antifragility = elasticity + resilience + machine learning. models and algorithms for open system...

Download Antifragility = Elasticity + Resilience + Machine Learning. Models and Algorithms for Open System Fidelity

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Presentation for the ANTIFRAGILE 2014 workshop, 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.


  • Vincenzo De Florio MOSAIC group, Universiteit Antwerpen & iMinds
  • What is computational antifragility? Is it different from, e.g., dependability, resilience, elasticity, robustness, safety? WHY?
  • A system's ability to preserve its identity through an active behavior Aristotles entelechy being at work (active behavior) staying the same (preserving identity) [Sachs, Aristotles Physics: A Guided Study. 1995] What do we mean by Identity?
  • 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!
  • 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.
  • 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!
  • 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.
  • 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.
  • 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 correspondence: reference point No drift 2. Strong correspondence: hard RT Known and fixed drifts.
  • 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.
  • [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!
  • Behavior! any change of an entity with respect to its surroundings [RWB43] Here: Any change an entity enacts in order to sustain its system identity. Different classes of behaviors.
  • 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!
  • Teleologic / extrapolatory: action is f (goal) / f (predicted goal) Resilience! Bouldings 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).
  • 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.
  • Elasticity + Resilience + Machine Learning! Use elasticity if identity is not jeopardized Requires: monitoring the drift!
  • 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.
  • Computational antifragility does make sense It is an urgent need! (cf. keynote speech & todays 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??
  • Thanks for your attention! Autoresilience Quality indicators Perception & apperception System-Environ- ment Fit