Slide 1

The future of systems modelling and the approaching "tipping point" for the whole field

7th Annual Gathering of the UK Chapter of the System Dynamics Society Harrogate, 9th & 10th February 2006

Justin Lyon, Simudyne and Dr. Dante Suarez, Simudyne and Trinity University

Slide 2

GE

BT

SAP

Microsoft

QinetiQ

Hawkeye

HVR

VBM

PSAS

PSUK

GSD

FJ

RS

CE

ERA

GB

DEL

FW

FRS

AXI

SSSK

Simulation Science

ORS

Strategy and Marketing

DH

SYM

GS

DTDM

DM

EW

SDG

McKinsey & Co.

S8

LW

FS

AL

Processes

SIE

SDS

SOCK

LVT

LBS

MIT

GRV

MM

D&B

RFG

MG

MASG

DK

OD

DS

MM

SFI

I8

OP

IS

Ernst & Young

PA

Who are we?

Slide 3

Simulation Science Market Reaches $20 Billion by 2010

15% growth projected by top industry analysts

Tipping point for Simulation Science occurs 2006 – 2007

Market Size

$-

$2,000,000,000.00

$4,000,000,000.00

$6,000,000,000.00

$8,000,000,000.00

$10,000,000,000.00

$12,000,000,000.00

$14,000,000,000.00

$16,000,000,000.00

$18,000,000,000.00

$20,000,000,000.00

2006 2007 2008 2009 2010

Software

Services

Slide 4

1998 - 2005

Simulation Science – Fifty Years in the Making and Growing Fast

1950 - 1998

SD

ABM

DES

Convergence‘Tipping

Point’

ABM

DES

2006• Simulation Science ‘Tips’• Explosive Growth• Hybrid Modeling• Paradigm Shift• Billions €, £, $

2006• Simulation Science ‘Tips’• Explosive Growth• Hybrid Modeling• Paradigm Shift• Billions €, £, $

SD

Slide 5

Market Trends Drive Adoption of Simulation Science by Global 1000

Simulation diffuses from natural sciences into social sciences

Less tolerance from Wall Street and the City for mistakes

Data-driven decision making with measurable ROI

Technology innovations

No strategy, only strategic execution

Press – Fortune, Forbes, Marketing, Strategy + Business

Slide 6

Barriers to the Adoption of Simulation Science by Global 1000

Economics and the myth of rational humans

Conflicting jargon of simulation experts is confusing

Scientific ego and the arrogance of simulation experts

Ignorance is bliss

Making something easy unnecessarily hard

Slide 7

Linear and Nonlinear Science areComplementary and Used Together

Most social science is based on linear models.

Linearity is based on independence of agents.

Under an assumption of linearity:The whole equals the sum of its parts.

Linearity works well with some systems

Nonlinear models quickly become intractable without computers.

Simulation Science: Enrico Fermi and the computer experiment.

Slide 8

Nonlinear Science = Simulation Science

Nonlinear Science: Zoology is the science that mostly studies non-elephant species.

Agents are interrelated.

Solutions to the system are complex:

the algorithm is the shortest solution.

Emergence:

The whole does not equal the sum of its parts.

The whole can be greater (or lesser) than the sum of its parts.

Slide 9

Simulation Science is a Great Tool for Harnessing Complexity to Achieve Results

Simulation Science is the study of how elaborate patterns and behaviors over time can: Emerge from very simple rules (policies, algorithms).Emerge from very simple structures (architectures).

Dynamics stem from the interactions of the agents over time, not necessarily the agents themselves.The neurons in the brain.The anthill as the organism.The humans in the corporation.

System Dynamics and Agent Based Modelling are great tools for harnessing complexity to achieve desired outcomes when managing and manipulating complex adaptive systems

Slide 10

How can we improve our marketing to ‘tip’ the ideas of Simulation Science?

Simulation Science is facing a sales and marketing challenge

It goes to the very heart of what many people hold dear

It challenges individuals most cherished beliefs

It makes it really hard to sell

Slide 11

Economics and its Realm of Action is Based on a Simplistic (and Outdated) View of Reality

The Utility function represents the individual, and it is set to reflect her preferences.

A paradigm based on the individual: an exogenous entity.

Key assumptions: markets exist for all possible goods, markets are perfectly competitive, and transaction costs are negligible.

Pareto Optimality: The optimum you get to where there is no way of making anyone happier without hurting someone else.

Economics is in dire need of a fundamental overhaul to bring it into alignment with 21st Century real world science!

