"All
forms of complex causation, and especially nonlinear transformations,
admittedly stack the deck against prediction. Linear describes an outcome
produced by one or more variables where the effect is additive. Any other
interaction is nonlinear. This would include outcomes that involve step
functions or phase transitions. The hard sciences routinely describe nonlinear
phenomena. Making predictions about them becomes increasingly problematic when multiple
variables are involved that have complex interactions. Some simple nonlinear
systems can quickly become unpredictable when small variations in their inputs
are introduced." (Richard N Lebow, "Forbidden Fruit: Counterfactuals
and International Relations", 2010)
"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, "Partial Memories:
Sketches from an Improbable Life", 2010)
"Most
systems in nature are inherently nonlinear and can only be described by
nonlinear equations, which are difficult to solve in a closed form. Non-linear
systems give rise to interesting phenomena such as chaos, complexity, emergence
and self-organization. One of the characteristics of non-linear systems is that
a small change in the initial conditions can give rise to complex and
significant changes throughout the system. This property of a non-linear system
such as the weather is known as the butterfly effect where it is purported that
a butterfly flapping its wings in Japan can give rise to a tornado in Kansas.
This unpredictable behaviour of nonlinear dynamical systems, i.e. its extreme
sensitivity to initial conditions, seems to be random and is therefore referred
to as chaos. This chaotic and seemingly random behaviour occurs for non-linear
deterministic system in which effects can be linked to causes but cannot be
predicted ahead of time." (Robert K Logan, "The Poetry of Physics and
The Physics of Poetry", 2010)
"System dynamics is an approach to understanding the behaviour of over time. It deals with internal feedback loops and time delays that affect the behaviour of the entire system. It also helps the decision maker untangle the complexity of the connections between various policy variables by providing a new language and set of tools to describe. Then it does this by modeling the cause and effect relationships among these variables." (Raed M Al-Qirem & Saad G Yaseen, "Modelling a Small Firm in Jordan Using System Dynamics", 2010)
"In dynamical systems, a bifurcation occurs when a small smooth change made to the parameter values (the bifurcation parameters) of a system causes a sudden 'qualitative' or topological change in its behaviour. Generally, at a bifurcation, the local stability properties of equilibria, periodic orbits or other invariant sets changes." (Gregory Faye, "An introduction to bifurcation theory", 2011)
"Each
systems archetype embodies a particular theory about dynamic behavior that can
serve as a starting point for selecting and formulating raw data into a
coherent set of interrelationships. Once those relationships are made explicit
and precise, the 'theory' of the archetype can then further guide us in our
data-gathering process to test the causal relationships through direct
observation, data analysis, or group deliberation." (Daniel H Kim,
"Systems Archetypes as Dynamic Theories", The Systems Thinker Vol. 24
(1), 2013)
"Statisticians set a high bar when they assign a cause to an effect. [...] A model that ignores cause–effect relationships cannot attain the status of a model in the physical sciences. This is a structural limitation that no amount of data - not even Big Data - can surmount." (Kaiser Fung, "Numbersense: How To Use Big Data To Your Advantage", 2013)
"Without
precise predictability, control is impotent and almost meaningless. In other
words, the lesser the predictability, the harder the entity or system is to
control, and vice versa. If our universe actually operated on linear causality,
with no surprises, uncertainty, or abrupt changes, all future events would be
absolutely predictable in a sort of waveless orderliness." (Lawrence K
Samuels, "Defense of Chaos", 2013)
"A
basic problem with MRA is that it typically assumes that the independent
variables can be regarded as building blocks, with each variable taken by
itself being logically independent of all the others. This is usually not the
case, at least for behavioral data. […] Just as correlation doesn’t prove
causation, absence of correlation fails to prove absence of causation.
False-negative findings can occur using MRA just as false-positive findings
do—because of the hidden web of causation that we’ve failed to identify."
