Slide del workshop Complexity Management: una nuova prospettiva

1. Complexity management: una nuova
prospettiva
Anticipare le crisi misurando la complessità e
semplificando il sistema
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2. COMPLEXITY MANAGEMENT:
A TECHNOLOGY FOR THE CHALLENGES OF
THE 21-st CENTURY
ONTONIX
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3. Disclaimer
The concepts and methods presented in this document are for illustrative purposes only, and are not intended to be exhaustive. Ontonix assumes no liability or responsibility to
any person or company for direct or indirect damages resulting from the use of any information contained herein.
Any reproduction or distribution of this document, in whole or in part, without the prior written consent of Ontonix is prohibited.
Reverse-engineering of the concepts, methods or ideas contained in this document is strictly forbidden. The methods described in the present document are protected by US
patents.
OntoSpace , OntoNet and OntoDyn are trademarks of Ontonix. All other trademarks are the property of their respective owners.
Copyright © 2011, Ontonix S.r.l. All Rights Reserved.
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4. Nassim Taleb – Cigno Nero Sulla base del comportamento nel passato di un
processo derivarne delle indicazioni – stime,
previsioni – sul suo comportamento nel futuro
MEDIOCRISTAN ESTREMISTAN
«Statistiche» funzionano Incertezza, non linearità prevalgono.
Statistiche comprendono sia quelle Le statistiche non funzionano.
basate su math, sia quelle basate Il processo non è prevedibile, al
su modelli mentali (logica) massimo ci si può organizzare per
Modello = mappa delle relazioni tra minimizzare effetti negativi e
le variabili che caratterizzano un massimizzare effetti positivi del «cigno
sistema, di come interagiscono tra nero»
di loro e di come impattano sul
«risultato» finale atteso.
Gli assunti dei modelli sono il
«fattore chiave», da essi dipende la
bontà del modello
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5. Nassim Taleb – Cigno Nero Sulla base del comportamento nel passato di un
processo derivarne delle indicazioni – stime,
previsioni – sul suo comportamento nel futuro
MEDIOCRISTAN ESTREMISTAN
Il mondo è pieno di casi in cui i POSSIBILE AVERE SEGNALI
«modelli» hanno fallito: DELL’APPROSSIMARSI DELLA
CRISI? OVVERO DI QUANDO I MIEI
Subprime MODELLI NON SONO PIU’
Lemahn brothers ADEGUATI?
Immobliaire
….. SI’ , NON IN ASSOLUTO,
LIMITATAMENTE AI DATI
DISPONIBILI (se i dati non
comprendono i parametri che in realtà
sono importanti, ….)
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6. Sulla base del comportamento nel passato di un processo derivarne delle
indicazioni – stime, previsioni – sul suo comportamento nel futuro
MODEL FREE
Non bisogna continuamente verificare la
ESTREMISTAN correttezza degli assunti sottostanti ANZI mi
segnala quando smettono di essere validi
POSSIBILE AVERE SEGNALI
DELL’APPROSSIMARSI DELLA QUANTITATIVO – BENCHMARK
CRISI? OVVERO DI QUANDO I MIEI Misura confrontabile nel tempo e con modelli
MODELLI NON SONO PIU’ del processo (validazione del modello,
ADEGUATI?
benchmark anche con dati parziali e
differenti)
NO STATISTICHE
Non vulnerabile nell’estremistan (prevede i
cigni neri?)
KEY CONCEPTS:
Complessità è un attributo misurabile di ogni sistema.
Un sistema molto complesso non è necessariamente fragile. La fragilità dipende dal rapporto
tra complessità e complessità massima sostenibile.
Forti variazioni di complessità e/o l’approssimarsi della complessità al valore massimo (di un
dato sistema) sono precursori di crisi repentine
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7. CONTENTS
• About Ontonix
• Why Manage Complexity
• What is Complexity?
• Beyond the Concept of Risk
• Our Products - Our Services
• Applications
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8. About Ontonix
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9. Publications
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10. Our Global Presence
CPS
Frankfurt, D
Ontonix Sp z o.o.
Business Dimensions Warsaw, Poland
Ontonix LLC Geneva, CH
Novi, USA
BLUE Eng.
Bursa, Turkey Soyotec
VAS Beijing, PRC
Hinteregg, CH
OntoMed LLC
Ann Arbor, USA Ontonix S.r.l.
Saconsulting FlexSci
Como, Italy
Madrid, Spain Beijing, PRC
Ontonix RSA
Pretoria, RSA
Ontonix Brasil
Sao Paulo, Brazil
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11. References
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12. Why Complexity Management
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13. What is Complexity?
Complexity is a function of structure and uncertainty (entropy). It quantifies the
degree of sophistication and the “amount of chaos” within a system. It is a
fundamental property of dynamical systems, just like energy.
