Portal:Complex Systems Digital Campus/Reconstructing Multi-scale Dynamics e-Department

= Reconstructing Multi-Scale Dynamics e-Departement=

Description
The data related to complex systems are most often incomplete and difficult to exploit because they are limited to a single level, i.e. refer to observations made on particular scales of space and time. Gathering data effectively first requires the definition of common concepts and pertinent variables for models at each level. Another important problem is obtaining unified and coherent representations for integrating different levels of organization as to predict the dynamics of the complete system. This goal can be achieved by defining pertinent variables at each level of organization, i.e. at different time (slow/fast) and spatial (macro/micro) scales, their relationships, and how they are coupled together in models that describe the dynamics at each level. The challenge is to make explicit integration functions from micro to macro levels (emergence functions) and from micro to macro levels (immergence functions).

Large cohorts of complex entities are more and more available, especially in medecine, in the social sphere and in the environment. The huge size of the data base makes it very difficult to reconstruct their multiscale dynamics through the multiple downward & upward influences. For such a task, the help of a formal epistemology and of computers is indispensable for complex systems scientists, generalizing the kind of open science performed by more and more scientific communities. The task of understanding a phenomenon amounts to finding a reasonably precise and concise approximation for that phenomenon and its behavior such that it can be grasped by the human brain. As it is, human intuition cannot handle the intrinsic properties of complex systems unaided. Ideally, optimal formal techniques provide us with candidate concepts and relations, which can then serve as a basis for the human experimental work. When the optimal forms found by the theory do not match the optimal concepts for the human brain, the reason for this discrepancy will itself be the subject of further investigation. Understanding complex systems thus requires defining and implementing a specific formal and applied epistemology.

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 * Andres Santos (chair), Universidad Politecnica de Madrid, http://www.die.upm.es/im/
 * Dr. Maria Eunice Quilici Gonzalez (chair), Philosophy Department - UNESP - University of Sao Paulo State.

Bibliography & resources
the roadmaps

e-sessions of CS-DC'15

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Keywords
Each keyword is linked to a list of papers where this keyword is referenced (in progress)

A

aggregation methods (1) algorithmic complexity (1) anthropology (4) applied algebraic topology (1) archaeology (1) artificial intelligence (2) automatic software improvement (1)

B

bayesian inference (1) bioinformatics (1) biology (3) bregman divergences (1)

C

cartesian genetic programming (1) cell dynamics (1) cell lineage (2) chaos theory (1) clustering (1) cohomology (1) combinatorial cheeger inequality (2) community detection (3) complex science (1) complex systems (10) complexity (5) computer science (5) computer vision (1) computing science (1) CorTexT (1) COTS (1) cross-cultural research (1) cultural evolution (1) cybernetics (1)

D

data science (2) data-driven approaches (1) data-driven modeling (1) developmental biology (3) dispatching (1) distributed computing (3) dynamical systems (2)

E

ecology (2) ecology of knowledges (1) embryogenesis (2) emergence (3) enetic programming (1) entropy (3) epithelium (1) ethnographic (1) ethnographic data (1) evolutionary algorithm (1) evolutionary algorithms (1) evolutionary complexity (2) evolutionary computation (4) evolutionary game theory (1) evolutionary process (1) exponential family (2)

G

genetic algorithms (2) genetic improvement (1) genetic programming (3) GPGPU (1) Graph theory (2)

H

hierarchical model (2) hunter-gatherers (1)

I

image processing (1) in-vivo fluorescent microscopy (1) information (1) information geometry (2) information science. (1)   information self-organization (1) information theory (2) inter/trans-disciplinary epistemology (1)

K

kullback-leibler divergence (3)

L

learning from demonstration (1) links (1)

M

machine learning (3) macroecology (1) manufacturing (1) mathematics (2) mechanics (1) metamodeling (1) microscopy (1) multi-level structures (1) multi-scale patterns (1) mutual-information (1)

N

noisy data (1) non-functional properties (1)

O

optimization (1) order/disorder (1)

P

performance (2) persistent homology (1) physics (3) possibility (1) probability (1) probability theory (1)

Q

qualitative modeling (1)

R

relative entropy (1) robotics (1)

S

science and technology studies (1) search-based software engineering (1) simplicial complex (2) social network analysis (1) social networks (2) Software mutation (1) survey (1) synergy (2) systemics (1)

T

theories (1) topological data analysis (1) traditions (1) transdisciplinarity (1) transdisciplinary science (1)

U

undirected graph (2) unsupervised learning (1)

W

workflow (1)

Z

zebrafish (1)