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(*Notations, non-standard concepts, and definitions used commonly in these investigations are detailed in this post.*)

**Ferguson’s and Priest’s thesis**

In a brief, but provocative, *review* of what they term as “the enduring evolution of logic” over the ages, the authors of Oxford University Press’ recently released ‘*A Dictionary of Logic*‘, philosophers *Thomas Macaulay Ferguson* and *Graham Priest*, take to task what they view as a Kant-influenced manner in which logic is taught as a first course in most places in the world:

“… as usually ahistorical and somewhat dogmatic. This is what logic is; just learn the rules. It is as if Frege had brought down the tablets from Mount Sinai: the result is God-given, fixed, and unquestionable.”

Ferguson and Priest conclude their review by remarking that:

“Logic provides a theory, or set of theories, about what follows from what, and why. And like any theoretical inquiry, it has evolved, and will continue to do so. It will surely produce theories of greater depth, scope, subtlety, refinement—and maybe even truth.”

However, it is not obvious whether that is prescient optimism, or a tongue-in-cheek exit line!

** A nineteenth century parody of the struggle to define ‘truth’ objectively**

For, if anything, the developments in logic since around 1931 has—seemingly in gross violation of the hallowed principle of Ockham’s razor, and its crude, but highly effective, modern avatar KISS—indeed produced a plethora of theories of great depth, scope, subtlety, and refinement.

These, however, seem to have more in common with the, cynical, twentieth century emphasis on subjective, unverifiable, ‘truth’, rather than with the concept of an objective, evidence-based, ‘truth’ that centuries of philosophers and mathematicians strenuously struggled to differentiate and express.

A struggle reflected so eloquently in *this nineteenth century quote*:

“When I use a word,” Humpty Dumpty said, in rather a scornful tone, “it means just what I choose it to mean—neither more nor less.”

“The question is,” said Alice, “whether you can make words mean so many different things.”

“The question is,” said Humpty Dumpty, “which is to be master—that’s all.”

… Lewis Carroll (Charles L. Dodgson), ‘Through the Looking-Glass’, chapter 6, p. 205 (1934 ed.). First published in 1872.

**Making sense of mathematical propositions about infinite processes**

It was, indeed, an epic struggle which culminated in the nineteenth century standards of rigour successfully imposed—in no small measure by the works of Augustin-Louis Cauchy and Karl Weierstrasse—on verifiable interpretations of mathematical propositions about infinite processes involving real numbers.

A struggle, moreover, which should have culminated equally successfully in similar twentieth century standards—on verifiable interpretations of mathematical propositions containing references to infinite computations involving integers—sought to be imposed in 1936 by Alan Turing upon philosophical and mathematical discourse.

**The Liar paradox**

For it follows from Turing’s 1936 reasoning that where quantification is not, or cannot be, explicitly defined in formal logical terms—eg. the classical expression of the Liar paradox as ‘This sentence is a lie’—a paradox cannot per se be considered as posing serious linguistic or philosophical concerns (see, for instance, the series of four posts beginning *here*).

Of course—as reflected implicitly in Kurt Gödel’s seminal 1931 paper on undecidable arithmetical propositions—it would be a matter of serious concern if the word ‘This’ in the English language sentence, ‘This sentence is a lie’, could be validly viewed as implicitly implying that:

(i) there is a constructive infinite enumeration of English language sentences;

(ii) to each of which a truth-value can be constructively assigned by the rules of a two-valued logic; and,

(iii) in which ‘This’ refers uniquely to a particular sentence in the enumeration.

**Gödel’s influence on Turing’s reasoning**

However, Turing’s constructive perspective had the misfortune of being subverted by a knee-jerk, anti-establishment, culture that was—and apparently remains to this day—overwhelmed by Gödel’s powerful Platonic—and essentially unverifiable—mathematical and philosophical 1931 interpretation of his own construction of an arithmetical proposition that is formally unprovable, but undeniably true under any definition of ‘truth’ in any interpretation of arithmetic over the natural numbers.

Otherwise, I believe that Turing could easily have provided the necessary constructive interpretations of arithmetical truth—sought by David Hilbert for establishing the consistency of number theory finitarily—which is addressed by *the following paper* due to appear in the December 2016 issue of ‘*Cognitive Systems Research*‘:

**What is logic: using Ockham’s razor**

Moreover, the paper endorses the implicit orthodoxy of an Ockham’s razor influenced perspective—which Ferguson and Priest seemingly find wanting—that logic is simply a deterministic set of rules that must constructively assign the truth values of ‘truth/falsity’ to the sentences of a language.

It is a view that I expressed earlier as the key to a possible resolution of the *EPR paradox* in the *following paper* that I *presented* on 26’th June at the workshop on *Emergent Computational Logics* at UNILOG’2015, Istanbul, Turkey:

where I introduced the definition:

A finite set of rules is a Logic of a formal mathematical language if, and only if, constructively assigns unique truth-values:

(a) Of provability/unprovability to the formulas of ; and

(b) Of truth/falsity to the sentences of the Theory which is defined semantically by the -interpretation of over a structure .

I showed there that such a definitional rule-based approach to ‘logic’ and ‘truth’ allows us to:

Equate the provable formulas of the first order Peano Arithmetic PA with the PA formulas that can be evidenced as `true’ under an algorithmically computable interpretation of PA over the structure of the natural numbers;

Adequately represent some of the philosophically troubling abstractions of the physical sciences mathematically;

Interpret such representations unambiguously; and

Conclude further:

First that the concept of infinity is an emergent feature of any mechanical intelligence whose true arithmetical propositions are provable in the first-order Peano Arithmetic; and

Second that discovery and formulation of the laws of quantum physics lies within the algorithmically computable logic and reasoning of a mechanical intelligence whose logic is circumscribed by the first-order Peano Arithmetic.

(*Notations, non-standard concepts, and definitions used commonly in these investigations are detailed in this post.*)

**A Economist: The return of the machinery question**

In a Special Report on Artificial Intelligence in its issue of 25th June 2016, ‘*The return of the machinery question*‘, the Economist suggests that both cosmologist *Stephen Hawking* and enterpreneur *Elon Musk* share to some degree the:

“… fear that AI poses an existential threat to humanity, because superintelligent computers might not share mankind’s goals and could turn on their creators”.

**B Our irrational propensity to fear that which we are drawn to embrace**

Surprising, since I suspect both would readily agree that, if anything should scare us, it is our irrational propensity to fear that which we are drawn to embrace!

And therein should lie not only our comfort, but perhaps also our salvation.

For Artificial Intelligence is constrained by rationality; Human Intelligence is not.

An Artificial Intelligence must, whether individually or collectively, create and/or destroy only rationally. Humankind can and does, both individually and collectively, create and destroy irrationally.

**C Justifying irrationality**

For instance, as the legatees of logicians Kurt Goedel and Alfred Tarski have amply demonstrated, a Human Intelligence can easily be led to believe that some statements of even the simplest of mathematical languages—Arithmetic—must be both ‘formally undecidable’ and ‘true’, even in the absence of any objective yardstick for determining what is ‘true’!

**D Differentiating between Human reasoning and Mechanistic reasoning**

An Artificial Intelligence, however, can only treat as true that which can be proven—by its rules—to be true by an objective assignment of ‘truth’ and ‘provability’ values to the propositions of the language that formally expresses its mechanical operations—Arithmetic.

The implications of the difference are not obvious; but that the difference could be significant is the thesis of *this paper* which is due to appear in the December 2016 issue of Cognitive Systems Research:

‘*The Truth Assignments That Differentiate Human Reasoning From Mechanistic Reasoning*‘.

**E Respect for evidence-based ‘truth’ could be Darwinian**

More importantly, the paper demonstrates that both Human Intelligence—whose evolution is accepted as Darwinian—and Artificial Intelligence—whose evolution it is ‘feared’ may be Darwinian—share a common (Darwinian?) respect for an accountable concept of ‘truth’.

A respect that should make both Intelligences fitter to survive by recognising what philosopher *Christopher Mole* describes in *this invitational blogpost* as the:

“… importance of the rapport between an organism and its environment”

—an environment that can obviously accommodate the birth, and nurture the evolution, of both intelligences.

So, it may not be too far-fetched to conjecture that the evolution of both intelligences must also, then, share a Darwinian respect for the kind of human values—towards protecting intelligent life forms—that, no matter in how limited or flawed a guise, is visibly emerging as an inherent characteristic of a human evolution which, no matter what the cost could, albeit optimistically, be viewed as struggling to incrementally strengthen, and simultaneously integrate, individualism (fundamental particles) into nationalism (atoms) into multi-nationalism (molecules) and, possibly, into universalism (elements).

**F The larger question: Should we fear an extra-terrestrial Intelligence?**

From a broader perspective yet, our apprehensions about the evolution of a rampant Artificial Intelligence created by a Frankensteinian Human Intelligence should, perhaps, more rightly be addressed—as some have urged—within the larger uncertainty posed by SETI:

*Is there a rational danger to humankind in actively seeking an extra-terrestrial intelligence?*

I would argue that any answer would depend on how we articulate the question and that, in order to engage in a constructive and productive debate, we need to question—and reduce to a minimum—some of our most cherished mathematical and scientific beliefs and fears which cannot be communicated objectively.

