Does science describe the world-in-itself?

John O. Campbell

This is the conclusion of The Knowing Universe

This book proposed an inferential systems definition of existence. In this interpretation, the world-in-itself is portrayed as a vast hierarchically nested series of inferential systems, each diligently investigating the possibilities for specific forms of existence. One benefit of this interpretation is its casting of the universe in an easily knowable form; a little knowledge of inferential systems provides a little knowledge of everything.

Our interpretation of scientific knowledge within an inferential systems framework reveals science itself in a new light; science is an inferential system that accumulates knowledge in essentially the same manner as all other naturally occurring inferential systems. This view of science may inform some problems that have long plagued western philosophy.

One of those problems is the general relationship between existing entities and our perceptions or mental concepts portraying them. During the scientific revolution, roughly between 1543 and 1687 (1), it became apparent that a combination of sensory experience and rational hypotheses could form synergistic intellectual models having powerful, pragmatic effects. Some scientific pioneers, such as Francis Bacon (1561 - 1626), assumed the scientific method would provide ultimate and infallible knowledge of the universe (2). But questions soon emerged regarding the relationship between scientific models and the entities they described.

David Hume

Fifty years after the scientific revolution, David Hume cast some shade on empirical claims of certain knowledge, noting that many natural processes, such as causation, are not entirely amenable to sensory evidence. However, he retained the critical caveat that instead of certainty, sensory evidence can provide probabilistic knowledge. Using this, essentially Bayesian insight, Hume argued that hypotheses judged as unlikely in our prior experience require a greater weight of evidence, or as more recently framed, extraordinary claims require extraordinary evidence (3). He most famously used this insight as a basis for skepticism regarding Christian miracles. At the time, Hume’s views failed to carry the day, and although currently rated the greatest philosopher in British history, his contemporaries excluded him from holding a university post due to his alleged atheistic tendencies (4). Scientific and philosophical insights were not yet strong enough to pose a severe threat to established religious models.

Forty years later Immanel Kant, built upon Hume’s empirical skepticism claiming an essential dichotomy between sensory-based knowledge and the true nature of things in themselves (5):

And we indeed, rightly considering objects of sense as mere appearances, confess thereby that they are based upon a thing in itself, though we know not this thing as it is in itself, but only know its appearances, viz., the way in which our senses are affected by this unknown something.

Kant describes an apparent gulf between things in themselves and any possible empirical knowledge we can have of them. A recent statement of this dichotomy uses the example of the gulf between maps and the territory they model (and admonishes us not to confuse the two). In Kant’s time, many perceived this as a trivial intellectual gulf posing no reason for despair as it did not yet challenge the near-universal religious models. Although lacking empirical foundations, the Christian model of the universe was considered an accurate portrayal of the world-in-itself. Indeed, Kant’s contemporaries understood his radical philosophy as resonating quite well with Christian doctrine. One of his early commentators noted (6):

And does not this system itself cohere most splendidly with the Christian religion? Do not the divinity and beneficence of the latter become all the more evident?  

But scientific and philosophical developments were building bridges between perceptual models and the world-in-itself, bridges that would come to challenge those offered by religious teaching and to undermine our cozy place within Christian models of the universe. Astronomers such as William Herschel (1738 – 1822) found evidence of a vast universe, suggesting that both earth and humanity played apparently insignificant roles – a scenario challenging Christian doctrine. And then Darwin demonstrated that counter to religious teaching, people have descended from earlier forms of life. These and countless other scientific findings undermined the Christian worldview among the intelligentsia.

Not only did this new learning contradict many religious teachings, it also illustrated the constraints that religion placed upon knowledge. Most Protestant sects identified the Bible as revealed truth and considered it a complete worldview for humanity. But the scope of biblical knowledge is relatively minimal. How far could knowledge grow within this confine? For example, the bible makes only passing references to stars. One of the more explicit passages is:

And God made the two great lights—the greater light to rule the day and the lesser light to rule the night—and the stars.

It does not offer any detailed knowledge concerning stars-in-themselves; they are only bit players in this God-centric tale. And it provides no path to greater knowledge of stars. For that, we must look elsewhere.

