Business Analysis for Responsible Organizations (MANBPRO363)
Institution
Radboud Universiteit Nijmegen (RU)
Complete summary of the all the chapters from the book "Thinking in Systems" by Meadows. Within chapter 5, I incorporated a complete overview of all the archetypes one must learn, based on the information provided in the lectures. Author of this summary scored an 8,5 for the subject "Business Analy...
Business Analysis for Responsible Organizations (MANBPRO363)
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Thinking in Systems
Inhoud
1. The Basics...........................................................................................................................................2
2. A Brief Visit to the System Zoo...........................................................................................................3
3. Why systems work so well..................................................................................................................4
4. Why systems surprise us....................................................................................................................5
5. System traps and opportunities..........................................................................................................5
Policy resistance – fixes that fail.........................................................................................................6
Tragedy of the commons....................................................................................................................6
Drift to low performance – eroding goals...........................................................................................8
Escalation...........................................................................................................................................9
Success to the successful – competitive exclusion.............................................................................9
Shifting the burden to the intervenor - addiction.............................................................................10
Rule beating......................................................................................................................................11
Seeking the wrong goal....................................................................................................................12
6. Creating change in systems and in our philosophy...........................................................................12
7. Living in a world of systems..............................................................................................................14
,1. The Basics
A system is a set of things - people, cells, molecules or whatever - interconnected in such a way that
they produce their own pattern of behaviour over time, in a way that achieves something. The
system may be buffeted, constricted, triggered, or driven by outside forces. But the system’s
response to these forces is characteristic of itself. Systems happen all at once, instead of occuring
only one at a time in linear, logical order. They are connected not just in one direction, but in many
directions simultaneously. Systems contains:
Elements Often visible and therefore easy to mention, but can be invisible as well
(beliefs). You can divide elements in sub an sub-sub elements, but make sure it stays clear.
Interconnections Can be physical, but also invisible like information flows (very
common!).
Function/purpose Are not always mentioned (or do not turn out to be what is
mentioned). Can be found by looking at a system for a while. Purposes are deduced from
behavior, not from rhetoric or stated goals. System purposes need not to be human purposes
and are not necessarily those intended by any single actor within the system. In fact, one of
the most frustrating aspects of systems is that the purpose of subunits may add to an overall
behaviour that no one wants. Systems contain sub-purposes (of the individual elements).
Keeping sub-purposes and overal purposes in harmony is an essential function of a succesful
system.
A system generally goes on being itself, changing only slowly if at all, even with complete
substitutions of its elements – as long as its interconnections and purposes remain intact. If
interconnections or the purpose change, the system will change dramatically. Although all aspects
are important, often the purpose is of the most importance, and the elements of the least.
Stocks are the elements of the system that you can see, feel, count, or measure at any given time
(again, do not have to be physical). Stocks can change over time through the actions of a flow. We
speak of dynamic equilibrium when the level/behaviour of a system does not change, althought
there is in- and outflow (example bathtub). A stock can be increased by decreasing its outflow rate as
well as by increasing its inflow rate. A stock however, takes time to change, because flows take time
to flow. Therefore, stocks act as delays or buffers or shock absorbers in systems. Stocks allow inflows
and outflows to be decoupled and to be independent and temporarily out of balance with each
other. People monitor stocks constantly and make decisions and take actions designed to raise or
lower stocks or to keep them within acceptable ranges. A feedback loop is a closed chain of causal
connections from a stock, through a set of decisions or rules or physical laws or actions that are
dependent on the level of the stock, and back again through a flow to change the stock. Feedback
loops can cause stocks to maintain their level within a range or grow or decline. Beware that labels in
diagram do not contain a direction (could be used in two-ways). We distinct:
Balancing feedback loops (B) Tries to keep a stock at a givin value or within a range of
values. They are equilibrating or goal-seeking structures in systems and are both sources of
stability and sources of resistance to change.
Reinforcing feedback loops (R) Enhances whatever direction of change is imposed on it. Is
amplifying, reinforcing, self-multiplying and snowballing. Leads to exponential growth. The
doubling time can be calculated by: 70 / growth rate (expressed in percentage).
The concept of feedback opens up the idea that a system can cause its own behaviour.
,2. A Brief Visit to the System Zoo
The information delivered by a feedback loop – even nonphysical feedback – can only affect future
behavior; it can’t deliver a signal fast enough to correct behavior that drove the currenct feedback.
Just like a person in the system who makes a decision based on the feedback can’t change the
behavior of the system that drove the current feedback; the decisions he or she makes will affect
only future behaviour. This means that there will always be a delay in responding.