Structure Uncertainty
(Topology of information flow) (Noise content in information)
COMPLEXITY
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14. Some Properties of Complexity
• Rapidly rising complexity is observed prior to a crisis or collapse
• Peak complexity generally corresponds to maximum crisis
intensity
• High complexity corresponds to high risk of contagion and
stress propagation
• Highly varying complexity points to a situation of low stability
and high unpredictability.
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15. Complex or Complicated?
• A system may be complicated, but have very low complexity.
• A large number of parts doesn’t generally imply high
complexity. It does, in general, imply a complicated system.
• Complexity implies capacity to surprise, to deliver unexpected
behaviour.
• In order to measure the amount of complexity it is necessary
to take uncertainty into account, not just the number of parts.
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16. Principle of Incompatibility
High precision is incompatible with high complexity.
L. Zadeh, UCLA
Low complexity High complexity
System is: System is:
• Predictable • Unpredictable
• Easy to understand • Difficult to understand
• Can be described with precision • Cannot be described
precisely
• Easy to control
• Difficult to control
• Unable to surprise
• Can surprise
• Structure dominated
• Entropy dominated
• Robust
• Vulnerable
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17. Representing Structure
Node
Link
Node
Link
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18. Examples of Complexity Maps
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19. Identifying Structure In Data
Variable 1 Variable 2 Variable 3 Variable 4 Variable 5 Variable 6 Variable 7 Variable 8 ........
Sample 1
Sample 2
Variable 1
Sample 3
.
.
.
.
Steps:
Variable 2
Variable 3
For all combinations of variables:
• Build (x;y) scatter plot
• Transform plot to 2D image
• Analyze image and measure its
information content and “amount
of structure”
• If image contains structure, create a
link in the map – this corresponds to a
relationship between two variables
Variable 5
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20. From Data to Images and Structure
y
Image with little or no structure:
no information is exchanged
between x and y.
y x
Image with evident structure:
much information is exchanged
between x and y.
y x
Image with evident structure:
much information is exchanged
between x and y.
x
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21. Critical Complexity
Mode 1 Mode 2
Mode 3 Mode 4
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22. Complexity and Robustness
Upper complexity bound (critical complexity)
Current system complexity
Lower complexity bound
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23. What Happens at Critical Complexity
Critical complexity (upper complexity bound)
y
Entropy dominates –
system is “chaotic”
y y
x
x
y
Current complexity x
Structure dominates –
system is deterministic
0
The x-y scatter plots shown in this slide represent typical relationships
between pairs of variables in a system which functions in the proximity x
of the lower and upper bounds of complexity. Lower complexity bound
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24. Complexity vs Time
Step
i
In systems which evolve in time, complexity changes,
as well as the corresponding lower and upper bounds.
Step
i+1
The same may be said of the Complexity Map, its
structure, density, hubs, etc.
Step
i+2
In such cases data is analyzed using a moving-window
approach.
t1 t2 Time
Time t1 Time t2
Complexity = 28.5 Complexity = 44.8
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25. Characteristics of a Highly Complex Business or
System
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26. The Two Facets of Fragility
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27. A New Approach To Risk
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28. Beyond Risk Management
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29. Complexity of the World’s Economy
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30. Beyond Risk Management
Conventional Modern
Risk management Uncertainty-based Complexity-based
paradigm
Rating mechanism Probability of Default (PoD) Current State of Health
Temporal horizon Mid/long term Short term
Economy Smooth Turbulent, globalized
Measure of Success Growth Survival
Approach Fragmented Holistic
Math techniques Statistical/MCS models Model-free
The table presents a comparison between the uncertainty and complexity-based philosophies
behind risk management. The fundamental difference lies in the idea of a rating:
Conventional PoD rating: a Probability of Default within a certain period of time ( i.e. years).
Complexity-based rating: measure of the present state of health, no future projection (e.g. like
doctors). A less complex company is less exposed and can better confront an uncertain market.
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31. SOFTWARE PRODUCTS
Our Software Products
OntoSpace™
The World’s first complexity management system.
(OntoSpace™ is an interactive tool)
OntoDyn™
A system for measuring the stability of portfolios of
companies and customer retention.
(OntoDyn™ can run as a batch tool)
OntoNet™
An on-line system for on-demand complexity analysis
and self-rating of corporations.
(OntoNet™ is available exclusively for on-line execution)
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32. SERVICES OFFERING
Our Services Offering
• Three levels of complexity rating
• Complexity/vulnerability analysis of companies or products
• Crisis anticipation
• Customer retention
• Merger simulation and analysis
• All our services produce quantitative results
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33. SERVICES: RATING
Our Rating Services
On-line self-rating: an entry
level solution accessible to
even small companies.