Under these influences, acceptance of biblical teachings as literal descriptions of the world-in-itself became increasingly untenable. Finally, in 1882 Fredrick Nietzsche (1844 – 1900) announced God’s murder and held humanity responsible:

God is dead. God remains dead. And we have killed him. How shall we comfort ourselves, the murderers of all murderers? What was holiest and mightiest of all that the world has yet owned has bled to death under our knives: who will wipe this blood off us? What water is there for us to clean ourselves? What festivals of atonement, what sacred games shall we have to invent? Is not the greatness of this deed too great for us? Must we ourselves not become gods simply to appear worthy of it?  

After fifteen hundred years, Europe's foundational model of the universe crumbled, leaving no handy alternative. As Martin Heidegger (1889 – 1976), a leading 20^th-century metaphysician, explained, humanity was left exposed to its most significant source of anxiety, the anxiety experienced when we face the finite nature of our existence.

And worse was to come. Ludwig Wittgenstein (1889 – 1951), perhaps the twentieth century’s most influential philosopher (7), described an even more profound metaphysical abyss – claiming that scientific understanding, the slayer of our old theological models, was incapable of offering a replacement model of the world-in-itself. He believed existence had no logical explanation and that its nature must forever remain a mystery (8).

It is not how things are in the world that is mystical, but that it exists.

Wittgenstein had experienced the terrors of WWI’s trench warfare, and although he rose to the occasion displaying remarkable valour, it left him deeply shaken and his philosophy practically a denial of any possibility for human meaning. Wittgenstein was not alone in his visceral reaction to the terrors of WWI. Many began to consider that at best, God was only remotely concerned with the world's workings and that to understand those workings and perhaps even shape them, we would have to look elsewhere. 

Together Nietzsche, Heidegger and Wittgenstein brought western philosophy to the brink of nihilism (9). We had outgrown the Gods providing meaning for millennia and found ourselves instead in a vast, uncaring universe devoid of meaning. Even worse, we were unable to conjure up any convincing alternative model of the world-in-itself. This era perhaps marked a low point for western philosophy. Existentialism, which succeeded this philosophical movement, also lamented human meaninglessness but supplemented despair with a growing sense that meaning was within us, that it was only necessary to pull ourselves up by our bootstraps. Existentialists, such as Soren Kierkegaard (1813 - 1855), Jean-Paul Sartre (1905 - 1980) and Albert Camus (1913 - 1960), maintained that living an authentic life, or at least persevering through the absurdities of life, could lead to meaning.

But even while philosophy despaired, science had quietly begun developing models detailing the human relationship to the world-in-itself. This scientific awakening offered a revolutionary new perspective of our place in the universe, one that converged on several different fronts into a single idea; humans are a part of nature - as the old saying goes, just like the trees and the stars you have a right to be here.

Just as western philosophy flirted with nihilism, science confirmed that everything in the universe, ourselves included, is made of the same hundred-odd elements. This discovery soon developed into the understanding that all elements are forged in stars from the same simple building blocks and spread to the rest of the universe when stars die. We, along with everything else, are composed of stardust.

But science soon moved beyond a simple unification of nature based on a shared universal composition. Perhaps the scientific finding having the greatest impact on our perceived relationship to the universe was the theory of natural selection developed by Charles Darwin. This theory offered an alternative to God’s special creation, placing humans above all other living things and describing humanity as a recent evolutionary design directly related to all other organisms. Darwin’s brilliant description of natural selection in On The Origin Of Species included many examples from the natural world supporting his theory that are incompatible with the Christian theory of creation from design. Many open-minded readers found Darwin’s arguments devastating to the biblical account, causing a collapse in credibility, a profound cultural shock recorded by pessimistic philosophers, such as Nietzsche and Heidegger.

Gone was our favoured place among the gods, and we found ourselves instead exposed to a sense of meaninglessness within a vast, uncaring universe. Yet, once over that initial shock, a closer reading of Darwin reveals new, more profound meaning. As noted by the Darwinian champion Thomas Huxley (1825 – 1895) in his great book Evidence As to Man's Place In Nature (10):

Mr. Darwin's hypothesis is not, so far as I am aware, inconsistent with any known biological fact; on the contrary, if admitted, the facts of Development, of Comparative Anatomy, of Geographical Distribution, and of Palaeontology, become connected together, and exhibit a meaning such as they never possessed before

We were no longer the favoured children of an all-powerful God, but we had gained membership in the more tangible family of all living things. As Darwin succinctly noted, this context provides us with significant meaning (11):

There is grandeur in this view of life

Although scientific explanations, such as natural selection, offered meaning through our context within nature, they often provide only a vague summary of nature-in-itself. Natural selection reduces almost to tautology in its central claim that only the fittest organisms exist because fitness is the relative frequency of existence.  It only escapes tautology due to the non-statistical or functional aspects of fitness composing a vastly complex network of adaptation working in conjunction to retain an organism within existence.  Although natural selection, by itself, does not go far in exposing the thing-in-itself of existence, science began developing knowledge of those mechanistic details.