(*Notations, non-standard concepts, and definitions used commonly in these investigations are detailed in this post.*)

**We investigate whether the probabilistic distribution of prime numbers can be treated as a heuristic model of quantum behaviour, since it too can be treated as a quantum phenomena, with a well-defined binomial probability function that is algorithmically computable, where the conjectured values of differ from actual values with a binomial standard deviation, and where we define a phenomena as a quantum phenomena if, and only if, it obeys laws that can only be represented mathematically by functions that are algorithmically verifiable, but not algorithmically computable.**

**1. Thesis: The concept of ‘mathematical truth’ must be accountable**

The thesis of this investigation is that a major philosophical challenge—which has so far inhibited a deeper understanding of the quantum behaviour reflected in the mathematical representation of some laws of nature (see, for instance, *this paper* by Eamonn Healey)—lies in holding to account the uncritical acceptance of propositions of a mathematical language as true under an interpretation—either axiomatically or on the basis of subjective self-evidence—without any specified methodology of accountability for objectively evidencing such acceptance.

**2. The concept of ‘set-theoretical truth’ is not accountable**

Since current folk lore is that all scientific truths can be expressed adequately, and communicated unambiguously, in the first order Set Theory ZF, and since the Axiom of Infinity of ZF cannot—even in principle—be objectively evidenced as true under any putative interpretation of ZF (as we argue in *this post*), an undesirable consequence of such an uncritical acceptance is that the distinction between the truths of mathematical propositions under interpretation which can be objectively evidenced, and those which cannot, is not evident.

**3. The significance of such accountability for mathematics**

The significance of such a distinction for mathematics is highlighted in this paper due to appear in the December 2016 issue of *Cognitive Systems Research*, where we address this challenge by considering the two finitarily accountable concepts of algorithmic verifiability and algorithmic computability (first introduced in *this paper* at the *Symposium on Computational Philosophy* at the AISB/IACAP World Congress 2012-Alan Turing 2012, Birmingham, UK).

**(i) Algorithmic verifiability**

A number-theoretical relation is algorithmically verifiable if, and only if, for any given natural number , there is an algorithm which can provide objective evidence for deciding the truth/falsity of each proposition in the finite sequence .

**(ii) Algorithmic computability**

A number theoretical relation is algorithmically computable if, and only if, there is an algorithm that can provide objective evidence for deciding the truth/falsity of each proposition in the denumerable sequence .

**(iii) Algorithmic verifiability vis à vis algorithmic computability**

We note that algorithmic computability implies the existence of an algorithm that can decide the truth/falsity of each proposition in a well-defined denumerable sequence of propositions, whereas algorithmic verifiability does not imply the existence of an algorithm that can decide the truth/falsity of each proposition in a well-defined denumerable sequence of propositions.

From the point of view of a finitary mathematical philosophy—which is the constraint within which an applied science ought to ideally operate—the significant difference between the two concepts could be expressed by saying that we may treat the decimal representation of a real number as corresponding to a physically measurable limit—and not only to a mathematically definable limit—if and only if such representation is definable by an algorithmically computable function (Thesis 1 on p.9 of *this paper* that was presented on 26th June at the workshop on *Emergent Computational Logics* at UNILOG’2015, 5th World Congress and School on Universal Logic, Istanbul, Turkey).

We note that although every algorithmically computable relation is algorithmically verifiable, the converse is not true.

We show in the CSR paper how such accountability helps define finitary truth assignments that differentiate human reasoning from mechanistic reasoning in arithmetic by identifying two, hitherto unsuspected, Tarskian interpretations of the first order Peano Arithmetic PA, under both of which the PA axioms interpret as finitarily true over the domain of the natural numbers, and the PA rules of inference preserve such truth finitarily over .

**4. The ambit of human reasoning vis à vis the ambit of mechanistic reasoning**

One corresponds to the classical, non-finitary, putative standard interpretation of PA over , and can be treated as circumscribing the ambit of human reasoning about ‘true’ arithmetical propositions.

The other corresponds to a finitary interpretation of PA over that circumscibes the ambit of mechanistic reasoning about ‘true’ arithmetical propositions, and establishes the long-sought for consistency of PA (see *this post*); which establishes PA as a mathematical language of unambiguous communication for the mathematical representation of physical phenomena.

**5. The significance of such accountability for the mathematical representation of physical phenomena**

The significance of such a distinction for the mathematical representation of physical phenomena is highlighted in *this paper* that was presented on 26th June at the workshop on *Emergent Computational Logics* at UNILOG’2015, 5th World Congress and School on Universal Logic, Istanbul, Turkey, where we showed how some of the seemingly paradoxical elements of quantum mechanics may resolve if we define:

**Quantum phenomena**: *A phenomena is a quantum phenomena if, and only if, it obeys laws that can only be represented mathematically by functions that are algorithmically verifiable but not algorithmically computable.*

**6. The mathematical representation of quantum phenomena that is determinate but not predictable**

By considering the properties of Gödel’s function (see 4.1 on p.8 of *this preprint*)—which allows us to strongly represent any non-terminating sequence of natural numbers by an arithmetical function—it would follow that, since any projection of the future values of a quantum-phenomena-associated, algorithmically verifiable, function is consistent with an infinity of algorithmically computable functions, all of whose past values are identical to the algorithmically verifiable past values of the function, the phenomena itself would be essentially unpredicatable if it cannot be represented by an algorithmically computable function.

However, since the algorithmic verifiability of any quantum phenomena shows that it is mathematically determinate, it follows that the physical phenomena itself must observe determinate laws.

**7. Such representation does not need to admit multiverses**

Hence (contrary to any interpretation that admits unverifiable multiverses) only one algorithmically computable extension of the function is consistent with the law determining the behaviour of the phenomena, and each possible extension must therefore be associated with a probability that the next observation of the phenomena is described by that particular extension.

**8. Is the probability of the future behaviour of quantum phenomena definable by an algorithmically computable function?**

The question arises: Although we cannot represent quantum phenomena explicitly by an algorithmically computable function, does the phenomena lend itself to an algorithmically computable probability of its future behaviour in the above sense?

**9. Can primes yield a heuristic model of quantum behaviour?**

We now show that the distribution of prime numbers denoted by the arithmetical prime counting function is a quantum phenomena in the above sense, with a well-defined probability function that is algorithmically computable.

**10. Two prime probabilities**

We consider the two probabilities:

(i) The probability of selecting a number that has the property of being prime from a given set of numbers;

*Example 1*: I have a bag containing numbers in which there are twice as many composites as primes. What is the probability that the first number you blindly pick from it is a prime. This is the basis for setting odds in games such as roulette.

(ii) The probability of determining a proper factor of a given number .

*Example 2*: I give you a -digit combination lock along with a -digit number . The lock only opens if you set the combination to a proper factor of which is greater than . What is the probability that the first combination you try will open the lock. This is the basis for RSA encryption, which provides the cryptosystem used by many banks for securing their communications.

**11. The probability of a randomly chosen number from the set of natural numbers is not definable**

Clearly the probability of selecting a number that has the property of being prime from a given set of numbers is definable if the precise proportion of primes to non-primes in is definable.

However if is the set of all integers, and we cannot define a precise ratio of primes to composites in , but only an order of magnitude such as , then equally obviously cannot be defined in (see Chapter 2, p.9, Theorem 2.1, *here*).

**12. The prime divisors of a natural number are independent**

Now, the following paper proves , since it shows that whether or not a prime divides a given integer is independent of whether or not a prime divides :

*Why Integer Factorising cannot be polynomial time*

We thus have that , with a binomial standard deviation.

Hence, even though we cannot define the probability of selecting a number from the set of all natural numbers that has the property of being prime, can be treated as the putative non-heuristic probability that a given is a prime.

**13. The distribution of primes is a quantum phenomena**

The distribution of primes is thus determinate but unpredictable, since it is representable by the algorithmically verifiable but not algorithmically computable arithmetical number-theoretic function , where is the ‘th prime.

The Prime Number Generating Theorem and the Trim and Compact algorithms detailed in *this 1964 investigation* illustrate why the arithmetical number-theoretic function is algorithmically verifiable but not algorithmically computable (see also *this Wikipedia proof* that no non-constant polynomial function with integer coefficients exists that evaluates to a prime number for all integers .).

Moreover, although the distribution of primes is a quantum phenomena with probabilty , it is easily seen (see Figs. 7-11 on pp.23-26 of *this preprint*) that the generation of the primes is algorithmically computable.

**14. Why the universe may be algorithmically computable**

By analogy, this suggests that although the measurable values of some individual properties of particles in the universe over time may represent a quantum phenomena, the universe itself may be algorithmically computable if the laws governing the generation of all the particles in the universe over time are algorithmically computable.

1. Since, by the Prime Number Theorem, the number of primes is , it would follow that determining a factor of requires at least one logical operation for each prime , and therefore cannot be done in polynomial time—whence —** IF** whether or not a prime divides an integer were independent of whether or not a prime divides the integer .

