Course code TPT09
Course title Emergence in Complex Systems: from Nature to Engineering
Institution TELECOM ParisTech
Course address TELECOM ParisTech - 46 rue Barrault - 75013 Paris
City Paris
Minimum year of study 4th year
Minimum level of English Good
Minimum level of French None
Key words

Complex systems, Collective Intelligence, Emergence, Genetic Algorithms, Small World, Swarm Intelligence.


Language English
Professor responsible Jean-Louis DESSALLES
Telephone + 33 (0) 1 45 81 75 29
Fax + 33 (0) 1 45 81 31 19
Participating professors Jean-Louis DESSALLES (TELECOM ParisTech, Dept Informatique et Réseaux)
Number of places Minimum: 10, Maximum: 30, Reserved for local students:


Complex systems are collective entities composed of many similar agents. Though the interactions between agents are too complex to be described, their collective behaviour often obeys much simpler rules. This is known for economy, but it is also observed in evolutionary selective processes, in human social networks and in insect societies. The objective of this course is to describe some of the laws that rule emergent behaviour and allow to predict it. The course will address conceptual issues. 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.


Les systèmes complexes sont composés de nombreux agents à peu près identiques. Bien que les interactions entre agents soient bien trop complexes pour être décrite, leur comportement collectif obéit parfois à des lois parfois simples. On le vérifie dans les processus d’évolution par sélection, dans les réseaux sociaux, chez les insectes sociaux ou dans les phénomènes économiques. L’objectif de cet enseignement est de décrire les lois qui permettent de prévoir et d’utiliser les comportements émergents.



Programme to be followed

An ant colony can find the shortest path in a complex environment; a species can solve complex adaptation problems; economic agents may spontaneously reach a locally optimal allocation of resources. Simple individual acts, in each case, produce non-trivial results at the collective level.


These observations constitute a rich source of inspiration for innovative engineering solutions, such as optimization using genetic algorithms, or message routing in telecom networks.

The emergent behaviour of complex collective systems often goes against intuition. Its dynamics can be described through non-linear models that predict sudden transitions. Emergence is best apparent during those transitions. Its study consists in accounting for the appearance of collective patterns when individual, generally simple, behaviours are given as input.


The main techniques studied in this module are:

- 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.

- Emergence of 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 antenna location 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.


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.

Course exam

The pedagogy consists in alternating lectures and practical work on machines. Students can modify the software platform that is provided to them, study emergent phenomena by themselves and develop their own personal project.


Students will be evaluated based on the following tasks:

- Answers during Lab work sessions

- Small open question quiz

- A 5 min. presentationof their personal project

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