Kant’s dichotomy, although softened by scientific understanding, remained stark. The modern scientific philosopher Alfred Korzybski (1879 – 1950) framed the Kantian dichotomy with a caveat offering a way forward (12):

A map is not the territory it represents, but, if correct, it has a similar structure to the territory, which accounts for its usefulness.

Here he makes two claims. The first supports Kant’s view that maps (models) and territories (things in themselves) are separate and should not be confused. He describes a dichotomy that is still somewhat faddishly used as a put down to proposed explanations, dismissing them as confusing the map with the territory, mistaking mere description for the thing-in-itself. His second point is more substantial than the first; any map is a good map to the extent that it shares its territory’s logical structure. 

Ensuing developments during the scientific age have explored this caveat bridging Kant’s dichotomy. As one example, Darwinian evolution argues that animals’ sensory perceptions should accurately portray the world-in-itself; senses and perceptions have evolved for the practical purpose of allowing animals to navigate challenges posed by the world-in-itself. It identifies perception as an adaptation for accurately portraying aspects of the world-in-itself and describes a mechanism for improving this perceptual accuracy over evolutionary time. In other words, the world-in-itself and animals’ perceptions of it must share much the same logical structure.

Nature's many domains such as life may be viewed, within the framework of inferential systems, as instances where autopoietic models, such as genomes, transform into existing entities such as organisms. Here the map transforms into the territory, revealing both as composed of the same logical structure. This transformative relationship allows scientific models to extend natural models and transform them into natural processes wielding tremendous powers. How could this kind of power be possible unless the underlying scientific models are, to a large extent, true?

A favourite, perhaps an apocryphal example, involves one of the first physicists to understand the process of nucleosynthesis within stars. While stargazing with a girlfriend, he bragged that he was the only person who truly understood why stars shone[1]. His brag implied a close correspondence between the scientific model he had discovered and stellar things-in-themselves, a correspondence borne out by utilizing his newly discovered model as a recipe for building thermonuclear bombs a few decades later. When carefully followed, this scientific model transforms into mini stars here on earth. How can models that prove so powerful fail to be faithful representations or share the same logical structure as the things-in-themselves?

The development of Covid-19 vaccinations provides a further example illustrating convergence between scientific models and the world-in-itself. The virus’ genetic code, published just weeks after it became a concern in China, stimulated a few medical labs to construct computer models of those genetic sequences expressing proteins on the virus’ surface. When these models are transformed into mRNA and injected into people, they alert the body's immune system, just as would an actual infection, ramping up antibodies capable of defeating future viral infections. This transformation from scientific model to immune system stimulation causes the vaccine's effectiveness and indicates convergence between scientific models and the world in itself.

As a final and perhaps decisive example of the convergence between scientific models and the world-in-itself, we consider computation's power to model natural processes. The Church-Turing thesis states that a Turing machine, the technical name for a classical computer having unlimited resources, can compute any computable function (13). As models describing natural systems are computable functions, we may say that a computational model is possible for every natural system. In other words, algorithms capturing scientific models of every natural system can simulate those natural systems. In turn, comparisons between these simulations and observation of natural systems become evidence updating the scientific models and algorithms to greater accuracy.  In this manner, scientific models and their simulations join in an inferential system that progressively bridges the chasm between scientific models and the world in itself.

The recent theoretical discovery of quantum computing has propelled this thesis into the Church – Turing - Deutsch principle (14). In noting a one-to-one correspondence between quantum phenomena and quantum computation and that all physical systems have a quantum description, this principle states that any physical system may be simulated to any degree of accuracy using a universal quantum computer. It is tantamount to claiming the world-in-itself, at the quantum level, is equivalent to a computational process, leaving little distinction between scientific models and the world-in-itself.

