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

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1 2

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1 2

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: 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