"Even
if our cognitive maps of causal structure were perfect, learning, especially
double-loop learning, would still be difficult. To use a mental model to design
a new strategy or organization we must make inferences about the consequences
of decision rules that have never been tried and for which we have no data. To
do so requires intuitive solution of high-order nonlinear differential
equations, a task far exceeding human cognitive capabilities in all but the
simplest systems." (John D Sterman,
"Business Dynamics: Systems thinking and modeling for a complex
world", 2000)
"A
model isolates one or a few causal connections, mechanisms, or processes, to
the exclusion of other contributing or interfering factors - while in the
actual world, those other factors make their effects felt in what actually
happens. Models may seem true in the abstract, and are false in the concrete.
The key issue is about whether there is a bridge between the two, the abstract
and the concrete, such that a simple model can be relied on as a source of
relevantly truthful information about the complex reality." (Uskali Mäki,
"Fact and Fiction in Economics: Models, Realism and Social
Construction", 2002)
"In a
complex system, there is no such thing as simple cause and effect."
(Margaret J Wheatley, "It's An Interconnected World", 2002)
"Nonetheless,
the basic principles regarding correlations between variables are not that
difficult to understand. We must look for patterns that reveal potential
relationships and for evidence that variables are actually related. But when we
do spot those relationships, we should not jump to conclusions about causality.
Instead, we need to weigh the strength of the relationship and the plausibility
of our theory, and we must always try to discount the possibility of
spuriousness." (Joel Best, "More Damned Lies and Statistics: How
numbers confuse public issues", 2004)
"We’re accustomed to thinking in terms of centralized control, clear chains of command, the straightforward logic of cause and effect. But in huge, interconnected systems, where every player ultimately affects every other, our standard ways of thinking fall apart. Simple pictures and verbal arguments are too feeble, too myopic." (Steven Strogatz, "Sync: The Emerging Science of Spontaneous", 2004)
"Chance
is just as real as causation; both are modes of becoming. The way to model a
random process is to enrich the mathematical theory of probability with a model
of a random mechanism. In the sciences, probabilities are never made up or
'elicited' by observing the choices people make, or the bets they are willing
to place. The reason is that, in science and technology, interpreted
probability exactifies objective chance, not gut feeling or intuition. No
randomness, no probability." (Mario Bunge, "Chasing Reality: Strife
over Realism", 2006)
"Thus,
nonlinearity can be understood as the effect of a causal loop, where effects or
outputs are fed back into the causes or inputs of the process. Complex systems
are characterized by networks of such causal loops. In a complex, the
interdependencies are such that a component A will affect a component B, but B
will in general also affect A, directly or indirectly. A single feedback loop can be positive or
negative. A positive feedback will amplify any variation in A, making it grow
exponentially. The result is that the tiniest, microscopic difference between
initial states can grow into macroscopically observable distinctions."
(Carlos Gershenson, "Design and Control of Self-organizing Systems",
2007)
"A
system is a set of things – people, cells, molecules, or whatever - interconnected in such a way that they produce their own pattern of behavior
over time. […] The system, to a large extent, causes its own behavior."
(Donella H Meadows, “Thinking in Systems: A Primer”, 2008)
"For
me, as I later came to say, cybernetics is the art of creating equilibrium in a
world of possibilities and constraints. This is not just a romantic description,
it portrays the new way of thinking quite accurately. Cybernetics differs from
the traditional scientific procedure, because it does not try to explain
phenomena by searching for their causes, but rather by specifying the
constraints that determine the direction of their development." (Ernst von
Glasersfeld, "The Cybernetics of Snow Drifts 1948", 2009)
"Traditional statistics is strong in devising ways of describing data and inferring distributional parameters from sample. Causal inference requires two additional ingredients: a science-friendly language for articulating causal knowledge, and a mathematical machinery for processing that knowledge, combining it with data and drawing new causal conclusions about a phenomenon." (Judea Pearl, "Causal inference in statistics: An overview", Statistics Surveys 3, 2009)
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