As illustrated by these examples, scientific understanding has evolved beyond mere descriptive models to provide models capable of duplicating or simulating nature’s many mechanisms and bringing entities to exist within the world in itself.  In other words, we can view the world in itself as essentially similar to our scientific models – resolving the Kantian dichotomy.

Scientific models simulate both nature’s models or generalized genotypes and their resulting physical forms, or generalized phenotypes, existing as the world in itself. But science goes beyond simulating existing forms and can serve as a generalized genotype that brings new technologies into existence.

The world itself and its scientific models are vastly complex, hierarchical nestings of inferential systems; each system is engaged in a cyclical two-step inferential process, evolving and implementing knowledge for existence. These two steps are consequences of the free-energy principle, which states that systems maximize the evidence predicted by their models and thereby reduce the surprise they experience. Systems may do this in two ways:

1)  They may accurately follow their models’ knowledge (active inference), causing the world in itself to conform to their models.

2) They may update their models to greater accuracy using evidence of their existence (learning or evolution) within the world in itself, causing their models to conform to reality.

These two steps form cyclic inferential systems where knowledgeable models create and maintain the world in itself (e.g. genotypes create and maintain phenotypes), and the phenotype’s experience within the world in itself updates model knowledge (e.g. natural selection). This universal dualism explains the accumulation of knowledge in the universe and identifies science as a recent, powerful, but essentially natural method of knowledge accumulation. We suggest that in this manner, the chasm between scientific models and the natural world in itself is bridged.

Inferential systems provide a general scientific description of existence and an account of the world-in-itself, where each entity within the vast web of existence is inferentially engaged in exploring possibilities for existence. Here, the polar concepts of scientific description and the world-in-itself converge in the nascent field of Bayesian mechanics (421), which, like Newtonian mechanics, serves as a scientific description and an account of the world-in-itself. Science’s inferential nature provides optimal models, those sharing maximal logical structure with the vast inferential system composing the world-in-itself. In other words, science provides potent maps of the world’s territory because of its shared logical structure.

Part I of this book claimed that all forms of existence are examples of inferential systems. Part II explored and described inferential systems, and Part III interpreted modern scientific findings in the domains of cosmology, quantum phenomena, biology, neural-based behaviour and culture as inferential systems. Almost certainly, many of this account’s details are incorrect but more accurate, universalist explanations of existence may soon emerge because many convergent frameworks are under development. And we may also have confidence that this eventual explanation will describe existence in terms of knowledge and knowledge in terms of inferential processes.

 

References

1. Wooten, David. The invention of science: A new history of the scientific revolution. s.l. : Harper Perennial; Reprint edition (December 13, 2016), 2016. ISBN-10: 0061759538.

2. Wikipedia. Francis Bacon. Wikipedia. [Online] [Cited: September 26, 2021.] https://en.wikipedia.org/wiki/Francis_Bacon.

3. —. Sagan standard. Wikipedia. [Online] [Cited: July 11, 2021.] https://en.wikipedia.org/wiki/Sagan_standard.

4. —. David Hume. Wikipedia. [Online] [Cited: November 22, 2020.] https://en.wikipedia.org/wiki/David_Hume.

5. Kant, Immanuel. Prolegomena to Any Future Metaphysics. 1783.

6. Wikipedia. Immanuel Kant. Wikipedia. [Online] [Cited: February 14, 2021.] https://en.wikipedia.org/wiki/Immanuel_Kant.

7. —. Ludwig Wittgenstein. Wikipedia. [Online] [Cited: Februarfy 15, 2021.] https://en.wikipedia.org/wiki/Ludwig_Wittgenstein.

8. Wittgenstein, Ludwig. Tractatus Logico-Philosophicus. New York : [Reprinted, with a few corrections] Harcourt, Brace,, 1933.

9. Wikipedia. Nihlism. Wikipedia. [Online] [Cited: February 24, 2021.] https://en.wikipedia.org/wiki/Nihilism.

10. Huxley, Thomas Henry. Evidence as to Man's Place in Nature. s.l. : Williams & Norgate, 1863.

11. Darwin, Charles. The Origin of Species. sixth edition. New York : The New American Library - 1958, 1872. pp. 391 -392.

12. Korzybski, Alfred. Science and Sanity: An Introduction to Non-Aristotelian Systems and General Semantics. . s.l. : International Non-Aristotelian Library Publishing Company., 1933.

