Reason: Variability, Causality, Determinism, and Randomness
On the Conceptual Boundaries of Explanation and Understanding
Author's Preface
Once again, I opine on stuff that is beyond my pay grade.
Conceptual Problems with Variability, Causality, Determinism, and Randomness
There are some real conceptual problems, epistemic problems with the notions of variability, causality, determinism, and randomness. We quickly tie ourselves into metaphysical knots and babble—essentially talk nonsense—about these concepts as soon as we start to explore them.
The Metaphysical Mire of Determinism and Randomness
Well, I've tried to make sense of the words we use: determinism and randomness. And the further I pursue it, the more opaque the meaning becomes, until eventually I end up wading into the metaphysical mire.
Observing Variability and Mistaking Ignorance for Acausality
One obvious mistake—perhaps it's a category mistake or some other logical fallacy—is that we observe variability. We observe causality. We're wired to do that; all animals, at least above a certain level of complexity, are so architected. And sometimes we can't determine causality, so we call it random. But upon further exploration, we find there are causes that we can identify, so it's no longer random. So, random seems to be a statement about our ability or inability to understand causality.
Not that causality isn't a problematic notion itself, but we're wired to detect causality. And a pragmatist says there is causality. However, we go from the assertion that we can't determine causes to the assertion that there are no causes. It's randomness as uncaused events. Well, that's an unjustifiable leap.
Mathematical Descriptions of Randomness and Pseudorandomness
So, I've asserted that the idea of randomness is that there are uncaused events. But there are also mathematical descriptions of randomness, and I don't think they're any better. There's the mathematical description of pseudorandomness, which is computational. So, to argue that randomness is thus not caused is a very strange, unjustified, logical leap.
It posits a world that does not conform to our everyday observations of causality. Yet, in our everyday observations, we see that a lot of stuff does not have apparent causes. And then we can experimentally determine that there actually were causes through manipulation of the world, in some cases. In some cases, it doesn't seem possible. But it's a leap from saying we can't determine causes to saying that there are no causes.
Variability, Complexity, and Bounded Patterns
Well, there is variability. That's an observable feature of the world. But we cannot always determine causality easily. That's another feature of the world. And we can sometimes determine causality through proper manipulation of the world. That's another feature. And sometimes we can't, perhaps because there's too much complexity, too much complication. For whatever reason, that's all conjecture as to why we can't.
But then we mistakenly, illogically jump from not being able to determine causality to "there is no causality." We call it random and give it a platonic status. But we look at variability, note that seemingly random but probabilistic patterns imply variability is bounded. Even though in the long run, we can make assertions about it based upon mathematics. You can't determine individual events, but you can determine long-run events. And we can do this empirically. And we do do it.
But it's a big step from saying that probability is random to asserting that there are no patterns, there's no bounded variability. Probability always assumes bounded variability. So there is a pattern; it's just not necessarily a simple one.
The Narrow Domain of Probability and Measurement
Of course, all of this stuff about probabilities applies only to things we can measure. In most of the world, we can't measure. So, it's a very narrow domain, concocted by philosophers and mathematicians, who only think about the world in very narrow terms, get caught up in their own linguistic abstractions, I think, or their own symbolic abstractions in the case of mathematicians and statisticians, and then mistake their map for the territory.
Pseudorandomness and the Illogical Leap to True Randomness
Well, it appears that pseudorandomness is algorithmic—it's computational. But then it's asserted that true randomness is not. How would one know? It's, again, an illogical inferential leap.
Experimental and Theoretical Physics: Conceptual Strangeness
So then we get into the strange world of experimental physics and theoretical physics, both where observation, mathematics, and description defy any common understanding of the world. And explanations make absolutely no sense in conventional terms. One is left with Haldane's description of things not only being queerer than we imagine but queerer than we can imagine. Quantum entanglement, wave-particle duality, Planck limits—the whole shebang. None of it makes any sense, of course. Yet, through observation and experiment, it makes some sense empirically, but not a lot.