Slide 12

Individual vs. Group Selection is the Root of the Anger and Fear of Simulation Science

History matters and individuals are not independent

The ‘Reductionism Nightmare’

Genes (Agents) do not make sense by themselves.Your children need a decent ‘pool of genes’ for them to preserve the

species.

Individuals (Agents) cannot survive by themselves.

Optimal species’ scales of environment exploitation.Many small and malleable individuals in species vs. large organized

wholes.

Slide 13

The Basics of the Proposed Model Combines Agent Based Modelling with System Dynamics

Endogenous utility functions.Endogenous utility functions.

The agent is not clearly defined. The agent is not clearly defined. It possesses coordinated subcomponents.It possesses coordinated subcomponents.It is fuzzy.It is fuzzy.

Slide 14

What do we mean by Fuzzy Agents?

Hispanics in USA

Latina Americans share culture among themselves

NAFTA Joins Canada, USA and Mexico

The USA and Europe share historic alliance, but may struggle for world leadership

The European Union creates a more cohesive agglomerate

Slide 15

Resulting behavior over timeResulting behavior over timeAimsAims

Resulting Resulting forceforce

Intertemporal Intertemporal aimsaims

t1 t4t2 t5t3

A. The static decomposition of desireA. The static decomposition of desire B. The intertemporal decomposition of utilityB. The intertemporal decomposition of utility

Patterns of Behavior are the Results of Accumulation and Depletion over Time

Slide 16

Agents Accumulate into a ‘Level’: Bounded Agents Accumulate into a ‘Level’: Bounded and Binding. It is a ‘Stock’. A ‘Bathtub’. and Binding. It is a ‘Stock’. A ‘Bathtub’.

There are people in our System Dynamics simulations!!

Slide 17

Answering “Whose Utility Am I Maximizing” Requires Moving Beyond the Individual

I maximize the utility of the one I am today.I maximize the utility of the one I am today.

I maximize the utility of all my “I’s”.I maximize the utility of all my “I’s”.

I maximize the utility of my family, of my group, of my country, I maximize the utility of my family, of my group, of my country, of my species.of my species.

The lines of who are my own, who is not, and who are my The lines of who are my own, who is not, and who are my enemies are not drawn, but are in constant change.enemies are not drawn, but are in constant change.

We must recognize the self interest of groups.We must recognize the self interest of groups.

Relative objects and subjects.Relative objects and subjects.

Slide 18

Back in to Utility

Individuals are only one of the many levels where utility Individuals are only one of the many levels where utility maximization is happening.maximization is happening.

In lower levels, individuals are a composition of inter-temporal utility In lower levels, individuals are a composition of inter-temporal utility maximizations.maximizations.

To form higher levels, sets of individuals may decide to join and To form higher levels, sets of individuals may decide to join and form groups that maximize their joint utility.form groups that maximize their joint utility.

These sets of individuals, or of other subsets, have the incentive to These sets of individuals, or of other subsets, have the incentive to become cohesive and coordinated.become cohesive and coordinated.

Slide 19

UUss

Realm of Action for the ‘Utility of Self’ function

Our utility functions are constrained by the ‘levels’ to which we belong.

Slide 20

Upper levels’ Upper levels’ realms of actionrealms of action

Individual’s Individual’s UtilityUtility

CollectiveCollective ForceForce

BehaviorBehavior

UUss

CountryFamily

Self

Instinct

UUff

UUii

UUcc

By Only Focusing on the Us People Ignorant of Simulation Science are Driving Us to Extinction

Slide 21

The World is Divided into Nested Levels of Agents

The agent in a social system simulation:The agent in a social system simulation:Represents strategic or non-strategic decision making;Represents strategic or non-strategic decision making;Is constrained by the upper level to which it belongs.Is constrained by the upper level to which it belongs.Created by the evolution of adaptability and responsiveness; Created by the evolution of adaptability and responsiveness; Confines the subcomponents that belong to it.Confines the subcomponents that belong to it.

The upper levels may be created by a conscious decision The upper levels may be created by a conscious decision OROR by a blind, by a blind, algorithmic evolutionary force.algorithmic evolutionary force.

Upper levels can represent coordination, identification with others, institutions, Upper levels can represent coordination, identification with others, institutions, implicit laws, religion, a credit bureau.implicit laws, religion, a credit bureau.

The lower levels seemingly disappear as the subcomponents become more The lower levels seemingly disappear as the subcomponents become more coordinated.coordinated.

Slide 22

Determinants of Behavior are Key Questions for the Future of Simulation Science

How can we simulate “fuzzy” agents?How can we simulate “fuzzy” agents?