2. Currently, conventional approaches to determining the computational complexity of *Integer Factorising* apparently appeal critically to the belief that:

(i) either—explicitly (see *here*)—that whether or not a prime divides an integer is ** not** independent of whether or not a prime divides the integer ;

(ii) or—implicitly (since the problem is yet open)—that a proof to the contrary must imply that if is the probability that is a prime, then .

3. If so, then conventional approaches seem to conflate the two probabilities:

(i) The probability of *selecting* a number that has the property of being prime from a *given* set of numbers;

*Example 1*: I have a bag containing numbers in which there are twice as many composites as primes. What is the probability that the first number you blindly pick from it is a prime. This is the basis for setting odds in games such as roulette.

(ii) The probability of *determining* that a *given* integer is prime.

*Example 2*: I give you a -digit combination lock along with a -digit number . The lock only opens if you set the combination to a proper factor of which is greater than . What is the probability that the first combination you try will open the lock. This is the basis for RSA encryption, which provides the cryptosystem used by many banks for securing their communications.

4. In case 3(i), if the precise proportion of primes to non-primes in is definable, then clearly too is definable.

However if is the set of all integers, and we cannot define a precise ratio of primes to composites in , but only an order of magnitude such as , then equally obviously cannot be defined in (see Chapter 2, p.9, Theorem 2.1, *here*).

5. In case 3(ii) the following paper proves , since it shows that whether or not a prime divides a given integer ** is** independent of whether or not a prime divides :

*Why Integer Factorising cannot be polynomial time*

Not only does it immediately follow that (see *here*), but we further have that , with a binomial standard deviation. Hence, even though we cannot define the probability of selecting a number from the set of all natural numbers that has the property of being prime, can be treated as the de facto probability that a given is prime, with all its attended consequences for various prime-counting functions and the Riemann Zeta function (see *here*).

**A. A mathematical physicist’s conception of thinking about infinity in consistent ways**

John Baez is a mathematical physicist, currently working at the math department at U. C. Riverside in California, and also at the Centre for Quantum Technologies in Singapore.

Baez is not only academically active in the areas of network theory and information theory, but also socially active in promoting and supporting the Azimuth Project, which is a platform for scientists, engineers and mathematicians to collaboratively do something about the global ecological crisis.

In a recent post—*Large Countable Ordinals (Part 1)*—on the Azimuth Blog, Baez confesses to a passionate urge to write a series of blogs—that might even eventually yield a book—about the infinite, reflecting both his fascination with, and frustration at, the challenges involved in formally denoting and talking meaningfully about *different sizes of infinity*:

“I love the infinite. … It may not exist in the physical world, but we can set up rules to think about it in consistent ways, and then it’s a helpful concept. … Cantor’s realization that there are different sizes of infinity is … part of the everyday bread and butter of mathematics.”

**B. Why thinking about infinity in a consistent way must be constrained by an objective, evidence-based, perspective**

I would cautiously submit however that (as I briefly argue in this blogpost), before committing to any such venture, whether we *can* think about the “*different sizes of infinity*” in “*consistent ways*“, and to what extent such a concept is “*helpful*“, are issues that may need to be addressed from an objective, evidence-based, computational perspective in addition to the conventional self-evident, intuition-based, classical perspective towards formal axiomatic theories.

**C. Why we cannot conflate the behaviour of Goodstein’s sequence in Arithmetic with its behaviour in Set Theory**

Let me suggest why by briefly reviewing—albeit unusually—the usual argument of Goodstein’s Theorem (see here) that every Goodstein sequence over the natural numbers must terminate finitely.

**1. The Goodstein sequence over the natural numbers**

First, let , be the terms of the Goodstein sequence for over the domain of the natural numbers, where is the base in which the hereditary representation of the ‘th term of the sequence is expressed.

**Some properties of Goodstein’s sequence over the natural numbers**

We note that, for any natural number , R. L. Goodstein uses the properties of the hereditary representation of to construct a sequence of natural numbers by an unusual, but valid, algorithm.

**Hereditary representation**: The representation of a number as a sum of powers of a base , followed by expression of each of the exponents as a sum of powers of , etc., until the process stops. For example, we may express the hereditary representations of in base and base as follows:

We shall ignore the peculiar manner of constructing the individual members of the Goodstein sequence, since these are not germane to understanding the essence of Goodstein’s argument. We need simply accept for now that is well-defined over the structure of the natural numbers, and has, for instance, the following properties:

If we replace the base in each term of the sequence by , we arrive at a corresponding sequence of, say, Goodstein’s functions for over the domain of the natural numbers.

Where, for instance:

It is fairly straightforward (see here) to show that, for all :

Either , or .

Clearly terminates in if, and only if, there is a natural number such that, for any , we have either that or that .

However, since we cannot, equally clearly, *immediately* conclude from the axioms of the first-order Peano Arithmetic PA that such a must exist merely from the definition of the sequence in , we cannot *immediately* conclude from the above argument that must terminate finitely in .

**2. The Goodstein sequence over the finite ordinal numbers**

Second, let , be the terms of the Goodstein sequence over the domain of the finite ordinal numbers , where is Cantor’s least transfinite ordinal.

If we replace the base in each term of the sequence by , where ranges over all ordinals upto , it is again fairly straightforward to show that:

Either , or .

Clearly, in this case too, terminates in if, and only if, there is an ordinal such that, for all finite , we have either that , or that .

**3. Goodstein’s argument over the transfinite ordinal numbers**

If we, however, let then—since the ZF axioms do not admit an infinite descending set of ordinals—it now ** immediately follows** that we cannot have:

for all .

Hence must terminate finitely in , since we must have that for some finite .

**4. The intuitive justification for Goodstein’s Theorem**

The *intuitive* justification—which must *implicitly* underlie any formal argument—for Goodstein’s Theorem then is that, since the finite ordinals can be meta-mathematically * seen* to be in a correspondence with the natural numbers, we can conclude from (2) above that every Goodstein sequence over the natural numbers

*must also*terminate finitely.

**5. The fallacy in Goodstein’s argument**

The fallacy in this conclusion is exposed if we note that, by (2), must terminate finitely in **even if*** did not* terminate in !

**6. Why we need to heed Skolem’s cautionary remarks**

Clearly, if we heed Skolem’s cautionary remarks (reproduced here) about unrestrictedly corresponding conclusions concerning elements of different formal systems, then we can validly only conclude that the relationship of ‘terminating finitely’ with respect to the ordinal inequality ‘‘ over an infinite set of finite ordinals in any *putative* interpretation of a first order Ordinal Arithmetic cannot be obviously corresponded to the relationship of ‘terminating finitely’ with respect to the natural number inequality ‘‘ over an infinite set of natural numbers in any interpretation of PA.

**7. The significance of Skolem’s qualification**

The significance of Skolem’s qualification is highlighted if we note that we cannot force PA to admit a constant denoting a ‘completed infinity’, such as Cantor’s least ordinal , into either PA or into any interpretation of PA without inviting inconsistency.

(The proof is detailed in Theorem 4.1 on p.7 of this preprint. See also this blogpage).

**8. PA is finitarily consistent**

Moreover, the following paper, due to appear in the December 2016 issue of *Cognitive Systems Research*, gives a finitary proof of consistency for the first-order Peano Arithmetic PA:

**9. Why ZF cannot have an evidence-based interpretation**

It also follows from the above-cited CSR paper that ZF axiomatically postulates the existence of an infinite set which cannot be evidenced as true even under any *putative* interpretation of ZF.

**10. The appropriate conclusion of Goodstein’s argument**

So, if a ‘completed infinity’ cannot be introduced as a constant into PA, or as an element into the domain of any interpretation of PA, without inviting inconsistency, it would follow in Russell’s colourful phraseology that the appropriate conclusion to be drawn from Goodstein’s argument is that:

(i) In the first-order Peano Arithmetic PA we always know what we are talking about, even though we may not always know whether it is true or not;

(ii) In the first-order Set Theory we never know what we are talking about, so the question of whether or not it is true is only of notional interest.

Which raises the issue not only of whether we *can* think about the *different sizes of infinity* in a consistent way, but also to what extent we may need to justify that such a concept is *helpful* to an emerging student of mathematics.

**The Unexplained Intellect: Complexity, Time, and the Metaphysics of Embodied Thought**

*Christopher Mole* is an associate professor of philosophy at the University of British Columbia, Vancouver. He is the author of *Attention is Cognitive Unison: An Essay in Philosophical Psychology* (OUP, 2011), and *The Unexplained Intellect: Complexity, Time, and the Metaphysics of Embodied Thought* (Routledge, 2016).

In his preface to *The Unexplained Intellect*, Mole emphasises that his book is an attempt to provide arguments for (amongst others) the three theses that:

(i) “Intelligence might become explicable if we treat intelligence thought as if it were some sort of computation”;

(ii) “The importance of the rapport between an organism and its environment must be understood from a broadly computational perspective”;

(iii) “ our difficulties in accounting for our psychological orientation with respect to time are indications of the need to shift our philosophical focus away from mental *states*—which are altogether too static—and towards a theory of the mind in which it is *dynamic* mental entities that are taken to be metaphysically foundational”.

Mole explains at length his main claims in *The Unexplained Intellect*—and the cause that those claims serve—in a lucid and penetrating, VI-part, series of invited posts in *The Brains blog* (a leading forum for work in the philosophy and science of mind that was founded in 2005 by *Gualtiero Piccinini*, and has been administered by *John Schwenkler* since late 2011).

