John O. Campbell
This is an excerpt from the book: The Knowing Universe.
As we have discussed, an important aspect of inferential
systems is their autopoietic form; inferential systems cause entities to exist.
That is, the knowledge which models accumulate is causal knowledge capable of
causing the specific structures, adaptations and regulation which form the entity
and allow it to resist natural forces towards dissipation.
Unfortunately, scientist and statisticians often consider ‘cause’
as something of a forbidden concept because data or evidence, on its own, can indicate
a correlation but not a causal relationship. An oft quoted example is that the
rising sun and the crowing rooster are correlated but that the rooster’s
crowing does not cause the sun’s rising. Something beyond data is required to
establish causality.
A recent
scientific innovation led by Judea Pearl, sometimes called the ‘causal
revolution’ (128; 129), may have completed
the logic of science as described by Bayesian probability (61). In particular, the causal revolution provides
some important new understanding that illuminates the autopoietic nature of
inferential systems underlying existence.
While the causal
revolution has provided methods of understanding new to science, these methods
may have previously been unconsciously discovered by humans in the course of
human genetic evolution. It is widely understood that sometime between 70,000
and 30,000 years ago a series of mutations in the genes specifying our neural
structures resulted in a ‘cognitive revolution’ that powered our species
cultural evolution and provided key abilities enabling our (perhaps temporary)
ascendency over other biological forms. Some researchers attribute this
cognitive revolution to a subconscious realization of casual reasoning, the
same causal reasoning that is now becoming consciously understood and that forms
the basis of the scientific causal revolution (130; 129;
131).
In this
view the current scientific revolution is a mere rediscovery, in conscious
terms, of a fitness enhancing reasoning process which natural selection placed in
our subconscious minds, tens of thousands of years ago.
Unfortunately,
so far, the causal revolution has acknowledged the process of causal reasoning
to be an ability possessed only by our species. In this section we will
demonstrate that causal reasoning, like the more general process of Bayesian
inference, is a key component of the process of evolutionary change which has
brought into existence and evolved reality’s many domains including the
cosmological, quantum, biological, neural and cultural domains (13).
The causal
revolution is better cast as a recent scientific discovery of a general law of
nature which underlies the many existing forms composing our universe and which
has operated since the beginning, long before the evolution of our species. In
that sense the causal revolution provides powerful tools for understanding inferential
systems and general evolutionary processes as described by the theory of
universal Darwinism (4; 6; 13; 9).
We will not
only demonstrate that the relationship between causal reasoning and inferential
systems provides a generalized context in which causal reasoning is found to
participate in the universe’s many evolutionary processes but will also provide
specific details of causal mechanisms operating in inferential systems. The causal revolution understood within the
context of autopoietic inferential systems provides insights into the widespread
role of causality within natural systems.
Contrary to traditional statistics and the mantra of the ‘big
data’ movement that ‘data is everything and everything is data’[1],
Pearl points out that this perspective severely constrains the scope of
statistics. Traditional statistics only views data as describing correlations. Correlations
are not causes as illustrated by the cliché of the crowing rooster and the rising
sun.
Pearl demonstrates that descriptions of causal relationships
require an ingredient beyond data, in addition they require a model that
hypothesizes causal relationships. As Pearl describes it (129):
The
model should depict, however qualitatively, the process that generates the data
– in other words, the cause and effect forces that operate in the environment
and shape the data generated.
The generative model described by Pearl is the same model that
operates within inferential systems, a model composed of a competing family of hypotheses
that explain how the evidence was generated or caused. Thus, effective models
are constrained to model how existing entities and processes are generated or brought
into existence.
Pearl’s preferred type of causal model is the causal diagram
which depicts the variables involved in a process as points and connects these points
with arrows that serve to hypothesize their cause and effect relationships. He
is clear that these causal diagrams are hypotheses or best guesses as to the
actual causal relationship and that they must be subject to the scrutiny of the
data or evidence.
If the evidence does not support the diagram, Pearl suggest we
should take another, perhaps informed, guess as to the actual relationship, and
test its implications against the data. We should continue with these guesses
until a diagram is discovered that is consistent with all the data (129):
If
the data contradict this implication, then we need to revise our model.
Rather than test one hypothesis at a time, a more systematic
approach often seen with inferential systems, is to consider a model composed
of a complete family of competing hypotheses describing the causal connections
between the variables. Then application of the data to the model consists of updating
the probability assigned to each possible hypothesis. Importantly this type of model
often quickly simplifies as the data eliminates unsupported hypotheses.
We might note Pearl’s reference above to ‘cause and effect forces’ that are described
by causal models. As noted in previous sections, system regulators generically
overcome challenges to existence posed by physical law through the application
of cause and effect forces. It is in this sense that the knowledge of inferential
systems initiates a causal chain of forces which form and maintain systems within
existence, and it is in this sense that inferential systems are autopoietic.