(Richard E Nisbett, "Mindware: Tools for Smart Thinking", 2015)
"The
theory behind multiple regression analysis is that if you control for
everything that is related to the independent variable and the dependent
variable by pulling their correlations out of the mix, you can get at the true
causal relation between the predictor variable and the outcome variable. That’s
the theory. In practice, many things prevent this ideal case from being the
norm." (Richard E Nisbett, "Mindware: Tools for Smart Thinking",
2015)
"The
work around the complex systems map supported a concentration on causal
mechanisms. This enabled poor system responses to be diagnosed as the
unanticipated effects of previous policies as well as identification of the
drivers of the sector. Understanding the feedback mechanisms in play then
allowed experimentation with possible future policies and the creation of a
coherent and mutually supporting package of recommendations for
change." (David C Lane et al,
"Blending systems thinking approaches for organisational analysis:
reviewing child protection", 2015)
"Correlation
is not equivalent to cause for one major reason. Correlation is well defined in
terms of a mathematical formula. Cause is not well defined." (David S
Salsburg, "Errors, Blunders, and Lies: How to Tell the Difference",
2017)
"Effects
without an understanding of the causes behind them, on the other hand, are just
bunches of data points floating in the ether, offering nothing useful by
themselves. Big Data is information, equivalent to the patterns of light that
fall onto the eye. Big Data is like the history of stimuli that our eyes have
responded to. And as we discussed earlier, stimuli are themselves meaningless
because they could mean anything. The same is true for Big Data, unless
something transformative is brought to all those data sets…
understanding." (Beau Lotto, "Deviate: The Science of Seeing
Differently", 2017)
"The
main differences between Bayesian networks and causal diagrams lie in how they
are constructed and the uses to which they are put. A Bayesian network is
literally nothing more than a compact representation of a huge probability
table. The arrows mean only that the probabilities of child nodes are related
to the values of parent nodes by a certain formula (the conditional probability
tables) and that this relation is sufficient. That is, knowing additional
ancestors of the child will not change the formula. Likewise, a missing arrow
between any two nodes means that they are independent, once we know the values
of their parents. [...] If, however, the same diagram has been constructed as a
causal diagram, then both the thinking that goes into the construction and the
interpretation of the final diagram change." (Judea Pearl & Dana
Mackenzie, "The Book of Why: The new science of cause and effect",
2018)
"Again,
classical statistics only summarizes data, so it does not provide even a
language for asking [a counterfactual] question. Causal inference provides a
notation and, more importantly, offers a solution. As with predicting the
effect of interventions [...], in many cases we can emulate human retrospective
thinking with an algorithm that takes what we know about the observed world and
produces an answer about the counterfactual world." (Judea Pearl &
Dana Mackenzie, "The Book of Why: The new science of cause and
effect", 2018)
"Bayesian networks inhabit a world where all questions are reducible to probabilities, or (in the terminology of this chapter) degrees of association between variables; they could not ascend to the second or third rungs of the Ladder of Causation. Fortunately, they required only two slight twists to climb to the top." (Judea Pearl & Dana Mackenzie, "The Book of Why: The new science of cause and effect", 2018)
"Some
scientists (e.g., econometricians) like to work with mathematical equations;
others (e.g., hard-core statisticians) prefer a list of assumptions that
ostensibly summarizes the structure of the diagram. Regardless of language, the
model should depict, however qualitatively, the process that generates the data
- in other words, the cause-effect forces that operate in the environment and
shape the data generated." (Judea Pearl & Dana Mackenzie, "The
Book of Why: The new science of cause and effect", 2018)
"The
calculus of causation consists of two languages: causal diagrams, to express
what we know, and a symbolic language, resembling algebra, to express what we
want to know. The causal diagrams are simply dot-and-arrow pictures that
summarize our existing scientific knowledge. The dots represent quantities of
interest, called 'variables', and the arrows represent known or suspected
causal relationships between those variables - namely, which variable 'listens'
to which others." (Judea Pearl & Dana Mackenzie, "The Book of
Why: The new science of cause and effect", 2018)
"We often say that causes precede effects and yet, in the elementary grammar of things, there is no distinction between 'cause' and 'effect'. There are regularities, represented by what we call physical laws, that link events of different times, but they are symmetric between future and past. In a microscopic description, there can be no sense in which the past is different from the future."
"With Bayesian networks, we had taught machines to think in shades of gray, and this was an important step toward humanlike thinking. But we still couldn’t teach machines to understand causes and effects. [...] By design, in a Bayesian network, information flows in both directions, causal and diagnostic: smoke increases the likelihood of fire, and fire increases the likelihood of smoke. In fact, a Bayesian network can’t even tell what the 'causal direction' is." (Judea Pearl & Dana Mackenzie, "The Book of Why: The new science of cause and effect", 2018)
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