Subscription rating: a specific
solution for publicly-traded
companies.
Holistic rating: an advanced
solution in which a corporation
is analyzed with its ecosystem.
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34. SERVICES: CRISIS ANTICIPATION
Pre-Alarms and Crisis-Anticipation
Complexity may be measured and monitored versus time. Sudden fluctuations of complexity point to instabilities,
traumas or imminent crises. The magnitude of the trauma may be quantified by the difference in complexity
before and after the event. Based on similar information it is possible to identify thresholds of complexity beyond
which one may expect a crisis and therefore take measures in order to mitigate its effects.
System with mildly increasing Highly unstable system – the System with step-wise Collapsing system. Loss of
complexity (middle orange case is relative to a hospital increases complexity. This complexity is equivalent to
curve is complexity, the other patient in an ICU. Each case corresponds to the US loss of functionality. When
curves correspond to lower complexity fluctuation housing market. The time complexity reaches a zero
and upper complexity corresponds to crisis. Time span is 5 years. The 2007 value, the system has no
bounds. span is 8 hours. sub-prime bubble is indicated longer any structure and
by red arrow. ceases all activity.
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35. SERVICES: CRISIS ANTICIPATION
Pre-Alarms and Crisis-Anticipation
Complexity evolution of four US banks as confronted
with that of the US housing market. Of the four banks
only bank C did not default – notice decrease of
complexity due to change of business model.
In many cases jumps in complexity may be
caused by deliberate actions such as: During 2008 and 2009 the complexity of the US
housing market has been increasing in a way similar to
• Mergers/Aquisitions the the period which preceed the 2007 bubble.
• Spin-offs
• New regulations
The above case illustrates the evolution of
the complexity of the Israeli banking system
over a period of four years.
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36. Rating Stratification
In highly complex turbulent environments, such as the global economy, stratifying risk ratings
into 30+ classes is deprived of physical significance. See Principle of Incompatibility.
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37. Robustness Rating
Below are examples of five systems, having very low (left) to very high robustness (right).
Typical scatter plots are illustrated for each case.
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38. SELF-RATING
Self-rating: Objective Rating Available Globally
User
• Process is fully automatic, 24/7
• FREE templates available for download
• Employees have no access to eServer
• Parameters may be unnamed –
guarantees confidentiality
• Login to www.ontonix.com
• Upload P&L data
• MS-Excel format
• Flat processing fee
• Pay via Credit Card/PayPal
• Invoice sent via e-mail
• e-mail notification when analysis is done
• Very fast turn-around time
• Comprehensive rating report
• Original data is destroyed
ONTONIX
eSERVER
Complexity-Rating Report
(see example here)
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39. SELF-RATING
Self-rating: Analysis Templates
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40. Example: On-line Rating of a Public Company
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41. Example: On-line Rating of a Public Company–
Complexity & Risk Map
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42. Example: On-line Rating of a Public Company–
Variable Ranking
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43. Example: On-line Rating of a Public Company–
Failure Analysis
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44. The OntoDyn Early-warning Concept
• The exposure of banks depends largely on the state of health of
their clients (corporate and retail), but:
– Companies can hide their real situation when applying for
credit.
– Ratings are unreliable and issued on a yearly basis.
– Balance sheets and financial statements are:
• Subjective
• Often unreliable
• Issued on a quarterly or yearly basis (too slow in a
turbulent world!)
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45. The OntoDyn Early-warning Concept
• However, the information on the interaction (i.e. transactions)
between a client and his bank is available. It is:
– Objective
– Reliable
– Available on a daily/weekly or monthly basis
• Using OntoDyn this information may be used to infer the state of
health of the client.
• Based on bank-client transaction data OntoDyn measured the
stability of the client as fast as the data is available and warn of
changes therein.
• Changes in client’s stability point to potential problems.
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46. The OntoDyn Early-warning Concept
• The stability of a client is computed as a function of the rate of
change of his complexity (in analogy to blood sugar levels, or
cholesterol or other bio-markers).
• Large and sudden changes in complexity point to an unstable
system. This is sufficient to issue a pre-alarm.
• Similar information may be used in two ways:
– Assist in credit risk analysis (less stable clients will get credit
at more stringent conditions)
– Customer retention (clients leaving bank to go with
competitor bank or client is defaulting).
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47. A Bank and Its Clients – A Dynamic System
A bank interacts with a
multitude of clients.
Clients also interact with
each other. This gives
rise to a huge dynamic
system which the bank
monitors with a fairly high
frequency (daily/weekly)
or monthly.
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48. Bank-Client Interaction and Early Warnings
Client
Complexity of Client
Transactions data
Bank-client Transactions
Complexity & Risk Map of Client
Concept: A bank doesn’t need to know a client’s Balance
Sheet in order to be able to infer his state of health.