In these posts, Mole seeks to make the following points.

**I: The Unexplained Intellect: The mind is not a hoard of sentences**

We do not currently have a satisfactory account of how minds could be had by material creatures. If such an account is to be given then every mental phenomenon will need to find a place within it. Many will be accounted for by relating them to other things that are mental, but there must come a point at which we break out of the mental domain, and account for some things that are mental by reference to some that are not. It is unclear where this break out point will be. In that sense it is unclear which mental entities are, metaphysically speaking, the most fundamental.

At some point in the twentieth century, philosophers fell into the habit of writing as if the most fundamental things in the mental domain are mental states (where these are thought of as states having objective features of the world as their truth-evaluable contents). This led to a picture in which the mind was regarded as something like a hoard of sentences. The philosophers and cognitive scientists who have operated with this picture have taken their job to be telling us what sort of content these mental sentences have, how that content is structured, how the sentences come to have it, how they get put into and taken out of storage, how they interact with one another, how they influence behaviour, and so on.

This emphasis on states has caused us to underestimate the importance of non-static mental entities, such as inferences, actions, and encounters with the world. If we take these dynamic entities to be among the most fundamental of the items in the mental domain, then — I argue — we can avoid a number of philosophical problems. Most importantly, we can avoid a picture in which intelligent thought would be beyond the capacities of any physically implementable system.

**II: The Unexplained Intellect: Computation and the explanation of intelligence**

A lot of philosophers think that consciousness is what makes the mind/body problem interesting, perhaps because they think that consciousness is the only part of that problem that remains wholly philosophical. Other aspects of the mind are taken to be explicable by scientific means, even if explanatorily adequate theories of them remain to be specified.

I’ll remind the reader of computability theory’s power, with a view to indicating how it is that the discoveries of theoretical computer scientists place constraints on our understanding of what intelligence is, and of how it is possible.

**III: The Unexplained Intellect: The importance of computability**

If we found that we had been conceiving of intelligence in such a way that intelligence could not be modelled by a Turing Machine, our response should not be to conclude that some alternative must be found to a ‘Classically Computational Theory of the Mind’. To think only that would be to underestimate the scope of the theory of computability. We should instead conclude that, on the conception in question, intelligence would (be) *absolutely* inexplicable. This need to avoid making intelligence inexplicable places constraints on our conception of what intelligence is.

**IV: The Unexplained Intellect: Consequences of imperfection**

The lesson to be drawn is that, if we think of intelligence as involving the maintenance of satisfiable beliefs, and if we think of our beliefs as corresponding to a set of representational states, then our intelligence would depend on a run of good luck the chances of which are unknown.

My suggestion is that we can reach a more explanatorily satisfactory conception of intelligence if we adopt a dynamic picture of the mind’s metaphysical foundations.

**V: The Unexplained Intellect: The importance of rapport**

I suggest that something roughly similar is true of us. We are not guaranteed to have satisfiable beliefs, and sometimes we are rather bad at avoiding unsatisfiability, but such intelligence as we have is to be explained by reference to the rapport between our minds and the world.

Rather than starting from a set of belief states, and then supposing that there is some internal process operating on these states that enables us to update our beliefs rationally, we should start out by accounting for the dynamic processes through which the world is epistemically encountered. Much as the three-colourable map generator reliably produces three-colourable maps because it is essential to his map-making procedure that borders appear only where they will allow for three colorability, so it is essential to what it is for a state to be a belief that beliefs will appear only if there is some rapport between the believer and the world. And this rapport — rather than any internal processing considered in isolation from it — can explain the tendency for our beliefs to respect the demands of intelligence.

**VI: The Unexplained Intellect: The mind’s dynamic foundations**

memory is essentially a form of epistemic retentiveness: One’s present knowledge counts as an instance of memory when and only when it was attained on the basis of an epistemic encounter that lies in one’s past. One can epistemically encounter a *proposition* as the conclusion of an argument, and so can encounter it before the occurrence of any event to which it pertains, but one cannot encounter an *event* in that way. In the resulting explanation of memory’s temporal asymmetry, it is the dynamic events of epistemic encountering to which we must make reference. These encounters, and not the knowledge states to which they lead, do the lion’s share of the explanatory work.

**A: Simplifying Mole’s perspective**

It may help simplify Mole’s thought-provoking perspective if we make an arbitrary distinction between:

(i) The mind of an applied scientist, whose primary concern is our sensory observations of a ‘common’ external world;

(ii) The mind of a philosopher, whose primary concern is abstracting a coherent perspective of the external world from our sensory observations; and

(iii) The mind of a mathematician, whose primary concern is adequately expressing such abstractions in a formal language of unambiguous communication.

My understanding of Mole’s thesis, then, is that:

(a) although a mathematician’s mind may be capable of defining the ‘truth’ value of some logical and mathematical propositions without reference to the external world,

(b) the ‘truth’ value of any logical or mathematical proposition that purports to represent any aspect of the real world must be capable of being evidenced objectively to the mind of an applied scientist; and that,

(c) of the latter ‘truths’, what should interest the mind of a philosopher is whether there are some that are ‘knowable’ completely independently of the passage of time, and some that are ‘knowable’ only partially, or incrementally, with the passage of time.

**B. Support for Mole’s thesis**

It also seems to me that Mole’s thesis implicitly subsumes, or at the very least echoes, the belief expressed by Chetan R. Murthy (‘An Evaluation Semantics for Classical Proofs‘, Proceedings of Sixth IEEE Symposium on Logic in Computer Science, pp. 96-109, 1991; also Cornell TR 91-1213):

“It is by now folklore … that one can view the values of a simple functional language as specifying evidence for propositions in a constructive logic …”

If so, the thesis seems significantly supported by the following paper that is due to appear in the December 2016 issue of ‘Cognitive Systems Research’:

The CSR paper implicitly suggests that there are, indeed, (only?) two ways of assigning ‘true’ or ‘false’ values to any mathematical description of real-world events.

**C. Algorithmic computability**

First, a number theoretical relation is algorithmically computable if, and only if, there is an algorithm that can provide objective evidence (cf. ibid Murthy 91) for deciding the truth/falsity of each proposition in the denumerable sequence .

(We note that the concept of `algorithmic computability’ is essentially an expression of the more rigorously defined concept of `realizability’ on p.503 of Stephen Cole Kleene’s ‘*Introduction to Metamathematics*‘, North Holland Publishing Company, Amsterdam.)

**D. Algorithmic verifiability**

Second, a number-theoretical relation is algorithmically verifiable if, and only if, for any given natural number , there is an algorithm which can provide objective evidence for deciding the truth/falsity of each proposition in the finite sequence .

We note that algorithmic computability implies the existence of an algorithm that can finitarily decide the truth/falsity of each proposition in a well-defined denumerable sequence of propositions, whereas algorithmic verifiability does not imply the existence of an algorithm that can finitarily decide the truth/falsity of each proposition in a well-defined denumerable sequence of propositions.

The following theorem (Theorem 2.1, p.37 of the *CSR paper*) shows that although every algorithmically computable relation is algorithmically verifiable, the converse is not true:

**Theorem**: There are number theoretic functions that are algorithmically verifiable but not algorithmically computable.

**E. The significance of algorithmic ‘truth’ assignments for Mole’s theses**

The significance of such algorithmic ‘truth’ assignments for Mole’s theses is that:

*Algorithmic computability*—reflecting the ambit of classical Newtonian mechanics—characterises natural phenomena that are determinate and predictable.

Such phenomena are describable by mathematical propositions that can be termed as ‘knowable completely’, since at any point of time they are algorithmically computable as ‘true’ or ‘false’.

Hence both their past and future behaviour is completely computable, and their ‘truth’ values are therefore ‘knowable’ independent of the passage of time.

*Algorithmic verifiability*—reflecting the ambit of Quantum mechanics—characterises natural phenomena that are determinate but unpredictable.

Such phenomena are describable by mathematical propositions that can only be termed as ‘knowable incompletely’, since at any point of time they are only algorithmically verifiable, but not algorithmically computable, as ‘true’ or ‘false’

Hence, although their past behaviour is completely computable, their future behaviour is not completely predictable, and their ‘truth’ values are not independent of the passage of time.

**F. Where Mole’s implicit faith in the adequacy of set theoretical representations of natural phenomena may be misplaced**

It also seems to me that, although Mole’s analysis justifiably holds that the:

“ importance of the rapport between an organism and its environment”

has been underacknowledged, or even overlooked, by existing theories of the mind and intelligence, it does not seem to mistrust, and therefore ascribe such underacknowledgement to any lacuna in, the mathematical and epistemic foundations of the formal language in which almost all descriptions of real-world events are currently sought to be expressed, which is the language of the set theory ZF.

**G. Any claim to a physically manifestable ‘truth’ must be objectively accountable**

Now, so far as applied science is concerned, history teaches us that the ‘truth’ of any mathematical proposition that purports to represent any aspect of the external world must be capable of being evidenced objectively; and that such ‘truths’ must not be only of a subjective and/or revelationary nature which may require truth-certification by evolutionarily selected prophets.