A stock-maintaining balancing feedback loop must have its goal set appropriately to compensate for
draining or inflowing processes that affect that stock. Otherwise, the feedback process will fall short
of or exceed the target for the stock (example: thermostat always little higher than your target
temperature). An example of a stock with two balancing feedback loops is the room temperature
influenced by a thermostat and heat leaking to outside (see page 36 and 40). A delay in a balancing
feedback loop makes a system likely to oscillate (hevige fluctuaties).
Sometimes we deal with a stock influenced by both a balancing and a reinforcing feedback loop, for
example the population. The stock (=population) will grow exponentially or die off, depending on
whether its reinforcing feedback loop determining births is stronger than its balancing feedback loop
determining deaths. In these systems, we deal with shifting dominance of feedback loops, meaning
that the behavior of the system is determined by the shifts between dominance of the feedback
loops. Complex behaviors of systems often arise as the relative strenghts of feedback loops shift,
causing first one loop and then another to dominate behavior. Possible behaviors in this systems are
(page 46):
Exponantial growth, when reinforcing loop > balancing loop (growth)
Dies off, when balancing loop > reinforcing loop (decline)
Will level off, when reinforcing loop = balancing loop (stabilization)
Questions for testing the value of a model are:
1. Are the driving factors likely to unfold this way?
2. If they did, would the system react this way?
3. What is driving the driving factors?
Model utility depends not on whether its driving scenarios are realistic (since no one can know for
sure), but on whether it responds with a realistic behaviour. Systems with similar feedback structures
produce similar dynamic behaviors.
In physical, exponentially growing systems, there must be at least one reinforcing loop driving the
growth and at least one balancing loop contraining the growth, because no physical system can grow
forever in a finite environment. This creates the “limits-to-growth” archetypes. When dealing with
resource-constraints, we distinguish renewable and non-renewable resources. A quantity growing
exponentially toward a constraint or limit reaches that limit in a surprisingly short time. The doubling
or quadrupling of the nonrenewable resource only give little added time to develop alternatives. The
real choice in the management of a nonrenewable resource is whether to get rich very fast or to get
less rich but stay that way longer.
Nonrenewable resources are stock-limited. The entire stock is available at once, and can be
extracted at any rate (limited mainly by extraction capital). But since the stock is not renewed, the
, faster the extraction rate, the shorter the lifetime of the resource. Renewable resources are flow-
limited. They can support extraction or harvest indefinetly, but only at a finite flow rate equal to
their regeneration rate. If they are extracted faster tan they regenerate, they may eventually be
driven below a critical threshold and become, for all practical purposes, nonrenewable. There are
therefore three possible behaviours of renewable source systems:
Overshoot and adjustment to a sustainble equilibrium
Overshoot beyond that equilibrium followed by oscillation around it, and
Overshoot followed by collapse of the resource and the industry dependent on the resource
3. Why systems work so well
Systems work well because of:
Resilience “The ability to bounce or spring back into shape, position, etc., after being
pressed or stretched. Elasticity”. A set of feedback loops that can restore or rebuild feedback
loops is resilience at a still higher level – meta-resilience. Resilience is not the same as being
static or constant over time. Resilient systems can be very dynamic with short-term
oscillations, periodic outbreaks, etc. Static stability is something you can see: it’s measured
by variation in the condition of a system week by week or year by year. Resilience is
something that may be very hard to see, unless you exceed its limits, overwhelm and
damage the balancing loops, and the system structure breaks down. Because resilience may
not be obvious without a whole-system view, people often sacrifice resilience for stability or
productivity (short-term). Systems need to be managed not only for productivity or stability,
but also for resilience (the ability to recover from perturbation).
Self-organisation Systems often have the property of self-organization – the ability to
structure themselves, to create new structure, to learn, diversify and complexify. Even
complex forms of self-organization may arise from relatively simple organizing rules – or may
not. Self-organisation produces heterogeneity and unpredictability.
Hierarchy Self-organizing systems often generate hierarchy: the arrangement of systems
and subsystems. Hierarchies are brilliant systems inventions, not only because they give a
system stability and resilience, but also because they reduce the amount of information that
any part of the system has to keep track of. In hierarchical systems relationships within each
subsystem are denser and stronger than relationships between subsystems. Hierarchies
evolve from the lowest level up – from the pieces to the whole. The original purpose of
hierarchy is always to help its originating subsystems do their jobs better. However, when a
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