Post on 06-May-2015
description
On the Behavioral Interpretation of
System-Environment Fit and Auto-Resilience
Vincenzo De Florio MOSAIC research group
University of Antwerp & iMinds
RATIONALE: THREE MAIN Q’S
• How can we reason about a
system’s resilience?
• How can we tell for any two
systems which one is more
resilient?
• How can we design systems that
are optimally resilient? 2
APPROACH
• Let us first consider a system’s
intrinsic qualities
• Quality = GST class
• Wiener’s classification: behavioral
classes
– Passive, Random, Purposeful;
Teleologic (reactive), Predictive
(proactive) behaviors 3
APPROACH
• Let us introduce a partial order among
the behavioral classes:
• Passive < Random <
Purposeful < Reactive <
Proactive1 < … < ProactiveN
• 1,…,N: degree of openness
• Classes: 1≤i≤N 4
INTRINSIC QUALITY
• IQ = the behavioral classes of the
system organs responsible for resilience
– Monitoring, Analysis, Planning,
Enacting, KW processing
• Behaviors of organs (M,A,P,E,K) =
A system’s cybernetic class
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INTRINSIC QUALITY
• We characterize any resilient system
by its cybernetic class
1. Redundant Data Structures:
2. Adaptive N-version Programming:
• In this case IQ(1.) < IQ(2.)
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INTRINSIC QUALITY
• Is IQ enough to characterize resilience?
• If I have two systems, a and b, such that
IQ(a) < IQ(b), does this imply that b is
more resilient than a ?
• Not exactly
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EXTRINSIC QUALITY
• Systemic features of Man are more
sophisticated than Dog’s; but what if
threat comes with, e.g., ultrasonic noise?
• Canary+Miner ≈ Miner < Miners; but
what if threat is, e.g., toxic gas in a mine?
• “What if” is the contingence
• “What if” is the environment
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ENVIRONMENT
a dynamic system we interact with
• More or less predictable!
– the result of the actions of
• a human being (a “user”)
• a software component
• A cyberphysical thing
• EMI source
• …etc… 9
ENVIRONMENT
Assumption: the evolution of an
environment can be expressed as a
set of behaviors
• Env dynamic variation of a number of
“firing context figures”
• Behavioral ecoregion = “area defined by
its behavioral conditions”
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EXAMPLE
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• A: system, B: ecoregion
• Here A can perceive possible changes
• Here A can
perceive
impossible
changes
• Here, disaster
In general, B is B(t) !
ENVIRONMENTAL DYNAMICS
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QUALITY METRICS
• We can then define two extrinsic quality
metrics
1. System supply: how “distant” are the
system and the environmental
behaviors
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QUALITY METRICS
2. System-environment fit: how the system
and the environmental behavior “match”
– A measure of the adequacy of the
resilience infrastructure
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-1
-∞
0 2 3 0
1 0.33333 0.25 1
1 2
3
4
5
1 2
3
4
5
1 2
3
4
5
1 2
3
4
5
1 2
3
4
5
: environmental behavior
: system behavior
: environment
: system (t)
(t)
EXAMPLE
AUTO-RESILIENT BEHAVIORS!
• behaviors tracking not merely context
figures but also supply & SEF!
• Behaviors speculating and extrapolating
on future resilience requirements!
• Possibly including the social dimension!
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APPLICATIONS
• Redundant data structures
– Optimal amount of
redundancy
• Adaptive NVP
– Optimal amount /
selection of replicas
• LittleSister framework
– Optimization of energy,
safety, performance 17
CONCLUSIONS
• How can we reason about a system’s
resilience? IQ+EQ
• How can we tell for any two systems
which one is more resilient?
IQ+EQ (to some extent!)
• How can we design systems that are
optimally resilient? Auto-resilience
Computational antifragility 18
CONCLUSIONS
• Leibniz’s lesson (cf. Session L.2)!
Intrinsic and extrinsic qualities!
–Intrinsic: systemic & (mostly)
immutable
–Extrinsic: contingent, env-
specific, dynamic
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Thank you very much!
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ERACLIOS
BLOG
Computational
Antifragility