13. Wikipedia. Church-Turing thesis. Wikipedia. [Online] [Cited: February 23, 2021.] https://en.wikipedia.org/wiki/Church%E2%80%93Turing_thesis.

14. —. Church-Turing-Deutsch principle. Wikipedia. [Online] [Cited: February 23, 2021.] https://en.wikipedia.org/wiki/Church%E2%80%93Turing%E2%80%93Deutsch_principle.

 

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[1] Sometimes these eureka moment celebrations can be deflationary. The neuroscientist Geoffry Hinton (born 1947) is reported to have announced to his family that he believed he had finally figured out how the human brain worked. His fifteen-year-old daughter replied: ‘Oh Daddy, not again!’.

Inferential Systems and the Causal Revolution

John O. Campbell

This is an excerpt from the book: The Knowing Universe.

As we have seen, real-world inferential systems, such as genetically controlled metabolic pathways, are maestros of causal control. Their step-by-step instructions form causal cascades creating entities and regulating them within existence. ­­­But this description of inferential systems as causal agents is scientifically awkward. The concept of causation is taboo because data or evidence, on its own, can indicate correlations but not causal relationships. This problem of induction, first described by the philosopher David Hume (1711 – 1776) and since integrated into the scientific worldview, claims there is no logical basis for the notion of causation. As Wikipedia explains (1):

Hume argued that inductive reasoning and belief in causality cannot be justified rationally; instead, they result from custom and mental habit. We never actually perceive that one event causes another, but only experience the "constant conjunction" of events.

An oft-quoted example is that the rising sun and the crowing rooster are correlated, but the crowing does not cause the rising. Something beyond data or observations is required to establish causality.

A recent scientific innovation called the causal revolution, led by Judea Pearl (born 1936) (2; 3), clarifies the scientific understanding of causality and provides critical new understandings illuminating the autopoietic and algorithmic (AA) nature of inferential systems.  While the causal revolution provides new methods to science, these methods may have been previously discovered and implemented subconsciously during human evolution. Sometime about 70,000 years ago, a series of mutations in genes specifying our neural structures most probably resulted in a cognitive revolution powering our species’ cultural evolution and providing critical abilities enabling our (perhaps temporary) ascendancy over other biological forms. Some researchers, including Pearl, attribute this cognitive revolution to the emergence of unconscious abilities for causal reasoning, the same type of causal reasoning now brought to our conscious attention with Pearl’s causal revolution (4; 3; 5). 

 Figure 1: Judea Pearl

Other researchers have noted that each generation of children inherits a genetic propensity for causal reasoning. As Allison Gopnik (born 1955) writes (6):

In much causal reasoning research participants learn how a particular set of pre-selected variables produce a particular effect. Here, we investigate 3–5-year-olds’ ability to select the relevant variable for intervention in a novel causal system. Results demonstrate that even young children can learn which variable is causally relevant from sparse evidence.

In this view, the current scientific revolution is a mere rediscovery, in conscious terms, of a fitness-enhancing process placed long ago in our unconscious brains by natural selection.

Unfortunately, the current scientific causal revolution only extends to technical methods for discerning causal relationships among data. But, below we will argue that causal reasoning is a vital component of inferential systems and all existing forms. Within the context of inferential systems, we may say that, in general, knowledge is the cause of existence, and it is this causal mechanism that has brought nature’s many domains of reality into existence (7; 8). Generalized genotypes cause generalized phenotypes; in all domains, existence requires a mastery of causal reasoning.

For example, an organism’s (epi) genotype causes its phenotype. Although a gene’s expression also depends on environmental factors, the causal relationship is unmistakable. For instance, a specific genetic abnormality in fruit flies predictably causes legs to grow from the developing insect’s head in place of antennae (9). These type of  dramatic genetic defects clearly illustrates the general causal relationship between genotypes and phenotypes.

The causal revolution is a recent scientific discovery of a general law of nature underlying the many existing forms composing our universe, operating since the beginning, long before our species' evolution. In that sense, the causal revolution provides powerful tools for our scientific understanding of inferential systems and the widespread role of causality within natural systems.

