adjacent possible air traffic complex network complexity complex_networks complex_systems creativity cuskley data_compression dynamical_systems evolutionary_dynamics gravino information_theory innovation_dynamics kreyon language_dynamics language_games local optimization loreto monechi opinion_dynamics phylogeny relevant_literature servedio social_dynamics statistical_physics techno_social_systems tria XTribe zippers
2016 |
Monechi, Bernardo; Ruiz-Serrano, Alvaro; Tria, Francesca; Loreto, Vittorio Waves of Novelties in the Expansion into the Adjacent Possible (Journal Article) PloS one, 12 (6), pp. e0179303, 2016. (Abstract | Links | BibTeX | Tags: adjacent possible, complex systems, innovation, kreyon, popularity, trends) @article{Monechi2016, title = {Waves of Novelties in the Expansion into the Adjacent Possible}, author = {Bernardo Monechi and Alvaro Ruiz-Serrano and Francesca Tria and Vittorio Loreto }, editor = {Public Library of Science}, url = {http://www.socialdynamics.it/pubs/}, year = {2016}, date = {2016-03-21}, journal = {PloS one}, volume = {12}, number = {6}, pages = {e0179303}, abstract = {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. }, keywords = {adjacent possible, complex systems, innovation, kreyon, popularity, trends}, pubstate = {published}, tppubtype = {article} } 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. |
2012 |
Miguel, Maxi San; Johnson, Jeffrey; Kertesz, Janosz; Kaski, Kimmo; Diaz-Guilera, Albert; Mackay, Robert; Loreto, Vittorio; Erdi, Peter; Helbing, Dirk Challenges in complex systems science (Journal Article) THE EUROPEAN PHYSICAL JOURNAL. SPECIAL TOPICS, 214 , pp. 245–271, 2012. (Abstract | BibTeX | Tags: complex systems, loreto, social_dynamics) @article{b, title = {Challenges in complex systems science}, author = {Maxi San Miguel and Jeffrey H. Johnson and Janosz Kertesz and Kimmo Kaski and Albert Diaz-Guilera and Robert S. Mackay and Vittorio Loreto and Peter Erdi and Dirk Helbing}, year = {2012}, date = {2012-01-01}, journal = {THE EUROPEAN PHYSICAL JOURNAL. SPECIAL TOPICS}, volume = {214}, pages = {245--271}, publisher = {SPRINGER HEIDELBERG}, abstract = {FuturICT foundations are social science, complex systems science, and ICT. The main concerns and challenges in the science of complex systems in the context of FuturICT are laid out in this paper with special emphasis on the Complex Systems route to Social Sciences. This include complex systems having: many heterogeneous interacting parts; multiple scales; complicated transition laws; unexpected or unpredicted emergence; sensitive dependence on initial conditions; path-dependent dynamics; networked hierarchical connectivities; interaction of autonomous agents; self-organisation; non-equilibrium dynamics; combinatorial explosion; adaptivity to changing environments; co-evolving subsystems; ill-defined boundaries; and multilevel dynamics. In this context, science is seen as the process of abstracting the dynamics of systems from data. This presents many challenges including: data gathering by large-scale experiment, participatory sensing and social computation, managing huge distributed dynamic and heterogeneous databases; moving from data to dynamical models, going beyond correlations to cause-effect relationships, understanding the relationship between simple and comprehensive models with appropriate choices of variables, ensemble modeling and data assimilation, modeling systems of systems of systems with many levels between micro and macro; and formulating new approaches to prediction, forecasting, and risk, especially in systems that can reflect on and change their behaviour in response to predictions, and systems whose apparently predictable behaviour is disrupted by apparently unpredictable rare or extreme events. These challenges are part of the FuturICT agenda.}, keywords = {complex systems, loreto, social_dynamics}, pubstate = {published}, tppubtype = {article} } FuturICT foundations are social science, complex systems science, and ICT. The main concerns and challenges in the science of complex systems in the context of FuturICT are laid out in this paper with special emphasis on the Complex Systems route to Social Sciences. This include complex systems having: many heterogeneous interacting parts; multiple scales; complicated transition laws; unexpected or unpredicted emergence; sensitive dependence on initial conditions; path-dependent dynamics; networked hierarchical connectivities; interaction of autonomous agents; self-organisation; non-equilibrium dynamics; combinatorial explosion; adaptivity to changing environments; co-evolving subsystems; ill-defined boundaries; and multilevel dynamics. In this context, science is seen as the process of abstracting the dynamics of systems from data. This presents many challenges including: data gathering by large-scale experiment, participatory sensing and social computation, managing huge distributed dynamic and heterogeneous databases; moving from data to dynamical models, going beyond correlations to cause-effect relationships, understanding the relationship between simple and comprehensive models with appropriate choices of variables, ensemble modeling and data assimilation, modeling systems of systems of systems with many levels between micro and macro; and formulating new approaches to prediction, forecasting, and risk, especially in systems that can reflect on and change their behaviour in response to predictions, and systems whose apparently predictable behaviour is disrupted by apparently unpredictable rare or extreme events. These challenges are part of the FuturICT agenda. |