The work of LISC founded by Guillaume Deffuant and currently led by Franck Jabot
focuses on the modeling of complex social and environmental systems
as well as strategies for action on these systems.
To understand the dynamics of forests, interactions between plant population or
animals in their environment or socials dynamics in rural areas; LISC researchers
use mathematical and computational models.
They develop models in which each individual,
plant, animal or human of a population are represented.
These computer models can be used to simulate interactions of each individual with
his peers and the environment.
Simulations reveal unexpected collective facts for which mathematical theory
is sometimes difficult to establish.
The transition from individual to collective is at the heart of recent theories of
complexity. occurs in most environmental, social or
societal dynamics. The purpose of LISC research is
to study globals behaviors belonging to individual interactions.
We develop software tools to perform numerical experiments
on these models.
That allows us to study how the globals behaviors observed depend on
models input parameters, which represents interactions between
individuals.
We also develop statistical methods to calibrate these models from data
on global systems and once calibrated these models can be used
to calculate policies actions on systems to ensure their viability or
their resilience.
The LISC participates in national and international multidisciplinary projects in which
collaborate computer scientists, mathematicians but also specialists from ecosystem
or social sciences.
The themes of the team are very diverse for example we are working on forest systems
to adapt forest management to climate change, on the lakes
to better manage eutrophication problems or on the psychological determinants
and social that will push the farmers to convert to organic farming.
This last work is done in collaboration with the LAPSCO laboratory in Clermont-Ferrand.
As part of a call for projects of region Auvergne we built a partnership
with BioFilm Control in order to develop new algorithms allowing
to detect the presence of bacterial bio-film. In some condition bacteria can
produce polymers matrix.
This polymers matrix may allow for proctection of these bacteria when they
are subject to external aggression such as antibiotics. So in this
collaboration we decided with BioFilm Control to develop new
algorithms that allow to have a much finer detection of the presence of these
bacterial bio-film.
On our side, this allows us to have access to industrial data that allow
to feed our methodological research questions.
For BioFilm Control these new algorithms will allow them to have
much finer detection of bacterial bio-film.
This will allow them to develop new
industrial applications.
The assimilation of heterogeneous data allows to model systems and find
solutions to a problem raised by an industrialist or a company.