Talks abstracts | ECCS2008 website

Sunday

This page lists the abstracts for the talks part of the Think tank event

Talks Abstracts
	Gregory Chaitin IBM Watson Research Center	*The Halting Probability Omega: Irreducible Complexity in Pure Mathematics.* How real are real numbers? In 1936 Alan Turing exhibited an example of an uncomputable real number. And using ideas of Emile Borel it can be shown that most real numbers are uncomputable, with probability one. Moreover in 1927, a decade before Turing, Borel exhibited an extremely uncomputable "know-it-all" real number, one whose Nth digit answers the Nth question in an enumeration of all possible questions. Does this real number exist? I will show how to gradually transform Borel's oracle number into a slightly more realistic number, the halting probability Omega, which shows that pure mathematics contains an infinite amount of irreducible complexity.
	Bernard Derrida Ecole Normale Supérieure	*Random trees and genealogies* The talk will review some of the statistical properties of the trees which represent the ancestry of evolving populations, both for neutral models of asexual and sexual reproduction. It will in particular show how the ages of the first common ancestors depend on the population size.
	Mauro Gallegati Universita Politecnica delle Marche	*Agent Based Models in Economics and Complexity* A crucial aspect of the complexity approach is how interacting elements produce aggregate patterns that those elements in turn react to. This leads to the emergence of aggregate properties and structures that cannot be guessed by looking only at individual behaviour. Explicitly considering how heterogeneous elements dynamically develop their behaviour through interaction is a hard task analytically, the equilibrium analysis of mainstream (neoclassical) economics being a not neutral shortcut. On the other hand, explicitly considering the dynamics of the process started to be a feasible alternative only when computer power became widely accessible. The computational study of heterogeneous interaction agents is called agent-based modelling (ABM). Interestingly, among its first applications a prominent role was given to economic models, although it was quickly found of value in other disciplines too. Goal of this lecture is to motivate the use of the complexity approach and agent-based modelling in economics, by discussing the weaknesses of the traditional paradigm of mainstream economics, and then explain what ABM is and which research and policy questions it can help to analyse.
	Shlomo Havlin Bar-Ilan	*Statistical physics and complex networks* Statistical physics approaches are developed and applied successfully in recent years to understand the topology, robustness and function of complex networks. We will show how ideas and tools from percolation theory lead to novel results on the robustness, immunization strategies, optimal paths and minimum spanning trees. These results are relevant to many real world systems ranging from the Internet to social systems and climate.
	Lord Julian Hunt Centre for Polar Observation & Modelling	*title* abstract
	Luciano Pietronero University of Rome "La Sapienza"	*Complexity: What are we talking about* This field of physics was originally identified as Solid state Physics, then P.W. Anderson coined the term Condensed Matter Physics and more recently it has merged with Statistical Physics to lead to the Physics of Complex Systems. The study of complex systems refers to the emergency of collective properties in systems with a large number of parts in interaction among them. These elements can be atoms or macromolecules in a physical or biological context, but also people, machines or companies in a socio-economic context. The science of complexity tries to discover the nature of the emerging behavior of complex systems, often invisible to the traditional approach, by focusing on the structure of the interconnections and the general architecture of systems, rather than on the individual components. It is a change of perspective in the forma mentis of scientists rather than a new scientific discipline. Traditional science is based on a reductionistic reasoning for which, if one knows the basic elements of a system, it is possible to predict its behavior and properties. It is easy to realize, however, that for a cell or for the socio-economic dynamics one faces a new situation in which the knowledge of the individual parts is not sufficient to describe the global behavior of the structure. We can represent this situation as the study of the architecture of matter and nature. It depends in some way from the individual elements (bricks) but then it shows fundamental laws and properties which cannot be derived from these elements. Starting from the simplest physical systems, like critical phenomena in which order and disorder compete, these emergent behaviors can be identified in many other systems, from ecology to the immunitary system, to the social behavior and economics. The science of complexity has the objective of understand the properties of these systems. Which rules govern their behavior? How they adapt to changing conditions? How they learn efficiently and how they optimize their behavior? The development of the science of complexity cannot be reduced to a single theoretical or technological innovation but it implies a novel scientific approach with enormous potentialities to influence deeply the scientific activities, social, economic and technological.
	Daniel Segré Boston University	*Adaptation and organization in the economy of living matter* Metabolic networks guarantee the supply of energy and building blocks necessary for the maintenance of life. Using genomic information, mathematical models, and optimality criteria, one can learn about their evolutionary history and organization principles.
	Ricard V. Solé Universitat Pompeu Fabra	*Emergence of complexity in biological networks: from selection to tinkering* Recent work has been searching for general principles of organization and evolution of natural and artificial systems changing through local rules based on reuse of previously existing substructures. Such a process of "tinkering" makes a big difference (at least in principle) when comparing biological structures and man-made artifacts. As pointed out by the French biologist François Jacob, the engineer is able to foresee the future use of the artifact (i.e. it acts as a designer) whereas evolution does not. The first can ignore previous designs, whereas the second is based on changes taking place by using available structures. In spite of its apparent drawbacks, tinkering has been able to generate most complex structures observable in the real world (including some in the technological world). Very often, the resulting structures share common principles of organization, suggesting that convergent evolution towards a limited number of basic plans is inevitable. How innovations emerge through evolution is one of the key problems in complexity. Recent work on evolved complex networks suggests that tinkering is a main driving force shaping complex systems and that several desirable properties, including modularity, might emerge for free under tinkered evolution.