(Not necessarily religious—see, for instance, Melvyn B. Nathanson’s remarks, “*Desperately Seeking Mathematical Truth*“, in the Opinion piece in the August 2008 Notices of the American Mathematical Society, Vol. 55, Issue 7.)

The broader significance of seeking objective accountability is that it admits the following (admittedly iconoclastic) distinction between the two fundamental mathematical languages:

1. The first-order Peano Arithmetic PA as the language of science; and

2. The first-order Set Theory ZF as the language of science fiction.

It is a distinction that is faintly reflected in Stephen G. Simpson’s more conservative perspective in his paper ‘*Partial Realizations of Hilbert’s Program*‘ (#6.4, p.15):

“Finitistic reasoning (my read: ‘First-order Peano Arithmetic PA’) is unique because of its clear real-world meaning and its indispensability for all scientific thought. Nonfinitistic reasoning (my read: ‘First-order Set Theory ZF’) can be accused of referring not to anything in reality but only to arbitrary mental constructions. Hence nonfinitistic mathematics can be accused of being not science but merely a mental game played for the amusement of mathematicians.”

The distinction is supported by the formal argument (detailed in the above-cited CSR paper) that:

(i) PA has two, hitherto unsuspected, evidence-based interpretations, the first of which can be treated as circumscribing the ambit of human reasoning about ‘true’ arithmetical propositions; and the second can be treated as circumscribing the ambit of mechanistic reasoning about ‘true’ arithmetical propositions.

What this means is that the language of arithmetic—formally expressed as PA—can provide all the foundational needs for all practical applications of mathematics in the physical sciences. This was was the point that I sought to make—in a limited way, with respect to quantum phenomena—in the following paper presented at Unilog 2015, Istanbul last year:

(Presented on 26’th June at the workshop on ‘*Emergent Computational Logics*’ at *UNILOG’2015, 5th World Congress and School on Universal Logic*, 20th June 2015 – 30th June 2015, Istanbul, Turkey.)

(ii) Since ZF axiomatically postulates the existence of an infinite set that cannot be evidenced (and which cannot be introduced as a constant into PA, or as an element into the domain of any interpretation of PA, without inviting inconsistency—see Theorem 1 in 4 of *this post*), it can have no evidence-based interpretation that could be treated as circumscribing the ambit of either human reasoning about ‘true’ set-theoretical propositions, or that of mechanistic reasoning about ‘true’ set-theoretical propositions.

The language of set theory—formally expressed as ZF—thus provides the foundation for abstract structures that—although of possible interest to philosophers of science—are only mentally conceivable by mathematicians subjectively, and have no verifiable physical counterparts, or immediately practical applications of mathematics, that can materially impact on the study of physical phenomena.

The significance of this distinction can be expressed more vividly in Russell’s phraseology as:

(iii) In the first-order Peano Arithmetic PA we always know what we are talking about, even though we may not always know whether it is true or not;

(iv) In the first-order Set Theory we never know what we are talking about, so the question of whether or not it is true is only of fictional interest.

**H. The importance of Mole’s ‘rapport’**

Accordingly, I see it as axiomatic that the relationship between an evidence-based mathematical language and the physical phenomena that it purports to describe, must be in what Mole terms as ‘rapport’, if we view mathematics as a set of linguistic tools that have evolved:

(a) to adequately abstract and precisely express through human reasoning our observations of physical phenomena in the world in which we live and work; and

(b) unambiguously communicate such abstractions and their expression to others through objectively evidenced reasoning in order to function to the maximum of our co-operative potential in acieving a better understanding of physical phenomena.

This is the perspective that I sought to make in the following paper presented at Epsilon 2015, Montpellier, last June, where I argue against the introduction of ‘unspecifiable’ elements (such as completed infinities) into either a formal language or any of its evidence-based interpretations (in support of the argument that since a completed infinity cannot be evidence-based, it must therefore be dispensible in any purported description of reality):

(Presented on 10th June at the Epsilon 2015 workshop on ‘*Hilbert’s Epsilon and Tau in Logic, Informatics and Linguistics*’, 10th June 2015 – 12th June 2015, University of Montpellier, France.)

**I. Why mathematical reasoning must reflect an ‘agnostic’ perspective**

Moreover, from a non-mathematician’s perspective, a Propertarian like *Curt Doolittle* would seem justified in his critique (comment of June 2, 2016 in *this Quanta review*) of the seemingly ‘mystical’ and ‘irrelevant’ direction in which conventional interpretations of Hilbert’s ‘theistic’ and Brouwer’s ‘atheistic’ reasoning appear to have pointed mainstream mathematics for, as I argue informally in an *earlier post*, the ‘truths’ of any mathematical reasoning must reflect an ‘agnostic’ perspective.

**A new proof?**

An interesting *review* by Natalie Wolchover on May 24, 2016, in the on-line magazine *Quanta*, reports that:

“With a surprising new proof, two young mathematicians have found a bridge across the finite-infinite divide, helping at the same time to map this strange boundary.

The boundary does not pass between some huge finite number and the next, infinitely large one. Rather, it separates two kinds of mathematical statements: ‘finitistic’ ones, which can be proved without invoking the concept of infinity, and ‘infinitistic’ ones, which rest on the assumption — not evident in nature — that infinite objects exist.”

More concretely:

“In the *new proof*, Keita Yokoyama, 34, a mathematician at the *Japan Advanced Institute of Science and Technology*, and Ludovic Patey, 27, a computer scientist from *Paris Diderot University*, pin down the logical strength of — but not at a level most people expected. The theorem is ostensibly a statement about infinite objects. And yet, Yokoyama and Patey found that it is ‘finitistically reducible’: It’s equivalent in strength to a system of logic that does not invoke infinity. This result means that the infinite apparatus in can be wielded to prove new facts in finitistic mathematics, forming a surprising bridge between the finite and the infinite.”

**The proof appeals to properties of transfinite ordinals**

My immediate reservation—after a brief glance at the formal definitions in 1.6 on p.6 of the *Yokoyama-Patey paper*—was that the domain of the structure in which the formal result is proved necessarily contains at least Cantor’s smallest transfinite ordinal , whereas the result is apparently sought to be ‘finitistically reducible’ (as considered by Stephen G. Simpson in an absorbing survey of *Partial Realizations of Hilbert’s Program*), in the sense of being not only finitarily provable, but interpretable in, and applicable to, finite structures (such as that of the natural numbers) whose domains may not contain (nor, in some cases, even admit—see Theorem 1 in 4.1 of *this post*) an infinite ‘number’.

Prima facie, the implicit assumption here (see also *this post*) seems to reflect, for instance, the conventional wisdom that every proposition which is formally provable about the finite, set-theoretically defined ordinals (necessarily assumed consistent with an axiom of infinity), must necessarily interpret as a true proposition about the natural numbers.

**Why we cannot ignore Skolem’s cautionary remarks**

In this conventional wisdom—by terming it as Skolem’s Paradox—both accepts and implicitly justifies ignoring Thoraf Skolem’s cautionary remarks about unrestrictedly corresponding putative mathematical relations and entities across domains of different axiom systems.

(*Thoralf Skolem*. 1922. *Some remarks on axiomatized set theory*. Text of an address delivered in Helsinki before the *Fifth Congress of Scandinavian Mathematicians*, 4-7 August 1922. In Jean van Heijenoort. 1967. Ed. *From Frege to Gödel: A source book in Mathematical Logic, 1878 – 1931*. Harvard University Press, Cambridge, Massachusetts.)

However, that the assumption is fragile is seen since, without such an assumption, we can only conclude from, say, Goodstein’s argument that a Goodstein sequence defined over the finite ZF ordinals must terminate finitely even if the corresponding Goodstein sequence over the natural numbers does not terminate (see Theorem 2 of this unpublished investigation)!

(*R. L. Goodstein*. 1944. *On the Restricted Ordinal Theorem*. In the *Journal of Symbolic Logic* 9, 33-41.)

**A remarkable exposition of Ramsey’s Theorem**

The Yokoyama-Patey proof invites other reservations too.

In a comment—remarkable for its clarity of exposition—academically minded ‘Peter’ illustrates Ramsey’s Theorem as follows:

Something that might help to understand what’s going on here is to start one level lower: Ramsey’s theorem for singletons () says that however you colour the integers with two colours (say red and blue), you are guaranteed to find an infinite monochromatic subset. To see this is true, simply go along the integers starting from and put them into the red or the blue bag according to their colour. Since in each step you increase the size of one or the other bag, without removing anything, you end up with an infinite set. This is a finitistic proof: it never really uses infinity, but it tells you how to construct the first part of the ‘infinite set’.

Now let’s try the standard proof for , pairs. This time we will go along the integers twice, and we will throw away a lot as we go.

The first time, we start at . Because there are infinitely many numbers bigger than , each of which makes a pair with and each of which pairs is coloured either red or blue, there are either infinitely many red pairs with or infinitely many blue pairs (note: this is really using ). I write down under ‘red’ or ‘blue’ depending on which it turned out to be (in case both sets of pairs are infinite, I’ll write red just to break a tie), then I cross out all the numbers bigger than which make the ‘wrong colour’ pair with .