How can we reproduce the appearance of agency in a simulation?How can we reproduce the appearance of agency in a simulation?

Why & how does evolution grant agency?Why & how does evolution grant agency?

How do we define agency? How do we define agency?

How do we define consciousness? How do we define consciousness?

Selfishness vs. Altruism.Selfishness vs. Altruism.

Slide 23

Optimality in a Hierarchically Decomposed World

To describe reality . . . To describe reality . . . Create a benchmark position where all behavior is optimal, so Create a benchmark position where all behavior is optimal, so

long as we identify the true active agent.long as we identify the true active agent.

Suboptimal behavior is thus a product of too much:Suboptimal behavior is thus a product of too much:‘‘zoom in’ or zoom in’ or ‘‘zoom out’. zoom out’.

Can we find an upper level in which the subparts are better off Can we find an upper level in which the subparts are better off without destroying the upper level?without destroying the upper level?

Institutions represent a better exploitation of the environment.Institutions represent a better exploitation of the environment.

Markets to exchange behavior. Markets to exchange behavior.

Slide 24

By Changing the Structure of a Business We Can Initiate a ‘Positive’ Phase Transition.

Cellular Automata Classes:Cellular Automata Classes:I I II II “IV” “IV” III III

Dynamical Systems:Dynamical Systems:Order Order “Complexity” “Complexity” Chaos Chaos

Matter:Matter:Fluid Fluid “Phase Transition” “Phase Transition” Gas Gas

Life: Life: Too Static Too Static “Life” “Life” Too Noisy Too Noisy

Businesses and Societies:Businesses and Societies:Too Rigid Too Rigid “Optimal” “Optimal” AnarchyAnarchy

Slide 25

Simulation Science Growth - One Year

Ignite Scenario

£0.00

£5,000,000.00

£10,000,000.00

£15,000,000.00

£20,000,000.00

£25,000,000.00

2005 2006

Failure

Pessimistic

Likely

Optimistic

Slide 26

System Dynamics Growth: Two Years

Three Year Scenarios

£0

£25,000,000

£50,000,000

£75,000,000

£100,000,000

2005 2006 2007

Failure without H

Pessimistic without H

Pessimistic with H

Likely with H

Optimistic with H

Slide 27

System Dynamics Growth - Five Years

Five Year Scenarios

£0.00

£100,000,000.00

£200,000,000.00

£300,000,000.00

£400,000,000.00

£500,000,000.00

£600,000,000.00

£700,000,000.00

£800,000,000.00

£900,000,000.00

£1,000,000,000.00

2005 2006 2007 2008 2009 2010

Failure

Pessimistic

Likely

Optimistic

Slide 28

System Dynamics Collapse - Five Years

Failure Scenario

£0

£1,000,000

£2,000,000

£3,000,000

£4,000,000

£5,000,000

£6,000,000

£7,000,000

£8,000,000

2005 2006 2007 2008 2009 2010

Failure

Slide 29

Process for Educating the Global 1000 about System Dynamics and Simulation

Adaptation of Dr. Kim Warren and Lars Finskud’s work on the Customer Conviction Chain.

An approach lauded as “one of the top 10 marketing theories” in Marketing magazine in October 2005

“Inspiration for the next Coca-Cola or Unilever marketing campaign might come from a computer game that looks an awful lot like The Sims.” in Forbes, November 2005

Slide 30

Declare

1Contact Brand Directors at XXXX.

SIMUDYNE conducts research on the use of simulation for FMCG!

Next ObjectiveGet meeting with appropriate top executives – CFO, CTO, CIO, CEO

Meetings with Brand, Consumer Marketing and Finance

Convert

4

Develop proposal& close deal

£500,000

Meet face to face with XXXX and

convince them on System Dynamics

Convince

3

Confidentiality agreement signed

S + P + I

Educate XXXX for 15 minutes and secure next meeting

Educate

2

Telephone meeting with XXXX at XXXXX

As Always – The Community Must Focus on the Flows that Drive Accumulation of Apostles

Connect

Deliver great work

5

Slide 31

SIMUDYNE’S Vision

To be the largest and most well-known simulation service provider in the world. Our intent is to be as pervasive as Visa is in the financial services industry. We are the key partner and system provider that every corporation and government has in its arsenal to make decisions and optimize success.

We do this by providing consultative services in creating simulations hosted in an encrypted internet-based environment that permeates all levels of an organization to virtually and collaboratively assess strategy, process and resource decisions.

We will radically change, improve and simplify the way in which strategy, process and resource decisions are made. Our clients literally see the future before it happens.

Slide 32

Thank You!