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Introduction to Complex Systems

Ludovic Seifert (Université de Rouen - CETAPS)
Mardi 9 Juillet 2013 9h­-12h30

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Summary: This talk is in three parts. First part introduces the key properties of complex and dynamical systems such as: (i) constraint, self-organisation and emergence, (ii) non linearity (bifurcation, multi- and meta-stability, and more broadly, redundancy and degeneracy), (iii) non proportionality (sensibility to initial condition, e.g., butterfly effect; circular coupling), (iv) non predictability i.e., functional and adaptive variability, affordances (opportunities for action).
In the second part, each property is defined according to neuroscience and ecological psychology background and exemplified as regards human movement science, in particular motor control and motor learning in sport and physical activities (Seifert et al., 2013).
The third part further focuses on practical applications of complex and dynamical system for daily life. One of the main applications is the use of a “constraint-led approach” (Davids et al., 2008). In the field of sport and physical activities, it means that experimenters, instructors and coaches manipulate several types of constraint (task, individual and environment) to make emerge the to-be-learned behaviour. From the learner point of view, it means that individuals are invited to explore and to exploit environmental properties rather than to imitate a model or to apply given procedures. More broadly, the constraint-led approach does not prescribe a behaviour but encouraged or prohibited actionthrough "field of promoted action" (field where the experimenter introduces objects, places and activities designed to increase the individual’s affordances).

KEY WORDS: emergence, self-organisation, constraint, variability.
Davids K., Button C., & Bennett S. (2008). Dynamics of skill acquisition: a constraints-led approach, Human Kinetics, Champaign, Illinois.
Seifert L., Button C. Davids K. (2013) Key properties of expert movement systems in sport: An Ecological Dynamics perspective. Sport Medicine, 43, 1, 167-178

Complex Systems and Models

Franck Varenne (Université de Rouen ­ GEMASS - UMR 8598 / CNRS / Paris Sorbonne)
Mardi 9 Juillet 2013 14h-15h30

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Summary: The current multiplication of complex systems studies bears strong relationships with the parallel spreading of formal models and computer simulations. It is all the more true in disciplines which, before the computer era and unlike physics, did not so extensively use formal models, such as biology, environmental sciences, behavioral sciences and social sciences. Nevertheless, not all disciplines that use models and simulations are studying complex systems. So it appears necessary to clarify and classify the specific roles of models and simulations in the study of complex systems. This will be the main objective of this talk. It is not possible to do this without at the same trying to characterize complex systems themselves or, what will be more modest and operative, our diverse complexity oriented visions on systems. Developing further the recent analysis of Deffuant et al (to appear) which suggests seeing three main visions on complexity, I propose introducing the concept of character of complexity for a model. Echoing theses visions on complexity, we may attribute at least four different characters of complexity to a model: its character of notation (the convenience of its formalism), its character of heterogeneity integration (related to its ability to integrate different aspects and/or scales of the target system), its character of condensation (related to the Kolmogorov complexity measure), its character of execution (related to the Bennett complexity measure). Thanks to these conceptual distinctions, some classical questions will be addressed and clarified if not incontestably answered, such as: To what extent can we say that a system under study is complex and not complicated? What is the difference between the model of a complicated system and the model of a complex one? What happens when models themselves become complex? Why the manipulation of complex models of complex systems still can be fruitful? To what extent can we say that a simple model of a complex system can lead to complex simulations? What kind of knowledge can we expect from complex simulations of complex systems? In these last two cases, another question arises: how are we sure thatthe two characters of complexity (the one of the target systems, the other of the model) are the same? Which means: how can we be sure that the model captured the type of complexity supposed to really occur in the target system?

Model construction : Individual Based Models, IBM Simulations (NetLogo)

Analysis of individual BasedModels

Frédéric Amblard, Arnaud Banos, Nicolas Marilleau (IRIT, Géographie-Cités, Unité de Recherche GEODES)
Mercredi 10 Juillet 2013 9h-12h30

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Summary: The process of building agent-based models goes through different important steps: conceptual modeling, implementation, verification, calibration, validation of the model. This course will first present these different steps, illustrated by examples from research projects, highlighting the main concepts and pitfalls at each step. Hereafter, the participants will practice rapidly the different phases on a modeling project using the Netlogo simulation platform.

Model construction: Dynamical Systems

Jean-Marc Ginoux (Université de Toulon - PROTEE)
Jeudi 11 Juillet 2013 9h­-10h30

Summary: Starting from the seminal works of Vito Volterra (1925­1931) concerning the struggle for existence (Variations and fluctuations of the number of individuals in animal species living together) we will present the construction of the most famous and simplest model: the predator­prey model. The three main assumptions made by Volterra to build this model will be also analyzed an discussed in order to verify if it is realistic or not ? Then, we will observe on the one hand that the complexity of the model increases as soon as we try to make it more realistic and on the other hand, that it will become more and more difficult to solve it analytically and, even numerically. We will conclude by highlighting the fact the the best models are generally the simplest which are considered as metaphor of the reality.