Now I move on to the next number, say , I didn’t cross out, and I look at all the pairs it makes with the un-crossed-out numbers bigger than it. There are still infinitely many, so either the red pairs or the blue pairs form an infinite set (or both). I write down red or blue below as before, and again cross out all the number bigger than which make a wrong colour pair with . And I keep going like this; because everything stays infinite I never get stuck.

After an infinitely long time, I can go back and look at all the numbers which I did not cross out – there is an infinite list of them. Under each is written either ‘red’ or ‘blue’, and if under (say) number the word ‘red’ is written, then forms red pairs with all the un-crossed-out numbers bigger than . Now (using again) either the word ‘red’ or the word ‘blue’ was written infinitely often, so I can pick an infinite set of numbers under which I wrote either always ‘red’ or always ‘blue’. Suppose it was always ‘red’; then if and are any two numbers in the collection I picked, the pair will be red – this is because one of and , say , is smaller, and by construction all the pairs from to bigger un-crossed-out numbers, including , are red. If it were always blue, by the same argument I get an infinite set where all pairs are blue.

What is different here to the first case? The difference is that in order to say whether I should write ‘red’ or ‘blue’ under (or any other number) in the first step, I have to ‘see’ the whole infinite set. I could look at a lot of these numbers and make a guess – but if the guess turns out to be wrong then it means I made a mistake at all the later steps of the process too; everything falls apart. This is not a finitistic proof – according to some logicians, you should be worried that it might somehow be wrong. Most mathematicians will say it is perfectly fine though.

Moving up to , the usual proof is an argument that looks quite a lot like the argument, except that instead of using in the ‘first pass’ it uses . All fine; we believe , so no problem. But now, when you want to write down ‘red’ or ‘blue under in this ‘first pass’ you have to know something more complicated about all the triples using ; you want to know if you can find an infinite set such that any pair in forms a red triple with . If not, tells you that you can find an infinite set such that any pair in forms a _blue_ triple with . Then you would cross off everything not in , and keep going as with . The proof doesn’t really get any harder for the general case (or indeed changing the number of colours to something bigger than ). If you’re happy with infinity, there’s nothing new to see here. If not – well, these proofs have you recursively using more and infinitely more appeals to something infinite as you increase k, which is not a happy place to be in if you don’t like infinity.

**Implicit assumptions in Yokoyama-Patey’s argument**

Peter’s clarity of exposition makes it easier to see that, in order to support the conclusion that their proof of Ramsey’s Theorem for pairs is ‘finitistically reducible’, Yokoyama-Patey must assume:

(i) that ZFC is consistent, and therefore has a Tarskian interpretation in which the ‘truth’ of a ZFC formula can be evidenced;

(ii) that their result must be capable of an evidence-based Tarskian interpretation over the ‘finitist’ structure of the natural numbers.

As to (i), Peter has already pointed out in his final sentence that there are (serious?) reservations to accepting that the ZF axiom of infinity can have any evidence-based interpretation.

As to (ii), Ramsey’s Theorem is an existensial ZFC formula of the form (whose proof must appeal to an axiom of choice).

Now in ZF (as in any first-order theory that appeals to the standard first-order logic FOL) the formula is merely an abbreviation for the formula .

So, under any consistent ‘finitistically reducible’ interpretation of such a formula, there must be a unique, unequivocal, evidence-based Tarskian interpretation of over the domain of the natural numbers.

Now, if we are to avoid intuitionistic objections to the admitting of ‘unspecified’ natural numbers in the definition of quantification under any evidence-based Tarskian interpretation of a formal system of arithmetic, we are faced with the ambiguity where the questions arise:

(a) Is the to be interpreted constructively as:

For any natural number , there is an algorithm (say, a deterministic Turing machine) which evidences that are all true; or,

(b) is the formula to be interpreted finitarily as:

There is a single algorithm (say, a deterministic Turing machine) which evidences that, for any natural number is true, i.e., each of is true?

As Peter has pointed out in his analysis of Ramsey’s Theorem for pairs, the proof of the Theorem necessitates that:

“I have to ‘see’ the whole infinite set. I could look at a lot of these numbers and make a guess – but if the guess turns out to be wrong then it means I made a mistake at all the later steps of the process too; everything falls apart. This is not a finitistic proof – according to some logicians, you should be worried that it might somehow be wrong.”

In other words, Yokoyama-Patey’s conclusion (that their new proof is ‘finitistically reducible’) would only hold if they have established (b) somewhere in their proof; but a cursory reading of their paper does not suggest this to be the case.

In a recent paper *A Relatively Small Turing Machine Whose Behavior Is Independent of Set Theory*, authors Adam Yedidia and Scott Aaronson argue upfront in their Introduction that:

“*Like any axiomatic system capable of encoding arithmetic, ZFC is constrained by Gödel’s two incompleteness theorems. The first incompleteness theorem states that if ZFC is consistent (it never proves both a statement and its opposite), then ZFC cannot also be complete (able to prove every true statement). The second incompleteness theorem states that if ZFC is consistent, then ZFC cannot prove its own consistency. Because we have built modern mathematics on top of ZFC, we can reasonably be said to have assumed ZFC’s consistency.*“

The question arises:

*How reasonable is it to build modern mathematics on top of a Set Theory such as ZF?*

Some immediate points to ponder upon (see also reservations expressed by Stephen G. Simpson in *Logic and Mathematics* and in *Partial Realizations of Hilbert’s Program*):

**1. “Like any axiomatic system capable of encoding arithmetic, …”**

The implicit assumption here that every ZF formula which is provable about the finite ZF ordinals must necessarily interpret as a true proposition about the natural numbers is fragile since, without such an assumption, we can only conclude from Goodstein’s argument (see Theorem 1.1 here) that a Goodstein sequence defined over the finite ZF ordinals must terminate even if the corresponding Goodstein sequence over the natural numbers does not terminate!

**2. “ZFC is constrained by Gödel’s two incompleteness theorems. The first incompleteness theorem states that if ZFC is consistent (it never proves both a statement and its opposite), then ZFC cannot also be complete (able to prove every true statement). The second incompleteness theorem states that if ZFC is consistent, then ZFC cannot prove its own consistency.”**

The implicit assumption here is that ZF is -consistent, which implies that ZF is consistent and must therefore have an interpretation over some mathematically definable structure in which ZF theorems interpret as ‘true’.

The question arises: Must such ‘truth’ be capable of being evidenced objectively, or is it only of a subjective, revelationary, nature (which may require truth-certification by evolutionarily selected prophets—see Nathanson’s remarks as cited in *this post*)?

The significance of seeking objective accountbility is that in a paper, “*The Truth Assignments That Differentiate Human Reasoning From Mechanistic Reasoning: The Evidence-Based Argument for Lucas’ Gödelian Thesis*“, which is due to appear in the December 2016 issue of *Cognitive Systems Research*, we show (see also *this post*) that the first-order Peano Arithmetic PA:

(i) is finitarily consistent; but

(ii) is *not* -consistent; and

(iii) has no ‘undecidable’ arithmetical proposition (whence both of Gödel’s Incompleteness Theorems hold vacuously so far as the arithmetic of the natural numbers is concerned).

**3. “Because we have built modern mathematics on top of ZFC, we can reasonably be said to have assumed ZFC’s consistency.”**

Now, one justification for such an assumption (without which it may be difficult to justify building modern mathematics on top of ZF) could be the belief that acquisition of set-theoretical knowledge by students of mathematics has some essential educational dimension.

If so, one should take into account not only the motivations of such a student for the learning of mathematics, but also those of a mathematician for teaching it.

This, in turn, means that both the content of the mathematics which is to be learnt (or taught), as well as the putative utility of such learning (or teaching) for a student (or teacher), merit consideration.

Considering content, I would iconoclastically submit that the least one may then need to accomodate is the following distinction between the two fundamental mathematical languages:

1. The first-order Peano Arithmetic PA, which is the language of science; and

2. The first-order Set Theory ZF, which is the language of science fiction.

A distinction that is reflected in Stephen G. Simpson’s more conservative perspective in *Partial Realizations of Hilbert’s Program* (6.4, p.15):

Finitistic reasoning (*read ‘First-order Peano Arithmetic PA’*) is unique because of its clear real-world meaning and its indispensability for all scientific thought. Nonfinitistic reasoning (*read ‘First-order Set Thyeory ZF’*) can be accused of referring not to anything in reality but only to arbitrary mental constructions. Hence nonfinitistic mathematics can be accused of being not science but merely a mental game played for the amusement of mathematicians.

Reason:

(i) PA has two, hitherto unsuspected, evidence-based interpretations (see *this post*), the first of which can be treated as circumscribing the ambit of human reasoning about `true’ arithmetical propositions; and the second can be treated as circumscribing the ambit of mechanistic reasoning about `true’ arithmetical propositions.

It is this language of arithmetic—formally expressed as PA—that provides the foundation for all practical applications of mathematics where the latter could be argued as having an essential educational dimension.

(ii) Since ZF axiomatically postulates the existence of an infinite set that cannot be evidenced (and which cannot be introduced as a constant into PA, or as an element into the domain of any interpretation of PA, without inviting inconsistency—see paragraph 4.2 of *this post*), it can have no evidence-based interpretation that could be treated as circumscribing the ambit of either human reasoning about `true’ set-theoretical propositions, or that of mechanistic reasoning about `true’ set-theoretical propositions.

