Ontonix Complexity

© Copyright 2011, Ontonix S.r.l. All rights reserved. No part of this document may be reproduced in any form without the written consent of Ontonix S.r.l.

Complexity management: una nuova prospettiva

Anticipare le crisi misurando la complessità e semplificando il sistema


COMPLEXITY MANAGEMENT: A TECHNOLOGY FOR THE CHALLENGES OF

THE 21-st CENTURY

ONTONIX


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.


Nassim Taleb – Cigno Nero

MEDIOCRISTAN

«Statistiche» funzionano Statistiche comprendono sia quelle basate su math, sia quelle basate su modelli mentali (logica) Modello = mappa delle relazioni tra le variabili che caratterizzano un sistema, di come interagiscono tra 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

Sulla base del comportamento nel passato di un processo derivarne delle indicazioni – stime, previsioni – sul suo comportamento nel futuro

ESTREMISTAN

Incertezza, non linearità prevalgono. Le statistiche non funzionano. Il processo non è prevedibile, al massimo ci si può organizzare per minimizzare effetti negativi e massimizzare effetti positivi del «cigno nero»


Nassim Taleb – Cigno Nero

MEDIOCRISTAN

Il mondo è pieno di casi in cui i «modelli» hanno fallito:

Subprime Lemahn brothers Immobliaire …..


ESTREMISTAN

POSSIBILE AVERE SEGNALI DELL’APPROSSIMARSI DELLA CRISI? OVVERO DI QUANDO I MIEI MODELLI NON SONO PIU’ ADEGUATI?

SI’ , NON IN ASSOLUTO, LIMITATAMENTE AI DATI DISPONIBILI (se i dati non comprendono i parametri che in realtà sono importanti, ….)



ESTREMISTAN

POSSIBILE AVERE SEGNALI DELL’APPROSSIMARSI DELLA CRISI? OVVERO DI QUANDO I MIEI MODELLI NON SONO PIU’ ADEGUATI?

MODEL FREE Non bisogna continuamente verificare la correttezza degli assunti sottostanti ANZI mi segnala quando smettono di essere validi

QUANTITATIVO – BENCHMARK Misura confrontabile nel tempo e con modelli del processo (validazione del modello, 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


CONTENTS

•  About Ontonix •  Why Manage Complexity •  What is Complexity? •  Beyond the Concept of Risk •  Our Products - Our Services •  Applications


About Ontonix

•  Established in 2005 by experts in the aerospace, nuclear and civil engineering industries. Over 60 man-years experience in advanced risk management and Monte Carlo Simulation.

•  Ontonix is the first company to have developed rational means of measuring complexity and relating it to business performance and sustainability.

•  Initially established in the USA, Ontonix is now headquartered in Como, Italy and has offices in the USA, Poland, Brazil and South Africa.

•  In 2005 Ontonix launched OntoSpace™, the first SW system for practical complexity management.

•  In 2007 the first on-line rating service has been inaugurated.

•  Ontonix currently serves customers in such fields as banking, air-traffic management, medicine, defense and engineering.


Publications

Computational Stochastic Mechanics in a Meta-Computing Perspective. 1997

Theory of Eigenvalue Orbits.1998

Principles of Simulation-Based Computer-Aided Engineering. 1999

Beyond Optimization in Computer-Aided Engineering. 2002

Practical Complexity Management. 2009

Application Strategies of Robust Design and Complexity Management in Engineering. 2009. Co-author Dr. H. Sippel

A New Theory of Risk and Rating. 2010


Ontonix LLC Novi, USA

Ontonix S.r.l. Como, Italy Saconsulting

Madrid, Spain

CPS Frankfurt, D

FlexSci Beijing, PRC

Our Global Presence

VAS Hinteregg, CH

OntoMed LLC Ann Arbor, USA

Ontonix Brasil Sao Paulo, Brazil

Ontonix RSA Pretoria, RSA

Soyotec Beijing, PRC

Business Dimensions Geneva, CH

BLUE Eng. Bursa, Turkey

Ontonix Sp z o.o. Warsaw, Poland


References


Why Complexity Management

Complexity is rapidly increasing in all spheres of social life. This leads to multiple converging stresses and increases the levels of turbulence of the global economy as well as of the society. Doing business is increasingly difficult. Complexity must be managed before it reaches dangerous levels and threatens sustainability.