Contrary to traditional statistics and the big data movement's mantra, that data is everything, and everything is data[1], Pearl points out that this perspective severely constrains the scope of statistics. Traditional statistics only view data as describing correlations, but correlations are not causes, as illustrated by the cliche of the crowing rooster and the rising sun. Pearl demonstrates that descriptions of causal relationships require an ingredient beyond data; they also require models that hypothesize causal relationships. As Pearl describes it (3):

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 models described by Pearl link his description of causal models to those of inferential systems. A generative model comprises a competing family of hypotheses explaining the generation or cause of some observable data. Thus, effective causal models must model how existing entities and processes are generated or brought into existence.

Pearl’s preferred types of causal models are causal diagrams depicting the variables involved as points and the cause-and-effect relationships between them as directed arrows serving 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 data or model evidence.

Pearl suggests that when the evidence does not fully support the hypothesis, we construct another, perhaps a more informed, hypothesis of the causal relationship and test its implications against the data. We should continue with these informed guesses until we discover a diagram consistent with all the data. As Pearl puts it (3):

If the data contradict this implication, then we need to revise our model.

We might note Pearl’s reference above to ‘cause and effect forces’ described by causal models. As noted in previous sections, system regulators employ causal forces to overcome obstacles to existence posed by nature’s laws. In this sense, inferential systems' knowledge initiates a causal chain of forces forming and maintaining systems within existence. It is in this manner that inferential systems achieve their autopoietic abilities.

Pearl provides a lovely metaphorical definition of 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 clear and accurate, the term ‘listens to’ may be a little anthropomorphic when applied to natural processes in general. 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 or state in response to that information.

Pearl’s definition of causality describes its role in inferential systems. The regulatory model of an inferential system receives information and performs updates in response to that information. The system’s sensed information causes optimal actions, and the model accumulates knowledge as it updates and learns from its actions. This regulatory knowledge exercises direct causal control over the system’s actions; the system only takes actions directed by the model. The sum effect of these actions predicts the creation and maintenance of the system within existence. In this sense, the model of the generalized genotype causes the existence of the generalized phenotype. In terms of Pearl’s definition, the generalized phenotype is caused by what it hears from the generalized genotype.

But within inferential systems, causation is a two-way street. Not only do generalized genotypes update or cause generalized phenotypes. But also, the evidence generated by generalized phenotypes updates or causes the knowledge of generalized genotypes. We may view the evidence as having a causal effect on models - each hypothesis's probability updates to consistency with the received evidence, and we may describe this update or response as a causal force; a model’s hypotheses are forced to new values by the evidence (10). Thus, the force of evidence shapes the model, or in Pearl’s terms, we may say that the generative model listens to the evidence of the phenotype and determines its value in response to what it hears.

This analogy between causation and force is widely accepted. As Wikipedia tells us (11):

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. 

Given the close analogy between causation, inference, and force, we may consider inferential systems a basic form of causation. Forces cause effects, and forces are always in terms of inferential systems’ updates. Steven Frank (born 1957) has demonstrated that inference may be considered the force of data applied to models (10). For example, fundamental physical forces occur when information carried by a gauge boson updates a quantum model. In Pearl’s terms, we might say that the quantum system listens to the gauge boson and determines or updates its value, such as the value of its momentum, in response to what it hears.

Although nature has always employed inferential systems as its primary engine of existence, the causal revolution rediscovers and adds this tool to the scientific toolkit. The causal revolution provides essential tools for describing autopoietic algorithmic (AA) aspects of inferential systems. In short, this new tool allows us to scientifically describe inferential systems as initiating a cascade of causes whose cumulative effects are existing systems. We can understand these orchestrated forces as forming regulated networks designed to overcome the many natural obstacles to existence.  

A breakthrough concept of the causal revolution is that a full scientific description of a causal process must include a causal model of the hypothesized causal cascade. Our understanding of inferential systems rests on this same concept that models must 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. Causal models are crucial components of existing systems; scientific explanations that do not include causal models can not account for their existence. For example, a scientific explanation of biology, not including the genetic model's role in initiating life’s causal cascade, could not accurately account for life's existence.

Another core component of the causal revolution’s understanding is that causation often involves interventions to the standard or spontaneous unfolding of events. A caused effect occurs when the normal range of possible outcomes is constrained to a few or a single caused outcome that without the imposed constraint might be very unlikely. We may view this imposed constraint as an intervention in the ordinary course of events (2). Evidence may cause AA models to predict or initiate a single outcome or effect; a sensed state regulates or causes a particular system response. The response is appropriate to the system's specific state and not one that would typically take place without regulation or constraints.