It is important to understand what Pearl means by causation:
For
the purpose of constructing the diagram, the definition of ‘causation’ is
simple, if a little metaphorical: a variable X is a cause of Y if Y ‘listens’
to X and determines its value in response to what it hears.
Although Pearl’s description is both clear and accurate, the
term ‘listens to’ may be a little anthropomorphic when applied to abstract variables.
Perhaps a better term is ‘receives information from’ and then his definition of
causation becomes:
A
variable X is a cause of Y if Y receives information from X and determines its
value in response to that information.
In these terms, causation, as defined by Pearl, is an exact
analog to inferential systems; models within inferential systems are updated by
information or evidence in the sense that the value of the probability of each
hypothesis composing the model is updated in response to the information or
evidence it receives. This update or response has been generally described in
terms of force; the evidence may be said to ‘force’ some of a model’s
hypotheses to be assigned greater probability and some less (21). Thus, the model is
shaped by the force of the evidence and we may say that the evidence causes the
resulting model.
This analogy between causation and force is widely accepted.
As Wikipedia tells us (132):
Causal
relationships may be understood as a transfer of force. If A causes B,
then A must transmit a force (or causal power) to B which results in the
effect.
In these terms, considering the close analogy between causation,
inference and force, inferential systems may be considered the fundament form
of causation. It is forces which cause effects and forces may always be
understood in terms of the updating of inferential systems. This generic
concept of force has been explored by Steven Frank (born 1957) who concludes
that the process of inference may be considered as the force of data applied to
models (21).
As we have seen in our previous discussion of physical
forces, at the fundamental physical level a force is produced when information
contained in a gauge boson updates the wave function of another quantum system.
In Pearl’s terms we might say that the quantum system listens to the gauge boson
and determines or updates its value, for example the value of its momentum, in
response to what it hears.
Although nature has employed inferential systems since the
beginning as its primary engine of existence, scientific understanding is only
now becoming able to more fully describe them. The causal revolution provides
our scientific understanding with important tools for describing the
autopoietic aspect of inferential systems. In short, this new tool allows us to
scientifically describe inferential systems as initiating a cascade of causes whose
effects are existing systems. The mechanisms by which causes lead to effects
may be understood in terms of orchestrated or regulated forces and these forces,
as we have seen, are essential to overcome the many natural obstacles to
existence. The initiated causes must be
highly orchestrated to achieve the effect of existence and knowledge required
to achieve this orchestration is accumulated by the inferential system through
the process of Darwinian evolution.
A breakthrough made by the causal revolution is its understanding
that a full scientific description of a causal process must include a causal model
of the hypothesised causal cascade. Central to our understanding of inferential
systems is that this same causal model is necessary to orchestrate or regulate existing
systems as required by the good regulator theorem. In other words, the causal
revolution has discovered that scientific descriptions of existing systems must
include a causal model, and this is necessary because causal models are an
essential component of existing systems; a scientific description of them that
did not include a causal model would be incomplete.
Central to the causal revolution’s understanding is that
causation often involves interventions made to the normal or spontaneous
unfolding of events. If an effect is caused, that means that the normal range
of possible outcomes is constrained to a single outcome and this selection is
described as occurring through an intervention in the normal course of events (128). In terms of
inference, circumstances are causally orchestrated so that the received data updates
the model to strongly predict or initiate a single outcome or effect.
At the core of autopoietic inferential systems is their
ability to cause outcomes, to intervene in the spontaneous course of events and
select a single outcome that might otherwise be extremely unlikely. As a
biological example, we might consider that the knowledge contained in an
organism’s genome causes specific outcomes or effects. A given three letter
genetic codon identifies a single specific amino acid to be added to a protein
and a gene, composed of a string of codons, identifies a specific complete
protein. In turn, that protein, if it is an enzyme, may catalyze a specific
bio-chemical reaction, an outcome or effect that is extremely unlikely to occur
without the participation of the enzyme. This biological cascade of
knowledgeable causes, resulting in specific effects, illustrates the tight
causal control or regulation exercised by autopoietic inferential systems.
Pearl describes the causal revolution in terms of a ladder
having three rungs which together fully describe causal systems. The first rung
is characterized by correlations, the traditional study of statistics. The
second rung is characterized by causal interventions of the type we have just
examined. The third rung involves counterfactuals or hypothesis regarding what
might be rather than what is. A dictionary example of a counterfactual
hypothesis is ‘If kangaroos had no tails,
they would topple over’ (133).
While causal interventions describe the autopoietic aspect
of inferential systems in that they are required to ensure that the system’s
knowledge causes a specific process of self-creation and self maintenance, counterfactual
hypotheses describe the evolutionary aspects of inferential systems as they make
hypotheses which have never been tested before. Counterfactual hypotheses are
tools which may be used to search through the space of possibilities, a search
for those possibilities which may be made actual, which may be brought into
existence.