Structure (Topology of information flow)

Uncertainty (Noise content in information)

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.

What is Complexity?


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.


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.


Principle of Incompatibility

High precision is incompatible with high complexity.

L. Zadeh, UCLA

Low complexity

System is:

• Predictable

• Easy to understand

• Can be described with precision

• Easy to control

• Unable to surprise

• Structure dominated

• Robust

High complexity

System is:

• Unpredictable

• Difficult to understand

• Cannot be described precisely

• Difficult to control

• Can surprise

• Entropy dominated

• Vulnerable


Representing Structure

Conventional Map: Difficult to analyze when number of nodes and links is large (“spaghetti effect”).

Hubs are difficult to identify.

Ontonix Complexity Map: Easy to analyze even when the number of nodes and links is very large.

Hubs are easy to identify.

Link

Node

Node

Link


Examples of Complexity Maps


Variable 1 Variable 2 Variable 3 Variable 4 Variable 5 Variable 6 Variable 7 Variable 8 ........

Variable 3

Variable 5

Variable 2

Variable 1

Identifying Structure In Data

Steps:

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

Sample 1 Sample 2 Sample 3 . . . .


From Data to Images and Structure

Image with little or no structure: no information is exchanged between x and y.

Image with evident structure: much information is exchanged between x and y.

Image with evident structure: much information is exchanged between x and y.

x

x

x

y

y

y


Critical Complexity

•  Complexity cannot grow indefinitely and has a maximum. Close to this maximum, called critical complexity, a given system becomes fragile and vulnerable.

•  In the proximity of critical complexity systems possess numerous modes of behaviour and can switch from one mode to another sponaneously.

•  Modes represent (behavioural) attractors.

•  Critically complex systems are very difficult to manage and can easily run out of hand.

•  After critical complexity decay begins unless structural changes are made to the system.

Mode 1 Mode 2

Mode 3 Mode 4


Complexity and Robustness

Upper complexity bound (critical complexity)

Current system complexity

Lower complexity bound

Ccritical ‒ Ccurrent

Robustness is function of Ccritical ‒ Ccurrent. This measure is known as topological robustness and quantifies the system’s ability to preserve its functionality.

This is the key concept behind our innovative complexity-based rating scheme.

•  It is not convenient to function in the proximity of critical complexity ‒ behaviour becomes unpredictable.

•  The vulnerability of a system is proportional to its distance from critical complexity. •  A well-managed system is kept at a safe distance from critical complexity. •  Sudden changes in complexity point to traumas (endogenous or exogenous) and may be seen as early warnings and precursors of crises.


What Happens at Critical Complexity

Entropy dominates – system is “chaotic”

Structure dominates – system is deterministic

x

y

x

y

x

y

Critical complexity (upper complexity bound)

Lower complexity bound

Current complexity

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 of the lower and upper bounds of complexity.

x

y


Complexity vs Time

Time Time t1

Complexity = 28.5

Time t2

Complexity = 44.8

t1 t2

In systems which evolve in time, complexity changes, as well as the corresponding lower and upper bounds. The same may be said of the Complexity Map, its structure, density, hubs, etc.

In such cases data is analyzed using a moving-window approach.

Step i+2

Step i+1

Step i


Characteristics of a Highly Complex Business or System

Difficult to manage

Highly exposed Vulnerable

Unhealthy

Unsustainable

Fragile


The Two Facets of Fragility

C Business Model x U Environment = Fragility

The equation indicates two fundamental facets of the global financial crisis and shows why the global economy is fragile:

•  Complex Business Model: Global financial operators manipulate extremely complex financial products, such as derivates (and derivatives of derivatives of ..

•  Turbulent Economy: The global economic environment is increasingly uncertain and interconnected.

Under similar conditions, conventional risk rating, assessment and management techniques don’t apply. A new look at risk is mandatory. NB. Fragility = rupture without early-warning


C Business Model x U Environment RISK

A New Approach To Risk

In a turbulent economy a new systemic approach to risk must be taken.

