Abstract
Many have wondered how mathematics, which appears to be the result of both human creativity and human discovery, can possibly exhibit the degree of success and seemingly-universal applicability to quantifying the physical world as exemplified by the laws of physics. In this essay, I claim that much of the utility of mathematics arises from our choice of description of the physical world coupled with our desire to quantify it. This will be demonstrated in a practical sense by considering one of the most fundamental concepts of mathematics: additivity. This example will be used to show how many physical laws can be derived as constraint equations enforcing relevant symmetries in a sense that is far more fundamental than commonly appreciated.
Familiarity breeds the illusion of understanding
–Anonymous
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Notes
- 1.
In preparing for this essay, I was pleased to find that Hamming had posed a similar question: “I have tried, with little success, to get some of my friends to understand my amazement that the abstraction of integers for counting is both possible and useful. Is it not remarkable that 6 sheep plus 7 sheep make 13 sheep; that 6 stones plus 7 stones make 13 stones? Is it not a miracle that the universe is so constructed that such a simple abstraction as a number is possible? To me this is one of the strongest examples of the unreasonable effectiveness of mathematics. Indeed, l find it both strange and unexplainable” [17].
- 2.
Einstein expressed a similar sentiment about particle physics: “I would just like to know what an electron is.”
- 3.
This is especially difficult since additivity of set measure is assumed by mathematicians. That is where they decided to start. Where else would one start? This highlights one of the subtle and insidious difficulties faced by those in foundational studies. Once it is decided to adopt an assumption as a foundational construct it precludes the ability to delve deeper within that framework. Furthermore, it discourages others from doing so through what I call “The Curse of Familiarity” where one’s familiarity with a problem fosters an illusion of understanding that blinds one from seeing subtle clues, hints, connections and/or difficulties. The parable of Newton and the apple is an example where Newton momentarily saw through the familiarity of falling apples to realize a connection with the falling of the Moon about the Earth.
- 4.
Finite sets are sufficient for our purposes here as we are not attempting to model an infinite world.
- 5.
It would be better to call it a heterarchy since sets in general cannot be linearly ordered.
- 6.
Since the goal is to rank elements via quantification (mapping elements to a total order), it may be helpful to formalize the desired systematic preservation of inequality by making explicit the assumption of cancellativity where for disjoint elements x, y, and z, where \(v(x) \le v(y) \le v(z)\), we have \(v(x) \oplus v(z) \le v(y) \oplus v(z)\), which is implicit in any generally-useful (non-degenerate) notion of ranking.
- 7.
Do we select a particular concept of ordering? Of course we do; an example is given in Fig. 2 where the counting numbers are ordered in two different ways. Selecting one way of ordering gives you one set of laws (min and max) and selecting the other gives you another set of laws (GCD and LCM). The entire of field of number theory results as an attempt to study relationships between these two resulting sets of laws.
- 8.
The entropies appearing in the definition of mutual information derive from probabilities, yet no one insists that probability theory is an exotic form of information theory. Entropy and probability are related in a very specific way with probabilities being used to compute entropies. Similarly, quantum amplitudes are related to probabilities in a very specific way with amplitudes being used to compute probabilities via the Born Rule.
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Acknowledgments
I would like to thank John Skilling, David Hestenes, and Rob MacDuff for their insightful comments and ongoing discussions on foundations. I would also like to thank James Walsh for his careful proofreading, and Bertrand Carado and Yuchao Ma for their critical reading of the manuscript.
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Technical Endnotes
Technical Endnotes
This technical section provides the formal definitions of a partially-ordered set (poset) and a lattice as well as short derivation of the generalized sum rule for non-exclusive join.
A partially-ordered set (poset) P is a set of elements S along with a binary ordering relation, generically denoted \(\le \), postulated to have the following properties for elements \(x, y, z \subseteq P\)
A poset \(P = (S,\le )\) is referred to as a partially ordered set since not all elements are assumed to be comparable. That is, there may exist elements \(x, y \subseteq P\) where it is neither true that \(x \le y\) nor that \(y \le x\). In these situations, we say that x and y are incomparable, which is denoted \(x\, \Vert \, y\).
A lattice L is a poset where each pair of elements has a supremum or least upper bound (LUB) called the join, and an infimum or greatest lower bound (GLB) called the meet. The meet of two elements \(x,y \in L\) is denoted \(x \wedge y\) and the join is denoted \(x\vee y\). The meet and the join can be thought of as algebraic operators that take two lattice elements to a third lattice element. It is in this sense that every lattice is an algebra. The meet and join are assumed to obey the following relations
Lattice elements (and poset elements in general) can be quantified by assigning a real number (or more generally a set of real numbers) to each element. This is performed via a function v called a valuation, which takes each lattice element to a real number: \(v:x \in L \rightarrow \mathbb {R}\).
Valuations are meant to encode the ordering of elements in the lattice and this is accomplished by insisting that for \(x\le y\) we have that \(v(x) \le v(y)\). Furthermore, if the valuation is to encode the relationships among the elements, it must be that the valuation \(v(x \vee y)\) assigned to the join of two disjoint elements x and y (Fig. 3, left) can be expressed as a function of the valuations v(x) and v(y) assigned to those two elements. We write this as
where the operator \(\oplus \) is to be determined. The concept of a generally-useful quantification by valuation (non-degenerate ranking) implies that the operator \(\oplus \) obeys a cancellativity property for disjoint elements x, y, and z, where for \(v(x) \le v(y) \le v(z)\) we have \(v(x) \oplus v(z) \le v(y) \oplus v(z)\). Commutativity of the lattice join requires that the operator \(\oplus \) is commutative, and associativity of the lattice join requires that the operator \(\oplus \) is associative. The associative relationship represents a functional equation, known as the Associativity Equation, for the operator \(\oplus \), whose solution is known to be an invertible transform of additivity [1, 4, 10], which can be written as
where the function f is an arbitrary invertible function. In terms of the valuations this is
which is
This suggests that one can always choose a simpler quantification than the valuations v by instead assigning values u(x) defined by \(u(x) = f(v(x))\) so that \(\oplus \) transforms to simple addition for disjoint x and y:
Recall that this result holds only for disjoint elements (in a join semi-lattice). We now derive the result for two lattice elements in general. Consider the elements \(x \wedge y\) and z illustrated in Fig. 3 (right). Since their join is y, we have that
Next consider that elements x and z are disjoint and their join is \(x \vee y\). This allows us to write
Solving (A1) for u(z) and substituting into (A2) we have the Sum Rule
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Knuth, K.H. (2016). The Deeper Roles of Mathematics in Physical Laws. In: Aguirre, A., Foster, B., Merali, Z. (eds) Trick or Truth?. The Frontiers Collection. Springer, Cham. https://doi.org/10.1007/978-3-319-27495-9_7
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