Chairs & Co-Chairs
Overview
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.
Program
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Epistemology of integrative and predictive sciences e-session (60) - Maria Eunice Gonzales (28), Dr. Mariana Claudia Broens (67), Guiou Kobayashi (298)
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A study of ethical problems from the perspective of complexity (63) - Mariana Claudia Broens (117)
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Towards an interdisciplinary epistemological view, bridging the human and the formal/natural sciences: any place in Complex Systems Science? (137) - Maria Eunice Quilici Gonzalez (255), Guiou Kobayashi (256), Jose Artur Quilici-Gonzalez (257)
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Information science and the complexity: are we orientated to a transdisciplinary science? (249) - Rafaela Carolina da Silva (412), Rosângela Formentini Caldas (413)
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Knowledge Maps e-session (62) - Jean-Philippe Cointet (101)
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The traditions of complexity theories (232) - Fabrizio Li Vigni (387)
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Analysis of a Planetary Scale Scientific Collaboration Dataset Reveals Novel Patterns (144) - Soumya Banerjee (265)
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Multi-Level Computational Vision e-session (64) - Andres Santos (103)
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Reconstructing embryo development through maps of gene expression from microscopy images (378) - Carlos Castro-González (555)
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Multi-scale embryogenesis mechanics from “in-vivo” images (380) - David Pastor-Escuredo (557)
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Machine Learning Methods e-session (65) - Michele Sebag (104)
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Analogical proportion and machine learning – An introductory survey (396) - Henri Prade (573)
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A formal model to compute uncertain continuous data (240) - Jérôme Dantan (401)
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Synthesis of Ecology, Biology and Ethnographic Data e-session (66) - Douglas White (105)
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Workflow-driven science for the synthesis of ecology, biology and ethnographic data (37) - Ilkay Altintas (62), Jianwu Wang (63), Mai Nguyen (64), Tolga Oztan (65), Douglas White (66)
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Comparative study of Pastoral Tenure Systems (48) - Mark Moritz (92), Elizabeth Gardiner (93), Amber Johnson (94)
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Origin of Complex Society in the Early Holocene (65) - Charles Stanish (120)
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Exploring ecological and evolutionary processes using Binford’s hunter-gatherer data (86) - Amber L Johnson (160)
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Regional contrasts in cultural diversity – Environmental influences ? (89) - Alexander Harcourt (163)
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Oscillatory complexity in human history: earth?s asymmetric biogeography and ethnographic data (314) - Douglas R. White (486)
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Can we evaluate a theory of non-linear transition from foraging to farming with ethnographic data? (461) - Jacob Freeman (674)
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Bayesian Causalities, Mappings, and Phylogenies: A Social Science Gateway for Modeling Complexity in Ethnographic, Archaeo-, Eco- and Bio-logical Variables (42) - Douglas White (72), Paul Rodriguez (73), Eric Blau (74), Łukasz Lacinski (75), Rachana Ananthakrishnan (76), Stuart Martin (77), Thomas Uram (78), Tolga Oztan (79), Douglas White (80)
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Oscillatory Complexity in Human History: Earth?s asymmetric biogeography and ethnographic data (43) - Douglas White (81)
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Evolutionary computation methods e-session (67) - Pierre Collet (2)
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In complex evolutionary systems, how do the moving parts move? (335) - James Michael McDermott (510)
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Cartesian Genetic Programming (337) - Julian Francis Miller (513)
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Data-driven paradigms of EvoNN and BioGP (345) - Nirupam Chakraborti (521)
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Software is Not Fragile (356) - Bill Langdon (533)
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The performance of social computing (358) - Juan-Julian Merelo (535)
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Automatic programming via evolution (394) - Lee Spector (571)
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Evolutionary design of self-organising manufacturing systems (395) - Juergen Branke (572)
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Using commodity-off-the-shelf robots (COTSBots) for evolutionary learning from demonstration (431) - Terence Soule (621)
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Community detection: are evolutionary algorithms comparable to standard machine learning methods? (432) - Ami Hauptman (622)
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TBA (508) - Ami Hauptman (742)
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Algebraic Topology and Information Theory (123) - Pierre Baudot (121)
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Relative entropy as a functor (263) - Tobias Fritz (432)
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Information geometry and applied algebraic topology: multi-scale approaches in developmental biology (264) - Paul Villoutreix (433)
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Information topology, a mathematical theory of Complex Systems and data (265) - Pierre Baudot (434)
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Information-theoretic Cheeger inequalities (326) - Peter Gmeiner (501)
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Information-Theoretic Cheeger inequalities (472) - Peter Gmeiner (692)