•  Conventional Approach: Risk is exposure to uncertainty and therefore uncertainty is THE source of risk:

•  Modern Approach: The uncertainty of the (global) environment cannot be controlled. Excessive complexity is THE source of risk:

U Environment, Business RISK


OLD PARADIGM: RISK MANAGEMENT Rating = PoD

NEW PARADIGM: COMPLEXITY MANAGEMENT Rating = Current Fragility

Beyond Risk Management

“OLD” ECONOMY FUTURE ECONOMY


Complexity of the World’s Economy

Complexity Map of global economy based on GDP growth.

Complexity Map of US economy (detail of circled area on left).

Complexity evolution of US economy from 2004 to 2007.

Complexity evolution of global economy from 2004 to 2007.

Entropy evolution of global economy from 2004 to 2007.

The increase of complexity and entropy (measure of uncertainty of the economic climate) is inevitable and is a result of the laws of physics.


Beyond Risk Management

Conventional Modern

Risk management paradigm

Uncertainty-based Complexity-based

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.


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)

SOFTWARE PRODUCTS


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

SERVICES OFFERING


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.

Corporate Complexity Profile

Complexity Map

State-of-Health Diagram

SERVICES: RATING

Complexity Evolution

Complexity Rating


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.

SERVICES: CRISIS ANTICIPATION

System with mildly increasing complexity (middle orange curve is complexity, the other curves correspond to lower and upper complexity bounds.

Highly unstable system – the case is relative to a hospital patient in an ICU. Each complexity fluctuation corresponds to crisis. Time span is 8 hours.

System with step-wise increases complexity. This case corresponds to the US housing market. The time span is 5 years. The 2007 sub-prime bubble is indicated by red arrow.

Collapsing system. Loss of complexity is equivalent to loss of functionality. When complexity reaches a zero value, the system has no longer any structure and ceases all activity.


Pre-Alarms and Crisis-Anticipation


In many cases jumps in complexity may be caused by deliberate actions such as:

• Mergers/Aquisitions • Spin-offs • New regulations

The above case illustrates the evolution of the complexity of the Israeli banking system over a period of four years.

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.

During 2008 and 2009 the complexity of the US housing market has been increasing in a way similar to the the period which preceed the 2007 bubble.


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.


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.


Self-rating: Objective Rating Available Globally

• 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

Complexity-Rating Report (see example here)

User • Process is fully automatic, 24/7 • FREE templates available for download • Employees have no access to eServer • Parameters may be unnamed – guarantees confidentiality

ONTONIX eSERVER

SELF-RATING


Self-rating: Analysis Templates

Generic Corporation Retail Bank

Ontonix offers free analysis templates which may be downloaded from its website and used for self-rating. Two examples, illustrating typical parameters used in complexity rating, are shown on the left.

SELF-RATING


Example: On-line Rating of a Public Company

State of health depends on how far the system functions from its critical complexity


Example: On-line Rating of a Public Company– Complexity & Risk Map

Strong link

Weak link

Hub

Macro-economic indices


Example: On-line Rating of a Public Company– Variable Ranking

Dominant variable

Insignificant variable


Example: On-line Rating of a Public Company– Failure Analysis

Weak link

Strong link


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!)



•  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.



•  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).


A Bank and Its Clients – A Dynamic System

BANK

Client 1

Client 2

Client 3

Client 4

Client 5

Client 6

Client 7

Bank-Client Interaction

Client-Client Interaction

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.


Bank-Client Interaction and Early Warnings

Bank

Client

Bank-client Transactions

Transactions data

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.

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.

Complexity of Client


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.


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.

Clients at risk of being lost

Unstable client Stable client Risky clients


Measuring the Stability of Bank Branches

Branch A Branch B

Courtesy, Banca Popolare di Sondrio Courtesy, Mizrahi-Tefahot Bank


Courtesy, Banca Popolare di Sondrio

Complexity

Pro

fitab

ility

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.


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.


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)

Ontonix Complexity

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