. We present results from a novel experimental paradigm centered around a story game where players write short continuations (between 60 and 120 characters) of existing stories. These continuations then become open to other players to continue in turn. Stories are subject to player selection, allowing for variation and speciation of the resulting narratives, and evolve as a result of collaborative effort between players. The game starts with a seed of over 60 potential stories, and players choose which stories to continue, providing a player-driven story selection mechanism. In this way, stories which are creative, intriguing, and open ended spawn more stories, and eventually lead to longer story paths as play continues. The game also introduces further limitations by constraining a players’ view of the story path: players have access only to a story and its parent, meaning knowledge of the existing narrative is limited. We present data from hundreds of players and stories, creating large story trees which explore the space of different possible narratives which grow out of a confined set of starting points. This data allows us to investigate several aspects of the growing story trees to illuminate not only what makes a story successful, but how creative stories trigger new stories, and what makes individual storytellers successful. Given the selection mechanism central to game play, we identify the most successful stories by their number of offspring. Particularly successful storytellers emerge measured both by how many children their stories have spawned, and also how long their story path extends. We also show that coherent stories often emerge, despite the fact that they are authored by several different players, and any given player only sees a limited snapshot of the story path. We contextualise the results of the game and connect it to language evolution in two ways. First, we look for detectable triggers of innovation and creativity within the story trees, and identify these as expanding the adjacent possible (e.g., new adaptations open the space of other possible adaptations in the future; Tria, Loreto, Servedio, & Strogatz, 2014). We argue that this concept can be extended to stories, using evidence from the game bolstered by evidence from more traditional literature (the Gutenberg Corpus). Second, we frame the results in terms of recurring themes found in storytelling cross-culturally (Tehrani, 2013). We suggest that the most successful triggers of innovation in stories combine original novelty and a firm grounding in existing recurring story frameworks in human culture. This indicates that much like other cultural and biological systems, stories are subject to competing pressures for stability and conservation on the one hand, and innovation and novelty on the other.
Air Transportation represents a very interesting example of a complex techno-social system whose importance has considerably grown in time and whose management requires a careful understanding of the subtle interplay between technological infrastructure and human behavior. Despite the competition with other transportation systems, a growth of air traffic is still foreseen in Europe for the next years. The increase of traffic load could bring the current Air Traffic Network above its capacity limits so that safety standards and performances might not be guaranteed anymore. Lacking the possibility of a direct investigation of this scenario, we resort to computer simulations in order to quantify the disruptive potential of an increase in traffic load. To this end we model the Air Transportation system as a complex dynamical network of flights controlled by humans who have to solve potentially dangerous conflicts by redirecting aircraft trajectories. The model is driven and validated through historical data of flight schedules in a European national airspace. While correctly reproducing actual statistics of the Air Transportation system, e.g., the distribution of delays, the model allows for theoretical predictions. Upon an increase of the traffic load injected in the system, the model predicts a transition from a phase in which all conflicts can be successfully resolved, to a phase in which many conflicts cannot be resolved anymore. We highlight how the current flight density of the Air Transportation system is well below the transition, provided that controllers make use of a special re-routing procedure. While the congestion transition displays a universal scaling behavior, its threshold depends on the conflict solving strategy adopted. Finally, the generality of the modeling scheme introduced makes it a flexible general tool to simulate and control Air Transportation systems in realistic and synthetic scenarios.
The comprehension of vehicular traffic in urban environments is crucial to achieve a good management of the complex processes arising from people collective motion. Even allowing for the great complexity of human beings, human behavior turns out to be subject to strong constraints – physical, environmental, social, economical – that induce the emergence of common patterns. The observation and understanding of those patterns is key to setup effective strategies to optimize the quality of life in cities while not frustrating the natural need for mobility. In this paper we focus on vehicular mobility with the aim to reveal the underlying patterns and uncover the human strategies determining them. To this end we analyze a large dataset of GPS vehicles tracks collected in the Rome (Italy) district during a month. We demonstrate the existence of a local optimization of travel times that vehicle drivers perform while choosing their journey. This finding is mirrored by two additional important facts, i.e., the observation that the average vehicle velocity increases by increasing the travel length and the emergence of a universal scaling law for the distribution of travel times at fixed traveled length. A simple modeling scheme confirms this scenario opening the way to further predictions.
This thesis is devoted to the study of transportation systems by means of Complex Systems and Complex Network Theories. Complex Networks are a tools of inestimable value in human transportation studies since in most of the cases the means of transportation used by individuals to move in space are bounded to move on a complex network. The topological properties of transportation networks can influence both the ability of individuals to move as well as their behavior in the environment, thus a characterization of the network is mandatory in order to understand the properties of the considered system.The two transportation systems that have been studied in this work are the Air Transport System and the mobility of cars in a urban environment.The analysis and modeling of the Air Transport System is the first and most extensive part of this thesis. In particular we will try to characterize and study the networks in which aircraft fly, exploiting these results to build a data-driven model of Air Traffic Control.The second part of the thesis is a continuation of the studies performed during by Pierpaolo Mastroianni during his Master Thesis. His work concerned the analysis of GPS tracks data in the City of Rome and the inference of statistical laws characterizing the behavior of car drivers. My contribution to his work is the development of a model capable of explaining some of the results presented in the Master Thesis.
The introduction of a new SESAR scenario in the European Airspace will impact the functioning and the performances of the current Air Traffic Management (ATM) System. The understanding of the features and the limits of the current system could be crucial in order to improve and design the structure of the future ATM. In this paper we present some results of the “Assessment of Critical Delay Patterns and Avalanche Dynamics” PhD project from the ComplexWorld Network. During this project we developed a model of Air Traffic Control (ATC) based on Complex Network theory capable of reproducing the features of the real ATC in three European National Airspaces. We then developed an optimization algorithm based on “Extremal Optimization” in order to build efficient and globally optimized planned trajectories. The ATC model is applied in order to study the efficiency of this new planned trajectories when subject to external perturbations and to compare them to the current situation.