At the core of AA knowledge is its ability to cause outcomes, intervene in the spontaneous course of events and specify a single, otherwise improbable outcome. As a biological example, we might consider that knowledge in an organism’s genome causes specific outcomes or effects. A given gene may cause the production of an enzyme catalyzing a specific biochemical reaction, which is extremely unlikely to occur without the enzyme’s intervention. Genetic knowledge initiates causal cascades, resulting in specific effects under their AA models' strict causal control or regulation.

Pearl describes the causal revolution in terms of a ladder having three rungs describing causality. Correlations, described by the field of statistics, characterize the first rung. Interventions in the ordinary course of events characterize the second rung. And counterfactuals regarding what might be rather than what is, characterize the third. A dictionary example of a counterfactual hypothesis is (12):

If kangaroos had no tails, they would topple over.

Counterfactual hypotheses incorporated into generative models form the Peircean branch of logic called abduction, they provide inferential systems with evolutionary abilities because they generate novel experimental tests. Only counterfactual hypotheses can explore design space, searching for new forms of existence as only these hypotheses concern what does not yet exist.

In this sense, counterfactual hypotheses aren’t restricted to science inquiry but are part of nature’s toolkit for discovering new forms of existence. For example, Kangaroo genetics may pose counterfactual hypotheses in the form of mutations hypothesizing tailless kangaroos. These hypotheses predict that a kangaroo with no tail would be reproductively successful (it doesn’t always topple over). If the resulting tailless kangaroo achieved reproductive success, the counterfactual possibility would become actual, and tail-less kangaroos would come to exist in the actual world. In this sense evolution generates a succession of counterfactual hypotheses, some of which become established actualities, although many prove unable to achieve existence.

The causal revolution extends scientific understanding of causation beyond correlations to include causal models, interventions, and counterfactual hypotheses. We have examined examples illustrating how these extensions describe actual biological causal processes, and in Part III, we see that this understanding is also central to other domains of reality. In all domains, AA inferential systems cause themselves to exist.

However, the causal revolution's founders tend to view their revolution more as a revolution in scientific calculation than in understanding natural phenomena. Pearl, for example, champions the crucial understanding that causal models are essential to computing relationships between statistical variables. However, his revolution doesn’t extend to the existence of causal models in the natural world or their essential role in bringing actual phenomena into existence (3).

A common confusion when science first reveals new phenomena for which there is little direct observational evidence is to conclude that this new phenomenon doesn’t exist but is just a calculational shortcut. And this is quite reasonable when the discovery of the mathematical description precedes discovery of the physical phenomena. For example, in the late 1800s, many considered the controversial concept of atoms as shorthand for making scientific calculations rather than as actual phenomena. When Boltzmann tried to publish his work deriving thermodynamics from atomic theory, his journal editors insisted he refer to atoms as Bilder; merely as counterfactual models or pictures (13).

Again, in the early 1900s, with the rediscovery of Mendel’s concept of genes, most leading biologists regarded these not as physical entities but merely as a means of making calculations regarding phenotypic outcomes. As the philosopher of science David Hull (1935 – 2010) recounts (14):

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 with atoms and genetics, we may be confident that the causal revolution’s new calculational methods describe actual physical reality, that nature also uses this same logical reasoning[2] to create and regulate existence. Nature is a masterful orchestrator of causal cascades bringing specified entities into existence. Science has merely rediscovered nature’s methods, but, after all, that is the proper role of science.

References

1. Wikipedia. David Hume. Wikipedia. [Online] [Cited: November 22, 2020.] https://en.wikipedia.org/wiki/David_Hume.

2. Causal inference in statistics: an overview. Pearl, Judea. s.l. : Statistics Surveys, 2009, Vols. Volume 3 (2009), 96-146.

3. Pearl, Judea and Mackenzie, Dana. The Book of Why: The New Science of Cause and Effect. s.l. : Basic Books, 2018. ISBN-10: 046509760X.

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.

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7. 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.

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[1] If in doubt as to the widespread use of this meme, try googling it.

[2] We are reminded of Peirce’s prescience in understanding the rules of logic describing ‘right reasoning' and insisting that all processes in the universe follow these same rules of thought.