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Vincenzo De Florio
MOSAIC group, Universiteit Antwerpen & iMinds
vincenzo.deflorio@uantwerpen.be
What is computational antifragility?
Is it different from, e.g.,
dependability, resilience, elasticity,
robustness, safe...
A system's ability to
 preserve its identity
 through an active behavior
Aristotle’s entelechy
 “being at work (activ...
Voice-over-IP system; call b/w two
endpoints
The identity of the system is
 the fact that the system works!
Communicati...
If the experienced quality of the
communication matches the
expected quality,
the system is resilient
If the system is n...
Thus resilience calls for a property:
fidelity
Fidelity: quality of representation &
control between a reference domain
...
Open system: systems that “interact
with other systems outside of
themselves” [Heylighen ‘98]
The interaction may involv...
One such aspect can be time 
time-open systems
aka Real-time systems!
 Systems that have a “social notion” of
time
 S...
 Quality = a measure of the drift
between the internal representation and
the measured context variable.
RTS: cybertime  physical time
The fidelity of a time-open system
depends on the quality of RTS
1. Perfect correspondenc...
3. Statistical correspondence: soft RT
Known average / stdev values
Active behaviors
4. Practical correspondence: best-
...
[RT]1 … [RT]5 : Five classes
(just an example!)
Resilience =
 perform intended function
(“being at work”)
 Staying in ...
Behavior! “any change of an entity
with respect to its surroundings”
[RWB’43]
Here: Any change an entity enacts
in order...
Passive: inert systems
 As-is, best-effort !
 No guarantees!
Purposeful: servo-mechanisms
 Guarantees through elastic...
Teleologic / extrapolatory: action is
f (goal) / f (predicted goal)
 Resilience!
 Boulding’s “Animals”: models of the
“...
Auto-predictive systems:
open to their own system-
environment fit!
 SEF-open systems!
 wisdom is developed as a result...
Elasticity + Resilience + Machine
Learning!
Use elasticity if identity is not
jeopardized
 Requires: monitoring the dri...
If identity is jeopardized:
Use resilience (both individual and
collective); and learn!
 Measure effectiveness of curren...
Computational antifragility does
make sense
It is an urgent need! (cf. keynote
speech & today’s papers!)
A lot needs to...
Thanks for your
attention!
Autoresilience
Quality indicators
Perception &
apperception
System-Environ-
ment Fit
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Antifragility = Elasticity + Resilience + Machine Learning. Models and Algorithms for Open System Fidelity

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Presentation for the ANTIFRAGILE 2014 workshop, https://sites.google.com/site/resilience2antifragile/

Abstract: We introduce a model of the fidelity of open systems—fidelity being interpreted here as the compliance between corresponding
figures of interest in two separate but communicating domains. A special case of fidelity is given by real-timeliness and synchrony,
in which the figure of interest is the physical and the system’s notion of time. Our model covers two orthogonal aspects of fidelity,
the first one focusing on a system’s steady state and the second one capturing that system’s dynamic and behavioral characteristics.
We discuss how the two aspects correspond respectively to elasticity and resilience and we highlight each aspect’s qualities and
limitations. Finally we sketch the elements of a new model coupling both of the first model’s aspects and complementing them
with machine learning. Finally, a conjecture is put forward that the new model may represent a first step towards compositional
criteria for antifragile systems.

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Transcript of "Antifragility = Elasticity + Resilience + Machine Learning. Models and Algorithms for Open System Fidelity"

  1. 1. Vincenzo De Florio MOSAIC group, Universiteit Antwerpen & iMinds vincenzo.deflorio@uantwerpen.be
  2. 2. What is computational antifragility? Is it different from, e.g., dependability, resilience, elasticity, robustness, safety? WHY?
  3. 3. A system's ability to  preserve its identity  through an active behavior Aristotle’s entelechy  “being at work (active behavior)  staying the same” (preserving identity) [Sachs, Aristotle’s Physics: A Guided Study. 1995]  What do we mean by Identity?
  4. 4. Voice-over-IP system; call b/w two endpoints The identity of the system is  the fact that the system works! Communication is possible b/w the endpoints  the experienced QUALITY of the communication matches the expectations of the two endpoints!  Throughout the call!
  5. 5. If the experienced quality of the communication matches the expected quality, the system is resilient If the system is not able to compensate for disturbances and the qualities drift away, the system is not resilient.
  6. 6. Thus resilience calls for a property: fidelity Fidelity: quality of representation & control between a reference domain and an execution domain Physical world  Cyberworld Fidelity must preserve concepts!  Delay, Echo, Jitter, Latency…  Time! Fidelity: A property of open systems. Leibniz!
  7. 7. Open system: systems that “interact with other systems outside of themselves” [Heylighen ‘98] The interaction may involve n aspects, corresponding to n context variables  Luminosity; jitter; sound; time…  n-open systems.
  8. 8. One such aspect can be time  time-open systems aka Real-time systems!  Systems that have a “social notion” of time  Systems that base their action on the accuracy of an internal representation of time.
  9. 9.  Quality = a measure of the drift between the internal representation and the measured context variable.
  10. 10. RTS: cybertime  physical time The fidelity of a time-open system depends on the quality of RTS 1. Perfect correspondence: reference point No drift 2. Strong correspondence: hard RT Known and fixed drifts.
  11. 11. 3. Statistical correspondence: soft RT Known average / stdev values Active behaviors 4. Practical correspondence: best- effort “Usually drifts are tolerated by the users” Passive behaviors 5. No guarantee: as-is.
  12. 12. [RT]1 … [RT]5 : Five classes (just an example!) Resilience =  perform intended function (“being at work”)  Staying in the same class (“staying the same”) Being at work: different ways to do so!
  13. 13. Behavior! “any change of an entity with respect to its surroundings” [RWB’43] Here: Any change an entity enacts in order to sustain its system identity. Different classes of behaviors.
  14. 14. Passive: inert systems  As-is, best-effort !  No guarantees! Purposeful: servo-mechanisms  Guarantees through elasticity / fault masking  Redundancy is predefined and statically defined as a result of Worst-case Analyses  Sitting ducks!
  15. 15. Teleologic / extrapolatory: action is f (goal) / f (predicted goal)  Resilience!  Boulding’s “Animals”: models of the “self” and of the “world” Auto-predictive: learning systems  the action of the environment and of the system leaves a footprint in the system  System “learns” by ranking strategies with obtained results (cf. EGT…).
  16. 16. Auto-predictive systems: open to their own system- environment fit!  SEF-open systems!  wisdom is developed as a result of the match between strategy and obtained results.
  17. 17. Elasticity + Resilience + Machine Learning! Use elasticity if identity is not jeopardized  Requires: monitoring the drift!
  18. 18. If identity is jeopardized: Use resilience (both individual and collective); and learn!  Measure effectiveness of current solutions  Rank current solution with respect to past ones  Derive and persist conclusions  Update resilience models accordingly.
  19. 19. Computational antifragility does make sense It is an urgent need! (cf. keynote speech & today’s papers!) A lot needs to be done yet  PANEL  How do we move from ideas to an engineering practice?  Antifragility vs autonomic behaviors?  Shall we begin??
  20. 20. Thanks for your attention! Autoresilience Quality indicators Perception & apperception System-Environ- ment Fit
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