For example, the counterfactual hypothesis involving
kangaroos and their tails may be coded in the genome of a kangaroo where a
mutational cause produces the effect of a tailless kangaroo offspring. The
mutant gene codes the counterfactual hypothesis which in effect asks if a
kangaroo with no tail would be reproductively successful (didn’t always fall
over). If the resulting tailless kangaroo did achieve reproductive success the
possibility would be made actual and tail-less kangaroos would achieve
existence in the world.
The causal revolution has extended scientific understanding
of causation beyond correlations to include causal models, interventions and
counterfactual hypotheses. I have provided examples from biology to illustrate
how this extended understanding describes actual causal processes central to
biology and in chapter 3 we will see that this understanding is also central to
domains of reality other than biology. In effect autopoietic inferential
systems in all domains cause their own existence and evolve through their
exploration of counterfactual hypotheses.
However, it appears that those who forged the causal
revolution consider it more of a revolution in scientific calculation than in the
understanding of actual phenomena found in nature. Pearl, for example writes
extensively on how causal models can assist the computation of relationships
between variables but nowhere does he suggest that these models actually exist
in the natural world or their essential role in bringing actual phenomena into
existence (129). Instead he understands
the findings of the causal revolution as calculational devices that allow us to
calculate data and arrive at causal conclusions.
This is a common misunderstanding when a scientific revolution
first brings some important new physical phenomena to light for which there is
little direct observational evidence. It occurred, for example, in the late
1800s when many considered the controversial concept of atoms as referring to a
shorthand for making scientific calculations rather than to actual phenomena.
When Boltzmann tried to publish his work deriving thermodynamics from atomic
theory, his journal editors insisted that he refer to atoms as ‘bilder’; merely
models or pictures that were not ‘real’ (134). Again, in the early
1900s when Mendel’s concept of genes was rediscovered there ensued a great
debate among biologist on whether Mendel’s theory conflicted with Darwin’s. The
one thing agreed upon by most leading biologists was that Mendel’s ‘genetics’ was not a physical process
but merely a means of making calculations. As recounted by the philosopher of
science David Hull (37):
As
much as Bateson might disagree with Pearson and Weldon about the value of
Mendelian genetics, he agreed with them that it was
unscientific to postulate the existence of genes as material bodies. They were
merely calculation devices.
As in the case of atoms and genetics I am confident that
history will demonstrate that the new calculational methods of the causal
revolution describe actual physical reality. The task of science is to describe
nature and when new calculational tricks are discovered that provide powerful
methods of describing reality at a deeper level, we may rest assured this is
because nature also uses those same tricks to create and maintain the actual phenomena,
in other words, that science has merely rediscovered and described nature’s own
methods. This is the proper role of science.
References
1. Causal inference in
statistics: an overview. Pearl, Judea. s.l. : Statistics
Surveys, 2009, Vols. Volume 3 (2009), 96-146.
2. Pearl, Judea and Mackenzie, Dana. The Book of
Why: The New Science of Cause and Effect. s.l. : Basic Books, 2018.
ISBN-10: 046509760X.
3. Jaynes, Edwin T. Probability Theory: The Logic
of Science. s.l. : University of Cambridge Press, 2003.
4. Harari, Yuval Noah. Sapiens: A brief history of
humankind. s.l. : Harvill Secker, 2014.
5. Boyer, Pascal. Minds make societies: How
Cognition Explains the World Humans Create. s.l. : Yale University
Press, 2018.
6. Universal Darwinism as a process of Bayesian
inference. Campbell, John O. s.l. : Front. Syst. Neurosci.,
2016, System Neuroscience. doi: 10.3389/fnsys.2016.00049.
7. Dennett, Daniel C. Darwin's Dangerous Idea. New
York : Touchstone Publishing, 1995.
8. Blackmore, Susan. The Meme Machine. Oxford,
UK : Oxford University Press, 1999.
9. Bayesian Methods and Universal Darwinism. Campbell,
John O. s.l. : AIP Conference Proceedings, 2009. BAYESIAN INFERENCE
AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: The 29th
International Workshop on Bayesian Inference and Maximum Entropy Methods in
Science and Engineering. AIP Conference Proceedings, Volume 1193. pp. 40-47.
10. Simple unity among the fundamental equations of
science. Frank, Steven A. s.l. : arXiv preprint, 2019.
11. Wikipedia. Causal reasoning. Wikipedia. [Online]
[Cited: May 26, 2019.] https://en.wikipedia.org/wiki/Causal_reasoning.
12. Google dictionary. Counterfactual. Google
dictionary. [Online] [Cited: June 2, 2019.]
https://www.google.com/search?q=counterfactual&oq=counterfactual&aqs=chrome..69i57j69i59l2j0l3.8063j0j8&sourceid=chrome&ie=UTF-8.
13. Wikipedia. Ludwig Boltzman. Wikipedia. [Online]
[Cited: June 7, 2014.] http://en.wikipedia.org/wiki/Ludwig_Boltzmann.
14. Hull, David L. Science as a Process: An
Evolutionary Account of the Social and Conceptual Development of Science. Chicago
and London : The University of Chicago Press, 1988.
No comments:
Post a Comment