EIASM Academic Council Meeting
Roberto Serra
Modena and Reggio Emilia University
CETRA: Complexity Education and
TRAining
• A EU-Leonardo project
• To improve the knowledge of companies (and in
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particular SME’s) in complex systems concepts and
applications
Case studies
 Complexity and innovation
• Guidelines for courses and curricula
• Development and test of selected modules
EIASM AC Meeting
Key concepts selected in Cetra
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Self organization
emergent properties
Description levels
Reciprocal causality
Positive feedback
Path dependency
Frozen accidents
Networks
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Tangled hierarchies
Self-similarity
Universality
Adaptation and exaptation
Coevolution
Edge of chaos
Redundancy and degeneracy
EIASM AC Meeting
The importance of models
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Dynamical models
Cellular automata
Agent-based models
Genetic algorithms
Genetic programming
Genetic networks
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EIASM AC Meeting
Tools to solve well-defined technical
problems
EU-Esprit projects Caboto and Colombo: in-situ bioremediation of
contaminated soils. Scale-up from pilot plant to the field
(cellular automata model)
EIASM AC Meeting
Models as conceptual tools to
address largely unknown problems
• Example: innovation (requires analysis at different
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levels!)
EU-FET project Iscom (Information Society as a COMplex
system)
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Modena and Reggio Emilia University (I)
Universitè Paris La Sorbonne (F)
CNRS, Paris (F)
Imperial College (UK)
• Models allow us to bridge the gap from a micro-theory to
its consequences at a macro-level
EIASM AC Meeting
Constraints from the theory
• transformations occur both in agent and artifact space
• => Agents and artifacts are both important
• Innovation leads to modifications of the role of agents as
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well as of the meaning of artifacts
=> both must be endogeneously generated
 External fitness functions cannot be used here
• Directedness (both in artifact and in agent space)
• => agents have intentionality
EIASM AC Meeting
outline of the model
• agents use artifacts, produced by other agents, to build other
artifacts
 Using suitable recipes
 Presently, artifacts are represented as numbers and recipes as
operators
• which can be “sold” to yet other agents, or to an “external world”
 agents and artifacts coevolve in order to better exploit the opportunities
of their mutual relationships and of the “external” world
• the meaning of artifacts is defined by which agents use them, and
for what
• the role of agents is defined by what they do, and by the other
agents with which they interact
• Agents can innovate: they identify a new artifact as their goal and
try to build the corresponding recipe
EIASM AC Meeting
Example: heterogeneity
• What are the effects of the agent heterogenity?
• We can consider different styles of innovation
 Innovation rate
 Jump frequency and range
 Jump identifies an attempt to build something really different
from the existent
• Homogeneous vs heterogeneous systems
EIASM AC Meeting
• In general, frequent innovators perform better than the
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others
But a world populated only by large jumpers is very
fragile
On the other hand, innovation is very slow in a world
populated by small jumpers only
The best results are achieved in the case where both
types coexist
A result which was not obvious a priori – it shows that
the theory can account for this phenomenon!
EIASM AC Meeting
Network of artifact types
t=350
initial
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t=4000