The language of set theory—formally expressed as ZF—thus provides the foundation for abstract structures that are only mentally conceivable by mathematicians (subjectively?), and have no physical counterparts, or immediately practical applications of mathematics, which could meaningfully be argued as having an essential educational dimension.

The significance of this distinction can be expressed more vividly in Russell’s phraseology as:

(iii) In the first-order Peano Arithmetic PA we always know what we are talking about, even though we may not always know whether it is true or not;

(iv) In the first-order Set Theory we never know what we are talking about, so the question of whether or not it is true is only of fictional interest.

The distinction is lost when—as seems to be the case currently—we treat the acquisition of mathematical knowledge as necessarily including the body of essentially set-theoretic theorems—to the detriment, I would argue, of the larger body of aspiring students of mathematics whose flagging interest in acquiring such a wider knowledge in universities around the world reflects the fact that, for most students, their interests seem to lie primarily in how a study of mathematics can enable them to:

(a) adequately abstract and precisely express through human reasoning their experiences of the world in which they live and work; and

(b) unambiguously communicate such abstractions and their expression to others through objectively evidenced reasoning in order to function to the maximum of their latent potential in acieving their personal real-world goals.

In other words, it is not obvious how how any study of mathematics that has the limited goals (a) and (b) can have any essentially educational dimension that justifies the assumption that ZF is consistent.

It is, indeed, gratifying that, after over 50 years of pursuing a path which challenged the accepted textbook wisdom that mathematical truth (which is the basis for asserting that any scientific proposition may be treated as true) is not definable objectively, my *contrary contention* has been accepted by the editors of the journal ‘*Cognitive Systems Research*‘ for publication in the December 2016 issue of the Journal. In a sense, this gives closure to the most challenging part of a journey which I have been privileged to afford and endure so far only because of the blessings, indulgence, and support provided by a generation of late elders (my teachers C. B. Nix James and Professor Manohar S. Huzurbazar, parents and mentors), contemporaries, and countless others who gave me the encouragement and strength to continue on such a nebulous path at crucial moments of my life. Defending the thesis promises to be as challenging—albeit far shorter—a journey!

In a paper *The Truth Assignments That Differentiate Human Reasoning From Mechanistic Reasoning: The Evidence-Based Argument for Lucas’ Gödelian Thesis*, due to appear in the December 2016 issue of **Cognitive Systems Research**, we briefly consider (*Anand [1]*) a philosophical challenge that arises when an intelligence—whether human or mechanistic—accepts arithmetical propositions as true under an interpretation—either axiomatically or on the basis of subjective self-evidence—*without* any specified methodology for evidencing such acceptance (for a brief, relatively recent, review of such challenges, see *Feferman [2], Feferman [3]*).

**The ambiguity in the standard interpretation of the Peano Arithmetic PA**

For instance conventional wisdom, whilst accepting Tarski’s classical definitions of the satisfiability and truth of the formulas of a formal language under an interpretation (*Anand [1]*, p.44), *postulates* that under the classical standard interpretation (we shall refer to this henceforth as ) of the first-order Peano Arithmetic PA (we take this to be the first-order theory defined in any standard text corresponding to the theory S in *Mendelson [4]*, p.102) over the domain of the natural numbers:

(i) The satisfiability/truth of the atomic formulas of PA can be assumed as *uniquely* decidable under ;

(ii) The PA axioms can be assumed to *uniquely* interpret as satisfied/true under ;

(iii) The PA rules of inference—Generalisation and Modus Ponens—can be assumed to *uniquely* preserve such satisfaction/truth under ;

(iv) Aristotle’s particularisation can be assumed to hold under .

We define Aristotle’s particularisation as the *non-finitary* assumption that an assertion such as, `There exists an such that holds’—usually denoted symbolically by `‘—can always be validly inferred in the classical logic of predicates from the assertion, `It is not the case that: for any given , does not hold’—usually denoted symbolically by `‘ (see also *Hilbert & Ackermann [5]*, pp.58-59).

We argue that the seemingly innocent and self-evident assumptions of *uniqueness* in (i) to (iii)—as also the seemingly innocent assumption in (iv) which, despite being obviously *non-finitary*, is unquestioningly accepted in classical literature as equally self-evident under any logically unexceptionable interpretation of the classical first-order logic FOL—conceal an ambiguity with far-reaching consequences.

**The two, hitherto unsuspected and essentially different, interpretations of PA**

The ambiguity is revealed if we note that Tarski’s classic definitions permit both human and mechanistic intelligences to admit *finitary* evidence-based definitions of the satisfaction and truth of the *atomic* formulas of PA over the domain of the natural numbers in two, hitherto unsuspected and essentially different, ways:

(1a) In terms of *classical* algorithmic verifiabilty; and

(1b) In terms of *finitary* algorithmic computability.

By ‘finitary’ we mean that (for a brief review of ‘finitism’ and ‘constructivity’ in the context of this paper see *Feferman [3]*):

“… there should be an algorithm for deciding the truth or falsity of any mathematical statement”

… http://en.wikipedia.org/wiki/Hilbert’s\_program.

We show that:

(2a) The two definitions correspond to two distinctly different assignments of satisfaction and truth to the *compound* formulas of PA over —say and (we shall refer to these henceforth as and respectively); where

(2b) The PA axioms are true over , and the PA rules of inference preserve truth over , under both and .

**A finitary proof of consistency for Arithmetic: The solution to Hilbert’s Second Millenium Problem**

We then show that:

(3a) If we assume the satisfaction and truth of the compound formulas of PA are always *non-finitarily* decidable under the assignment , then this assignment defines a *non-finitary* interpretation of PA in which Aristotle’s particularisation always holds over ; and which corresponds to the classical *non-finitary* standard interpretation of PA over the domain —from which only a human intelligence may *non-finitarily* conclude (as Gentzen’s argument does) that PA is consistent; whilst

(3b) The satisfaction and truth of the compound formulas of PA are always *finitarily* decidable under the assignment , which thus defines a *finitary* interpretation of PA—from which both intelligences may *finitarily* conclude that PA is consistent (as sought by David Hilbert for the second of the twenty three problems that he highlighted at the International Congress of Mathematicians in Paris in 1900; see *Hilbert [6]*).

**PA is categorical and has no non-standard models**

We show further that both intelligences would logically conclude that:

(4a) The assignment defines a subset of PA formulas that are algorithmically computable as true under the standard interpretation if, and only if, the formulas are PA provable;

(4b) PA is categorical (and so has no non-standard model, as argued in *Anand [7]*);

We note that the standard argument to the contrary—as detailed, for instance, in *Kaye [8]* (pp.10-11)—violates finitarity by adding a new constant to the language of PA that is not definable in and, ipso facto, by adding an atomic formula to PA whose satisfaction under any interpretation of PA is not algorithmically verifiable.

However, since the atomic formulas of PA are algorithmically verifiable under the standard interpretation (Theorem 5.1, p. 38, in *Anand [1]*), the above argument invalidly postulates precisely that which it seeks to prove (as also do arguments in: *Boolos, Burgess & Jeffrey [9]*, p.306, Corollary 25.3; *Luna [10]*, p.7)!

**There are no ‘undecidable’ arithmetical propositions: Gödel’s Theorems hold vacuously**

Both intelligences would also logically conclude that:

(4c) PA is not -consistent.

(5a) Since PA is not -consistent, Gödel’s argument in *Gödel [11]* (p.28(2))—that “ is not -PROVABLE”—does not yield a `formally undecidable proposition’ in PA;

The reason we prefer to consider Gödel’s original argument (rather than any of its subsequent avatars) is that, for a purist, Gödel’s remarkably self-contained 1931 paper—it neither contained, nor needed, any formal citations—remains unsurpassed in mathematical literature for thoroughness, clarity, transparency and soundness of exposition—*from first principles* (thus avoiding any implicit mathematical or philosophical assumptions)—of his notion of arithmetical `undecidability’ as based on his Theorems VI and XI and their logical consequences.

We also note that if PA is not -consistent, then Aristotle’s particularisation does not hold in any finitary interpretation of PA over .

Now, J. Barkeley Rosser’s ‘undecidable’ arithmetical proposition in *Rosser [12]* is of the form .

Thus his ‘extension’ of Gödel’s proof of undecidability too does not yield a ‘formally undecidable proposition’ in PA, since it implicitly presumes that Aristotle’s particularisation holds when interpreting under a finitary interpretation over (*Rosser [12]*, Theorem II, pp.233-234; *Kleene [13]*, Theorem 29, pp.208-209; *Mendelson [4]*, Proposition 3.32, pp.145-146).

(5b) The appropriate conclusion to be drawn from Gödel’s argument (in *Gödel [11]*, p.27(1))—that “ is not -PROVABLE”—is thus not that there is a ‘formally undecidable arithmetical proposition’ (see also *Feferman [4]* for an interesting perspective on how he—as well as, reportedly, both Gödel and Hilbert—informally viewed the concept of ‘formally undecidable arithmetical propositions’) but that any such putatively ‘undecidable arithmetical proposition’ is an instantiation of the argument (corresponding to Cantor’s diagonal argument and Turing’s halting argument) that we can define number-theoretic formulas which are algorithmically verifiable as always true, but not algorithmically computable as always true.

