Determinants of creativity and innovation in science, art and technology  (DCIS) Session 1

Abstract: Many evolutionary systems are combinatorial in the sense that the creation of new entities is based on the combination of already existing things. By formalising this kind of dynamics into mathematical models one can realise that evolutionary systems are prototypes of complex systems, where the underlying network structure ? that determines the next possible steps in evolution (adjacent possible)? co-evolves with the population of phase space (which things currently exist). We show that these models are self-organised critical and therefore are able to capture several key features of evolutionary systems, such as power laws in creation and extinction statistics, punctuated equilibria, and phases of massive and rapid re-structuring. We show an example where the model can be used to explain innovation dynamics as seen in world trade data. Close

Abstract: In this talk, we will present a summary of our interdisciplinary research project ?Evolutionary Perspective on Collective Decision Making? that was conducted through close collaboration between computational, organizational, and social scientists at Binghamton University. We redefined collective human decision making and creativity as evolution of ecologies of ideas, where populations of ideas evolve via continual applications of evolutionary operators such as reproduction, recombination, mutation, selection, and migration of ideas, each conducted by participating humans. Based on this evolutionary perspective, we generated hypotheses about collective human decision making and creativity, using agent-based computer simulations. The hypotheses were then tested through several experiments with real human subjects. Throughout this project, we utilized evolutionary computation (EC) in non-traditional ways? as a theoretical framework for reinterpreting the dynamics of idea generation and selection, as a computational simulation model of collective human decision-making processes, and as a research tool for collecting high-resolution experimental data on actual collaborative design and decision making from human subjects. Close

Abstract: Recently, large databases witnessing human activities allowed the observation that novelties - such as the individual process of listening a song for the first time - and innovation processes - such as the fixation of new genes in a population of bacteria - share striking statistical regularities. Theoretical results drew attention to the mechanism of expansion into the adjacent possible, originally proposed by Stuart Kauffman in the framework of biological evolution, as a very general and powerful mechanism able to explain such regularities. This translates mathematically in looking at the evolution of systems where innovation occurs, as a path in a complex space, whose structure and topology get continuously reshaped and expanded by the occurrence of the new. I will present a general framework based on Polya?s urn able to account for many of the statistical regularity measured in the analyzed databases. Close

Abstract: When looking at the history of technology, we can see that all inventions are not of equal importance. Only of a few technologies have the potential to start a new branching series (specifically, by increasing diversity), have a lasting impact in human life and ultimately became turning points. Technological transitions correspond to times and places in the past when a large number of novel artefact forms or behaviours appeared together or in rapid succession. Why does that happen? Is technological innovation continuous and gradual or it occurs in sudden leaps and bounds? The evolution of information technology allows for a quantitative and theoretical approach to technological transitions. The value of information systems experiences sudden changes when when we learn how to use this technology, when we can accumulate large amounts of information and when communities of practice create and exchange information freely. The coexistence between gradual improvements and discontinuous technological change is a consequence of the asymmetric relationship between complexity and hardware and software. Using a cultural evolution approach, I suggest that sudden changes in the organization of information technologies depend on the high costs of maintaining and transmitting reliable information. Close