v2.6.4 (3785)

Enseignement ATHENS - TP09 : Social Emergence in Complex Systems (Télécom Paris - Palaiseau)

Domaine > Informatique.

Descriptif

Prerequisites:

All lectures and all materials are in English, so we expect students to be fluent in English. Lab work sessions are based on software written in Python. Mastery of the Python language is not required, but students who attend this course will be fluent in procedural object-oriented programming (Java, C++, Python or equivalent). They will get some knowledge of Python by themselves before the Athens week.  

Objectives:

Insect colonies, evolving species, economic communities, social networks are complex systems. Complex systems are collective entities, composed of many similar agents, that show emerging behaviour. Though the interactions between agents are too complex to be described, their collective behaviour often obeys much simpler rules. The objective of this course is to describe some of the laws that control emergent behaviour and allow to predict it. The course will address conceptual issues, at the frontier between biology and engineering. Each afternoon consists in a lab work session in which students will get an intuitive and concrete approach to phenomena such as genetic algorithms, ant-based problem solving, collective decision, cultural emergence or sex ratio in social insects.

Students who have a scientific curiosity for emerging phenomena in nature (evolution of species, self-organizing collective behaviour) and are interested in importing ideas from nature to engineering are welcome to this course. 

 

Programme to be followed:

The main topics studied in this module are:

- Biological evolution; Genetic algorithms, in which a virtual population evolves and collectively adapts to a particular problem or to a new environment.

- Swarm intelligence, as a model of natural phenomena and as a class of collective algorithms. They are used to address problems in which adaptability and robustness are essential.

- Emerging phenomena like morphogenesis, cooperation, segregation through symmetry breaking, and emergence in social networks. We show how these different models can be applied to concrete problems, such as message routing in communication networks, optimal resource allocation or the emergence of communication.

The notion of emergence is formally defined, as well as concepts like punctuated equilibria, scale invariance, implicit parallelism and autocatalytic phenomena.

Course exam:

 The pedagogy consists in alternating lectures and practical work on machines. Students are asked to use the software platform that is provided to them and to perform slight modifications. They will study emergent phenomena by themselves and develop their own personal (micro-)project. 

Students will be evaluated based on the following tasks:

- Answers during Lab work sessions

- Small open question quiz

- A 5 min. presentation of their personal project

- A short written description of their personal project (+ source files)

30 heures en présentiel

effectifs minimal / maximal:

10/12

Diplôme(s) concerné(s)

Pour les étudiants du diplôme Diplôme d'ingénieur

All lectures and all materials are in English, so we expect students to be fluent in English. Lab work sessions are based on software written in Python. Mastery of the Python language is not required, but students who attend this course will be fluent in procedural object-oriented programming (Java, C++, Python or equivalent). They will get some knowledge of Python by themselves before the Athens week.  

Format des notes

Numérique sur 20

Littérale/grade réduit

Pour les étudiants du diplôme Diplôme d'ingénieur

L'UE est acquise si Note finale >= 10
  • Crédits ECTS acquis : 3 ECTS
  • Crédit d'UE électives acquis : 3

La note obtenue rentre dans le calcul de votre GPA.

Pour les étudiants du diplôme Echange international non diplomant

L'UE est acquise si Note finale >= 10
  • Crédits ECTS acquis : 3 ECTS

La note obtenue rentre dans le calcul de votre GPA.

Veuillez patienter