Understanding Forces That Dictate System Behavior

1. System Dynamics Understanding the Forces That Dictate System Behavior Lecture 7 Spring 2014

2. Understand the forces that cause a system to behave the way it does……

3. Equilibrium • Systems often settle into a stable state • Systems can have multipleequilibria • When all seems stable the only thing you can be certain of is change 3

4. Entropy • Entropy is the amount of randomness in a system • Decreasing entropy increases stability, but at the cost of energy loss • High entropy indicates free energy in a system that can be captured, but also creates significant instability • Feedback effects tend to amplify entropy http://www.youtube.com/watch?v=ncpU0yax1Oo 4

5. Linear Change Complexity Chaos Order Nonlinearity • Chaotic systems exhibit nonlinear effects — i.e., linear changes cause qualitative changes in state 5

6. Nonlinearity…continued • Very little in life is linear 6

7. Regression Towards the Mean 'The odds of another plane hitting this house are astronomical!‘ - The World According to Garp

8. Pareto Principle (80/20) Vilfredo Pareto observed that 20% of the pea pods in his garden produced 80% of the peas. Roughly 80% of the effects come from 20% of the causes - 20% of your sales team generates 80% of results.

9. Increasing Returns A phenomenon where success (in a given market or with a given technology) aids in further success. An example of increasing returns are network effects, where the more there is participation in a given network, the overall value of the network increases exponentially.

10. Network Effects - Defined • Network effects exist when the value of a good increases as the number of people using the good increases • Metcalfe’s Law: value = n2 - n members (n) value 3 6 10 90 20 380 100 9,900

11. Network Effects Are the Foundation Of some of the best business models ever devised. These kinds of businesses are incredibly valuable, because the cost of a new customer is wildly exceeded by the value of that customer’s contribution to the network.

12. Class Exercise: Network Effects • Can you think five examples of Network Effects at UT? • (I’ve given you 1) • Assault & Battery • Can you think of five examples of Increasing Returns in Austin? • (I’ve given you 3) • Startups • Largest Law Library • Alumni Relations

13. So Let’s Step Back…What is a System? Systems are things in which there are multipleinteractions between many different components (or agents). A complex system is characterized by multiple agents whose interactions give rise to structural effects that aren’t apparent in the agents themselves. For example, an ant colony is a complex system — its structure is highly dependent on the characteristics of individual agents, but you can’t derive the structure of an ant colony by studying individual ants.

14. Stock Population Births Deaths Flows Components of a System: Stocks and Flows • A Stock is a variable measured at a specific point in time. • A Flow is the rate of change in a particular variable. • (In calculus terms, a stock is an initial quantity plus the integral of a flow, and a flow is the derivative of a stock over time) • For example: 14

15. Births Population Deaths Available Resources Components of a System: Feedback loops • Feedback loops (positive / negative / delayed) Births have a positive feedback effect, but it’s delayed (think baby boomers) Population increases take up available resources, which decreases the birth rate (a negative feedback effect) 15

16. Sales Customers Revenue Products Components of a System: Feedback loops • Sales efficiency 16

17. Emergence • A system can be more than the sum of its parts! 17

18. Class Exercise: Chaos and Order at UT • Order (Equilibrium) • Chaos (Entropy) • Agents (Students, Faculty, Regents) • Output (Emergence) 18

19. Class Exercise: Chaos and Order in Government • Order (Equilibrium) • Chaos (Entropy) • Agents (Senate, House, Voters, $) • Output (Emergence) 19

20. Examples of Models • Any investment could be modeled by its costs and profits. The question is what happens in between. • A bad model may describe some reality but still lack explanatory power or detail. • A good model reflects reality while remaining flexible and providing explanatory power. 20

21. A (too) simple model • This model may reflect reality, but isn’t that useful: 21

22. A complex but useful model Life Insurance in the UK: 22

23. Complex Adaptive Systems • Complex adaptive systems are a subset of complex systems that adapt to their environment • Examples: • Agent: a single ant • Complex (multi-agent): an ant farm • Complex Adaptive: an ant colony • Agent: an employee • Complex (multi-agent): a firm • Complex Adaptive: a market 23

24. You Don’t Know What You Don’t Know.. • Of course, details do matter • (Ant biology is important if the colony faces an epidemic) • But we can’t know or model everything. • Therefore, we have to consider: • What's difficult to discover versus simply unknowable (which assumptions are unavoidable) • Whether an incorrect assumption can be corrected later (which errors matter most) • What information is valuable and why (cost-benefit of research) • How much error the system can tolerate (volatility, constraints) • “Fools ignore complexity. Pragmatists suffer it. Some can avoid it. Geniuses remove it.” — Alan Perlis 24

25. Systems Thinking • We also don't have to model every system in order to take advantage of system dynamics principles; just thinking in terms of systems can be helpful: • First, systems thinking can be applied broadly: All systems tend to exhibit certain behaviors that we can learn to isolate and recognize, and that can give us a decided advantage even if we can't formally analyze every system. • Second, systems are everywhere — not just business. Thinking in terms of systems gives us a means to approach problems in other disciplines, and a way to apply the lessons learned in one field to another. 25

26. Systems Thinking Systems Thinking • Third, systems thinking focuses us on the things that matter — inputs and outputs, rival behavior, tolerances, repeated effects: high-level dynamics... • Fourth, systems thinking is a very powerful abstraction: • The inputs and outputs of a system can be easily changed to model different organizational goals or even different value networks • Systems are modular, so the same organization can be modeled even if it contains very dissimilar systems • This modularity is also flexible — it can help organize our thinking when dealing with very difficult problems, like merging two organizations or adapting to new market conditions 26

27. System Behavior • “One can only display complex information in the mind. Like seeing, movement or flow or alteration of view is more important than the static picture, no matter how lovely.” — Alan Perlis • By studying complex systems, we can learn to recognize certain consistent patterns produced in such systems, what causes those patterns, and the effects they produce. 27

28. Wisdom • System dynamics are just tools. • Someone has to make and maintain a model, and a model is only as good as its data. This skill (art?) requires discipline and practice. • We must make consistently good assumptions; errors are bad, but systemic errors will be fatal. • Therefore, in order to effectively analyze systems, we need reliable ways of adapting our models and avoiding systemic or repeated errors. • We have to recognize bias, and we must be self-critical: this is part of what we mean by "wisdom." 28