Business, Law, and Innovation
System Dynamics
Spring 2011
Professor Adam Dell
The University of Texas School of Law
What is System Dynamics?
• System dynamics is the application of systems
theory to the behavior of complex systems.
• To review, systems theory is:
• “The basic idea of system theory is that all things in the
universe (rivers; baseball games; galaxies) can be viewed
as discrete systems, operating under a defined set of rules.
While the systems may be different, they exhibit strikingly
similar behavior. If different systems behave similarly,
perhaps it's because they are connected.”
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What are complex systems?
• There are various definitions, but for our purposes
“complex systems” are systems in which there are
multiple interactions 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.
•
A car, on the other hand, is merely a machine — complicated, but its
operation can be understood by studying the component parts.
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Basic Concepts: 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:
Stock
Population
Births
Deaths
Flows
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Basic Concepts: Feedback loops
• Positive / negative feedback loops, delayed loops
Births have a positive feedback effect,
but it’s delayed (think baby boomers)
Births
Population
Available
Resources
Deaths
Population increases take up available
resources, which decreases the birth rate
(a negative feedback effect)
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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.
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A (too) simple model
• This model may reflect reality, but isn’t that useful:
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A complex but useful model
Life Insurance in the UK:
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Complex adaptive systems
• Complex adaptive systems are a conceptual 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
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Don't Be Confused
• All of the previous examples could be characterized
as “complex” or “adaptive” on some level (all life is
“complex,” even ants) — but we use different levels
of abstraction depending on the analysis:
• For example, we don't necessarily need to know the
physiology of an ant to study its colony — it's enough that
we can make generalizations about groups of ants.
• In fact, a “complex adaptive” system may be easier to
analyze than its “complex” components; e.g., markets are
less chaotic than firms.
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On the other hand...
• 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
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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.
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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
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System Behaviors
•“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.
•Here are some examples (some we’ve seen before):
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Feedback Effects: 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 significant instability
• Feedback effects tend to amplify entropy
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Feedback Effects: Equilibria
• Systems often settle into a stable state
(equilibrium); the interesting question is how they
can be knocked out of those states
• Systems can have multiple equilibria:
• Tipping points and chasm-crossing can be thought of as
moving between equilibria
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Common Patterns: Golden Mean
• Certain formations tend to be
ubiquitous in complex systems:
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Common Patterns: Order or Chaos?
• [Wolfram]
• [Cellular automata: order disguised as chaos]
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Chaotic Effects
• [Complex systems exhibit chaotic effects; sensitivity
to initial conditions]
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Chaotic Effects: Nonlinearity
• Chaotic systems exhibit nonlinear effects — i.e.,
linear changes cause qualitative changes in state
Linear Change
Complexity
Chaos
Order
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Network Effects
• [Network effects are very important to systems!
They have important impacts on system dynamics
models because linear inflows lead to exponential
outflows]
• [Network effects have another, subtle aspect -- if
you interconnect two systems exhibiting network
effects, or combine two stocks feeding network
effects, the resulting change is exponential]
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Emergence
• A system can be more than the sum of its parts!
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Wisdom
• However, systems thinking and 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."
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