Model Analysis: Analysis of Dynamical Systems

Jeudi 11 Juillet 2013 11h­-12h30

Summary: After having recalled the main fundamental concepts of Dynamical Systems Theory from their historical background and noticed that many phenomena observed in nature evolve with multiple time scales, we will focus on slow­fast dynamical systems which are used to modelize such phenomena. Then, a new method called Flow Curvature Method which is based on the use of curvatures of trajectory curves, integral of any n­dimensional dynamical system will be briefly presented and it will shown that the main features such as fixed points stability, center manifolds, local bifurcations, canards solutions, ... can be find again according to this method. More particularly it will be shown that this method directly provides an approximation of the slow invariant manifold of any n­dimensional dynamical system. Many applications and examples in 2D, 3D, 4D, 5D and 6D such as Van der Pol, Chua, Lorenz models will be given to illustrate this method.

Networks analysis

Marc Barthélémy (IPHT - CEA, ISC-PIF)
Vendredi 12 Juillet 2013 9h­-10h30

Summary: This talk will present elements which are developed in 1. Complex systems are very often organized under the form of networks where nodes and edges are embedded in space. Transportation and mobility networks, Internet, mobile phone networks, power grids, social and contact networks, neural networks, are all examples where space is relevant and where topology alone does not contain all the information. Characterizing and understanding the structure and the evolution of spatial networks is thus crucial for many different fields ranging from urbanism to epidemiology.
An important consequence of space on networks is that there is a cost associated to the length of edges which in turn has dramatic effects on the topological structure of these networks. We will expose thoroughly the current state of our understanding of how the spatial constraints affect the structure and properties of these networks. We will review the most recent empirical observations and the most important models of spatial networks.
1 Marc Barthélemy, Spatial networks, Physics Reports, Volume 499, Issues 1–3, February 2011, Pages 1-101, ISSN 0370-1573, 10.1016/j.physrep.2010.11.002.

Introduction to artificial ants

Nicolas Monmarché, (Département d'informatique - Univ. Tours)
Lundi 15 juillet 2013 9h­-12h30

Summary: The goal of this lecture is to demonstrate that animal societies, particularly ants, are stimulating and promising paradigms for computational studies. The talk will focus on 3 aspects: first we will draw a quick overview of ants' collective behaviors through several examples of their collective intelligence. Then, we will focus on applications of artificial ants for problem solving in computer science like clustering data or optimization. Then the last part of the talk will be dedicated to the aesthetic question of using such generative systems to produce artistic materials.

Maritime networks and port systems

César Ducruet, (Géographie-Cités)
Mardi 16 juillet 2013 9h-­12h30

Summary: Maritime transport ensures about 90% of global trade volumes but has been so far poorly analyzed in the literature on complexsystems. After a brief introduction about the evolution of shipping and ports in the contemporary era, we recall the traditional themes explored by port geographers through the concept of "port system" (or system of ports). The analysis of concentration / de­concentration trends is based on real­world cases such as China, Europe, and the world. Then we move from the ideal­typical sequence model to more recent approaches from a graph perspective. The topology of maritime flows at different periods since the late 19th century is highlighted and compared with more generic models of networks. Then, the multigraph (or multiplex) approach to such flows allows validating a number of "rules of thumb" by which largest nodes are often more diversified in terms of their overseas connections, number of commodities handled, and are also more central in the network, notwithstanding certain regional specificities in the specialization of largest flows between ports. This is complemented by a look at how the two main interoceanic canals of Suez and Panama polarize container flows thereby making the network highly vulnerable. The lecture thus provides students a concrete case of a global transport system and a number of methods and models to analyze its structure, evolution, and local­global dynamics.

Where Next? Group Coordination and Decision Making in Animals

Cédric Sueur, (IPHC)
Mercredi 17 Juillet 2013 9h­-12h30

Summary: Animal groups need to remain coordinated in their activities and collectively decide when and where to travel if they are to accrue the benefits and minimize the costs of sociality. The achievement of coordinated activity and group decision making therefore has important implications for individual survival and reproduction. The aim of this talk is to bring together a collection of empirical, theoretical, and commentary articles by scientists studying this rapidly expanding topic. I will begin by focusing on decisions that involve a collective transition between a resting and a moving state, a transition we term making the move. I will examine whether specific predeparture behaviours seen during transitions represent intentional processes or more simple response facilitation. Next I will classify decisions according to the contribution of individual group members, and describe how, and why, certain individuals can have a disproportionate influence over group­mates’ behaviour. I will then review how animal groups make decisions on the move. In particular, I will focus on how variability in group size and spatial organization helps or hinders information transmission and coordination. I will end with a discussion of new tools and methodology that will allow future investigators to address some outstanding questions in animal coordination and decision­making research. I will conclude that a better integration of concepts and terminology, along with a focus on how individuals integrate environmental and social information, will be critical to developing a satisfactory understanding of collective patterns of behaviour in animal societies.


NetLogo: Arnaud Banos, (Géographie-Cités)
Tutorial: Introduction to NetLogo
Summary: Netlogo is nowadays one of the most used agent-based simulation platform. One of its principal advantage compared to other ones is the underlying programing language and modeling interface that make it easily accessible to non programmers. After presenting the main features of the platform, this course will detail how to use this tool for the modeling and implementation of a spatial agent-based simulation based on several examples available through the Netlogo platform models library.

> NetLogo : Tutorial Movie Maker

GraphStream: Stefan Balev / Antoine Dutot / Yoann Pigné

Detailed Program

Tuesday 9th > Friday 12th July (click on the picture to enlarge)

Monday 15th > Friday 18th July (click on the picture to enlarge)

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Page last modified on Wednesday 10 July, 2013 12:22:02 by Marlene-Etienne.