Bank
Based on the objective transactional data, the client’s
complexity is measured on a daily/weekly or monthly basis.
Sudden changes in complexity are identified and signalled.
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49. SERVICES: CRISIS ANTICIPATION
Measuring the Stability of Bank Clients –
Customer Retention
Courtesy, Banca Popolare di Sondrio
Tracking the complexity of the clients of a bank provides an advanced mechanism for customer retention analysis.
Based on the customer’s transactions (see examples of parameters in the Complexity Maps) it is possible to track his
complexity and to measure the stability based on how this complexity varies over time. Sudden changes point to
potential customer retention problems. In the above example, the complexity of a client went from 14.77 to 8.81
indicating a potential problem which the bank may decide to investigate.
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50. SERVICES: CRISIS ANTICIPATION
Stability Profile of Bank Clients and Customer
Retention
Example of stability profile of a portfolio of
230 corporate customers of a retail bank.
A Stability Profile is generated every month.
Potentially unstable clients are listed.
Each client hs a stability index ranging from 0
to 1. A value close to 1 indicates a stable
client.
The orange vertical line corresponds to a first
level of attention – all clients falling below the
corresponding value of stability are potentially
at risk of being lost.
All clients with stability to the left of the red
vertical line must be investigated.
Both levels of attention depend on the type of
business and type of clients and are
established in collaboration with Ontonix.
The skyline of the profile should be checked
at least every month.
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51. SERVICES: CRISIS ANTICIPATION
Measuring the Stability of Bank Branches
Branch A Branch B
Courtesy, Banca Popolare di Sondrio
Profitability
Complexity
Courtesy, Mizrahi-Tefahot Bank Courtesy, Banca Popolare di Sondrio
Just like in the case of bank customers, it is possible to track the complexity of a bank’s branches. Performed on a
monthly basis this provides an innovative mechanism for the anticipation of problems. A stability profile like the one
indicated in the previous slide can be produced based on conventional parameters branches use to report their
performance. The system is able to show also how the complexity and profitability of a branch are strongly related –
see plot on the right.
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52. APPLICATIONS
UNA SERIE DI CASI ED ESEMPI PER FAR COMPRENDERE LE POTENZIALITA’
RAFFINERIA : … e inaspettatamente la pompa «critica» lunedì si fermò (a dispetto
dei sistemi di prevenzione messi in campo)
COMMERCIALISTA: nei bilanci trimestrali è nascosta una informazione che con le
analisi economiche finanziarie, anche le più innovative, non riesco a cogliere?
PRODUZIONE FILATI NYLON : … com’è che con 15 addetti al controllo dei
processi produttivi , mi faccio sempre sorprendere …. e non capisco perché è
successo il fermo impianto
PORTAFOGLIO CREDITI IN CONTENZIOSO: mi fidavo delle statistiche e dei
modelli predittivi che le utilizzavano (nonostante la variabilità/incertezza del contesto
fosse tanta), ma adesso come faccio a sapere se sto utilizzando i parametri corretti
per valutare (prezzi, rischi,..)?
ENAV - per dimensionare adeguatamente le risorse in funzione della densità di
traffico e anche per segnalare con congruo anticipo l’avvicinarsi di situazioni
potenzialmente rischiose.
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53. UNA SERIE DI CASI ED ESEMPI PER FAR COMPRENDERE LE POTENZIALITA’
CONFINDUSTRIA GENOVA: aggregare imprese è probabilmente l’unica via di
uscita per le PMI italiane, come faccio a sapere, prima, se il sistema che vado a
creare (PMI aggregate) è più o meno fragile di quello di partenza (PMI isolate)
PONTE PEDONALE: la creatività non è infinita, anche i geni riconosciuti tendono a
ripetersi e cmq le soluzioni vengono trovate all’interno dello spazio del «già
spèrimentato, già conosciuto». Come faccio a scoprire se esistono soluzioni
progettuali migliori (meno complesse) di quelle «tradizionali»?
UCC LOMBARDIA : 2 progetti pilota per riprodurre i risultati già ottenuti in
esperienze fatte negli USA e in altri ospedali italiani. L’interpretazione dei segnali
(cardiologici, pressori, ecc..) dei pazienti monitorizzati è complessa, spesso la
differenza la fa l’esperienza di chi legge i segnali. La complessità si è dimostrata un
buon indicatore (80-85% di affidabilità) dello stato generale del paziente. Secondo
progetto riguarda utilizzo della misura di complessità come indicatore della buona
riuscita di una terapia, in tutti quei casi dove ancora una volta i segnali da
interpretare sono molti e complessi (defibrillatori)
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