**The argument for Lucas’ Gödelian Thesis**

We conclude from this that Lucas’ Gödelian argument can validly claim:

**Thesis**: There can be no mechanist model of human reasoning if the assignment can be treated as circumscribing the ambit of human reasoning about ‘true’ arithmetical propositions, and the assignment can be treated as circumscribing the ambit of mechanistic reasoning about ‘true’ arithmetical propositions.

Although Lucas’ original 1961 thesis (*Lucas [14]*):

“… we cannot hope ever to produce a machine that will be able to do all that a mind can do: we can never not even in principle, have a mechanical model of the mind.”

deserves consideration that lies beyond the immediate scope of this investigation, we draw attention to his informal 1996 defence of it from a philosophical perspective in *Lucas [15]*, where he concludes with the argument that:

“Thus, though the Gödelian formula is not a very interesting formula to enunciate, the Gödelian argument argues strongly for creativity, first in ruling out any reductionist account of the mind that would show us to be, au fond, necessarily unoriginal automata, and secondly by proving that the conceptual space exists in which it is intelligible to speak of someone’s being creative, without having to hold that he must be either acting at random or else in accordance with an antecedently specifiable rule”.

**Argument**: Gödel has shown how to construct an arithmetical formula with a single variable—say (Gödel refers to this formula only by its Gödel number (*Gödel [11]*, p.25(12)))—such that is not PA-provable, but is instantiationally PA-provable for any given PA numeral .

Hence, for any given numeral , Gödel’s primitive recursive relation must hold for some natural number (where denotes Gödel’s primitive recursive relation ‘ is the Gödel-number of a proof sequence in PA whose last term is the PA formula with Gödel-number ‘ (*Gödel [11]*, p.22(45)); and denotes the Gödel-number of the PA formula ).

If we assume that any mechanical witness can only reason *finitarily* then although, for any given numeral , a mechanical witness can give evidence under the assignment that the PA formula holds in , no mechanical witness can conclude *finitarily* under the assignment that, for any given numeral , the PA formula holds in .

However, if we assume that a human witness can also reason *non-finitarily*, then a human witness *can* conclude under the assignment that, for any given numeral , the PA formula holds in .

**Bibliography**

[1] Bhupinder Singh Anand. 2016. *The Truth Assignments That Differentiate Human Reasoning From Mechanistic Reasoning: The Evidence-Based Argument for Lucas’ Gödelian Thesis*. To appear in *Cognitive Systems Research*. Volume 40, December 2016, Pages 35-45, doi:10.1016/j.cogsys.2016.02.004.

[2] Solomon Feferman. 2006. *Are There Absolutely Unsolvable Problems? Gödel’s Dichotomy*. In *Philosophia Mathematica* (2006) 14 (2): 134-152.

[3] Solomon Feferman. 2008. *Lieber Herr Bernays!, Lieber Herr Gödel! Gödel on finitism, constructivity and Hilbert’s program*. In the *Special Issue: Gödel’s dialectica Interpretation* of *Dialectica*, Volume 62, Issue 2, June 2008, pp. 245-290.

[4] Elliott Mendelson. 1964. *Introduction to Mathematical Logic*. Van Norstrand, Princeton.

[5] David Hilbert & Wilhelm Ackermann. 1928. *Principles of Mathematical Logic*. Translation of the second edition of the *Grundzüge Der Theoretischen Logik*. 1928. Springer, Berlin. 1950. Chelsea Publishing Company, New York.

[6] David Hilbert. 1900. *Mathematical Problems*. An address delivered before the *International Congress of Mathematicians* at Paris in 1900. Dr. Maby Winton Newson translated this address into English with the author’s permission for the *Bulletin of the American Mathematical Society*, 8 (1902), 437-479. An HTML version is accessible at http://aleph0.clarku.edu/~djoyce/hilbert/problems.html.

[7] Bhupinder Singh Anand. 2008. *Can we really falsify truth by dictat?*. In *The Reasoner*, Vol(2)1 pp. 7-8.

[8] Richard Kaye. 1991. *Models of Peano Arithmetic*. Oxford Logic Guides, 15. Oxford Science Publications. The Clarendon Press, Oxford University Press, New York, 1991.

[9] George S. Boolos, John P. Burgess, & Richard C. Jeffrey. 2003. *Computability and Logic* (4th ed). Cambridge University Press, Cambridge.

[10] Laureano Luna. 2008. *On non-standard models of Peano Arithmetic*. In *The Reasoner*, Vol(2)2 p. 7.

[11] Kurt Gödel. 1931. *On formally undecidable propositions of Principia Mathematica and related systems I*. Translated by Elliott Mendelson. In M. Davis (ed.). 1965. *The Undecidable*. Raven Press, New York.

[12] J. Barkley Rosser. 1936. *Extensions of some Theorems of Gödel and Church*. In M. Davis (ed.). 1965. *The Undecidable*. Raven Press, New York. Reprinted from *The Journal of Symbolic Logic*, Vol.1, pp.87-91.

[13] Stephen Cole Kleene. 1952. *Introduction to Metamathematics*. North Holland Publishing Company, Amsterdam.

[14] J. R. Lucas. 1961. *Minds, Machines and Gödel*. In *Philosophy*, XXXVI, 1961, pp.112-127; reprinted in *The Modeling of Mind*, Kenneth M.Sayre and Frederick J.Crosson, eds., Notre Dame Press, 1963, pp.269-270; and in *Minds and Machines*, ed. Alan Ross Anderson, Prentice-Hall, 1954, pp.43-59.

[15] J. R. Lucas. 1996. The Gödelian Argument: Turn Over the Page. A paper read at a BSPS conference in Oxford. Reproduced in 2003 as *Series/Report no.: Etica & Politica / Ethics & Politics V* (2003) 1, EUT Edizioni Università di Trieste, URI: http://hdl.handle.net/10077/5477.

*This argument laid the foundation for this investigation. See also this arXiv preprint, and this broader update.*

Abstract We define the residues for all and all such that if, and only if, is a divisor of . We then show that the joint probability of two unequal primes dividing any integer is the product . We conclude that the prime divisors of any integer are independent; and that the probability of being a prime is . The number of primes less than or equal to is thus given by . We further show that , and conclude that does not oscillate.

** The residues **

We begin by defining the residues for all and all as below:

**Definition 1:** where .

Since each residue cycles over the values , these values are all incongruent and form a complete system of residues ^{[1]} .

It immediately follows that:

**Lemma 1:** if, and only if, is a divisor of .

** The probability **

By the standard definition of the probability of an event , we conclude that:

**Lemma 2:** For any and any given integer , the probability that is , and the probability that is .

We note the standard definition:

**Definition 2:** Two events and are mutually independent for if, and only if, .

** The prime divisors of any integer are mutually independent**

We then have that:

**Lemma 3:** If and are two primes where then, for any , we have:

where and .

**Proof:** The numbers , where and , are all incongruent and form a complete system of residues ^{[2]} . Hence:

.

By Lemma 2:

.

The lemma follows.

If and in Lemma 3, so that both and are prime divisors of , we conclude by Definition 2 that:

**Corollary 1:** .

**Corollary 2:** .

**Theorem 1:** The prime divisors of any integer are mutually independent. ^{[3]}

** The probability that is a prime**

Since is a prime if, and only if, it is not divisible by any prime , it follows immediately from Lemma 2 and Lemma 3 that:

**Lemma 4:** For any , the probability of an integer being a prime is the probability that for any if .

**Lemma 5:** ^{[4]}.

** The Prime Number Theorem**

The number of primes less than or equal to is thus given by:

**Lemma 6:** .

This now yields the Prime Number Theorem:

**Theorem 2:** .

**Proof:** From Lemma 6 and Mertens’ Theorem that

^{[5]}:

it follows that:

The behaviour of as is then seen by differentiating the right hand side, where we note that :

Hence does not oscillate as .

**Acknowledgements**

I am indebted to my erstwhile classmate, Professor Chetan Mehta, for his unqualified encouragement and support for my scholarly pursuits over the past fifty years; most pertinently for his patiently critical insight into the required rigour without which the argument of this 1964 investigation would have remained in the informal universe of seemingly self-evident truths.

**References**

**HW60** G. H. Hardy and E. M. Wright. 1960. *An Introduction to the Theory of Numbers* 4th edition. Clarendon Press, Oxford.

**Ti51** E. C. Titchmarsh. 1951. *The Theory of the Riemann Zeta-Function*. Clarendon Press, Oxford.

**Notes**

Return to 1: HW60, p.49.

Return to 2: HW60, p.52, Theorem 59.

Return to 3: In the previous post we have shown how it immediately follows from Theorem 1 that integer factorising is necessarily of order ; from which we conclude that integer factorising cannot be in the class of polynomial-time algorithms.

Return to 4: By HW60, p.351, Theorem 429, Mertens’ Theorem.

Return to 5: By the argument in Ti51, p.59, eqn.(3.15.2).

Return to 6: HW60, p.9, Theorem 7.

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