Who are we?

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 2006Justin Lyon, Simudyne and Dr. Dante Suarez, Simudyne and Trinity University Slide 1

2. Processes Who are we? Strategy and Marketing SAP GE DH Microsoft FRS Hawkeye McKinsey & Co. AXI Ernst & Young GB FW ERA GRV MM VBM QinetiQ PA GSD D&B SDS HVR FJ MIT GS DM SYM LBS EW DT ORS DM DS LVT RS S8 MM MG DK DEL RFG SDG AL SSSK CE BT PSAS OD MA LW SG SIE PSUK SOCK FS I8 IS SFI OP Simulation Science Slide 2

3. Simulation Science Market Reaches $20 Billion by 2010 • 15% growth projected by top industry analysts • Tipping point for Simulation Science occurs 2006 – 2007 Slide 3

4. Simulation Science – Fifty Years in the Making and Growing Fast 1950 - 1998 1998 - 2005 SD SD DES ABM Convergence ‘Tipping Point’ DES • 2006 • Simulation Science ‘Tips’ • Explosive Growth • Hybrid Modeling • Paradigm Shift • Billions €, £, $ ABM Slide 4

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 5

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 6

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 7

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 8

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 9

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 10

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 11

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 12

13. The Basics of the Proposed Model Combines Agent Based Modelling with System Dynamics • Endogenous utility functions. • The agent is not clearly defined. • It possesses coordinated subcomponents. • It is fuzzy. Slide 13

14. What do we mean by Fuzzy Agents? The USA and Europe share historic alliance, but may struggle for world leadership NAFTA Joins Canada, USA and Mexico The European Union creates a more cohesive agglomerate Hispanics in USA Latina Americans share culture among themselves Slide 14

15. Patterns of Behavior are the Results of Accumulation and Depletion over Time Aims Resulting behavior over time Intertemporal aims Resulting force t1 t2 t3 t4 t5 A. The static decomposition of desire B. The intertemporal decomposition of utility Slide 15

16. Agents Accumulate into a ‘Level’: Bounded and Binding. It is a ‘Stock’. A ‘Bathtub’. There are people in our System Dynamics simulations!! Slide 16

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 all my “I’s”. • I maximize the utility of my family, of my group, of my country, of my species. • The lines of who are my own, who is not, and who are my enemies are not drawn, but are in constant change. • We must recognize the self interest of groups. • Relative objects and subjects. Slide 17

18. Back in to Utility • Individuals are only one of the many levels where utility maximization is happening. • In lower levels, individuals are a composition of inter-temporal utility maximizations. • To form higher levels, sets of individuals may decide to join and form groups that maximize their joint utility. • These sets of individuals, or of other subsets, have the incentive to become cohesive and coordinated. Slide 18

19. Our utility functions are constrained by the ‘levels’ to which we belong. Us Realm of Action for the ‘Utility of Self’ function Slide 19

20. Individual’s Utility Behavior Collective Force By Only Focusing on the Us People Ignorant of Simulation Science are Driving Us to Extinction Uf Us Self Uc Family Country Ui Instinct Upper levels’ realms of action Slide 20

21. The World is Divided into Nested Levels of Agents • The agent in a social system simulation: • Represents strategic or non-strategic decision making; • Is constrained by the upper level to which it belongs. • Created by the evolution of adaptability and responsiveness; • Confines the subcomponents that belong to it. • The upper levels may be created by a conscious decision OR by a blind, algorithmic evolutionary force. • Upper levels can represent coordination, identification with others, institutions, implicit laws, religion, a credit bureau. • The lower levels seemingly disappear as the subcomponents become more coordinated. Slide 21

22. Determinants of Behavior are Key Questions for the Future of Simulation Science • How can we simulate “fuzzy” agents? • How can we reproduce the appearance of agency in a simulation? • Why & how does evolution grant agency? • How do we define agency? • How do we define consciousness? • Selfishness vs. Altruism. Slide 22

23. Optimality in a Hierarchically Decomposed World • To describe reality . . . • Create a benchmark position where all behavior is optimal, so long as we identify the true active agent. • Suboptimal behavior is thus a product of too much: • ‘zoom in’ or • ‘zoom out’. • Can we find an upper level in which the subparts are better off without destroying the upper level? • Institutions represent a better exploitation of the environment. • Markets to exchange behavior. Slide 23

24. By Changing the Structure of a Business We Can Initiate a ‘Positive’ Phase Transition. Cellular Automata Classes: I  II  “IV”  III Dynamical Systems: Order  “Complexity”  Chaos Matter: Fluid  “Phase Transition”  Gas Life: Too Static  “Life”  Too Noisy Businesses and Societies: Too Rigid  “Optimal” Anarchy Slide 24

25. Simulation Science Growth - One Year Slide 25

26. System Dynamics Growth: Two Years Slide 26

27. System Dynamics Growth - Five Years Slide 27

28. System Dynamics Collapse - Five Years Slide 28

29. Process for Educating the Global 1000 about System Dynamics and Simulation • 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 Adaptation of Dr. Kim Warren and Lars Finskud’s work on the Customer Conviction Chain. Slide 29

30. Educate XXXX for 15 minutes and secure next meeting Telephone meeting with XXXX at XXXXX SIMUDYNE conducts research on the use of simulation for FMCG! 2 Educate Confidentiality agreement signed S + P + I 3 Convince Meetings with Brand, Consumer Marketing and Finance As Always – The Community Must Focus on the Flows that Drive Accumulation of Apostles Contact Brand Directors at XXXX. 1 Meet face to face with XXXX and convince them on System Dynamics Declare Deliver great work 5 Develop proposal& close deal £500,000 4 Next ObjectiveGet meeting with appropriate top executives – CFO, CTO, CIO, CEO Connect Convert Slide 30

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 31

32. Thank You! Slide 32