Patterns in Atomic Decay and the Limits of Understanding
Yet even the randomness apparent of atomic decay follows a pattern. It's not as though all atoms decay at the same time. There's order to it. There's regularity. We can say things about it mathematically. So we have patterns with causes we cannot determine.
However, when we look at causes—ultimate causes—we end in always infinite regress. So, it probably shows the limits of our mental capabilities and the limits of our language to describe the world.
The Unresolvable: The Metaphysical Mire Revisited
So, where do we go? It's unresolvable. We get into the metaphysical mire. Is true randomness a situation where there are uncaused events? In subatomic physics, the events determined to be random actually show pattern. So, that indicates there's some constraint on randomness, which argues that "uncaused" is not an applicable term. Uncaused but constrained? That makes no sense. That's incoherent.
The Conceptual Level of Discussion and the Limits of Debate
I know scholars have looked at these things probably in ways more sophisticated than I can. And when it comes to physics, you really have to be a mathematician. I'm only vaguely familiar with the territory. Without extensive training, you can't even enter the debate, I guess—except at the conceptual level that I have arrived at—where the notion of randomness as things that we're unable to determine the cause of makes sense. That's empirically true.
The notion of randomness as uncaused events is problematic. It can't be demonstrated. It can be asserted, but there's no logical justification for asserting it—nor for asserting the opposite. And the fact that anything we can look at, things that can be measured, that come across as random but show probabilistic constraints.
Of course, we have more outcomes that are not measurable—one-offs—than measurable and regular. So, asserting that there are constraints but uncaused is logically incoherent. And that's about as far as we can go.
Determinism and Randomness as Metaphysical Commitments
Maintaining the world is deterministic and maintaining that the world is random are both metaphysical. We have the observed, empirical, pragmatic observations of the world, whereas we can see variability of outcomes. And sometimes the world is quite regular and predictable, and sometimes it's not.
Sometimes we can see causality; apparently, all animals can act as though they understand, at some level, causality. But often enough, we can't understand the causality. Sometimes we can conduct controlled experiments and come to understand causal relationships. Other times we cannot. We try to, but we fail routinely. We hypothesize, we conjecture, but in the end we fail to understand an awful lot.
And we only understand that which we can measure, and most things can't be measured. And definitions are a poor substitute for understanding—very limited. And then we see that even at the subatomic level, there are patterns. So, to say that things are uncaused becomes problematic. And we note that even these things that are apparently uncaused show regularity patterns—bounded variability.
So, in the end we're left with saying, Haldane was right. And we avoid stepping into the metaphysical mire.
Closing Reflection
Of course, these are my thoughts. Perhaps others, brighter than I am, more informed, more reflective, more well-trained, more familiar with the territory, have deeper thoughts.
Objections That May Be Raised
What objections may be raised to this discussion:
1 - Are there errors of fact?
2 - Are there assertions which have been challenged in other discussions?
It seems pretty silly to claim that existing interpretations of quantum events are more than speculative leaps. None of these objections address the point of the incoherence of positing uncaused but patterned and probabilistic. The first cause idea should be regarded as a hail-Mary pass. So, are objections all conjectural, not grounded empirically nor even in reason?
Introduction
The interplay between variability, causality, determinism, and randomness invites philosophical and scientific debate, but these debates often dissolve into metaphysical incoherence. While variability is observable, its interpretation hinges on conceptual frameworks of causality and randomness that frequently overreach empirical justification. Determinism, when presented as comprehensive causal necessity, and randomness, as uncaused variability, function more as metaphysical stances than empirically substantiated claims. This exploration attempts to clarify the conceptual boundaries of these notions, focusing on their epistemological limits and pragmatic roles rather than their metaphysical ambitions.
Discussion
1. The Observability of Variability
Empirically, variability is everywhere. No two phenomena, events, or objects are perfectly identical in their observable properties. Statistical science emerged to describe this observable variability, constructing frameworks for measuring distributions, deviations, and correlations. However, observing variability is distinct from understanding it. The raw fact of variability does not explain whether it is due to underlying causal structures or a manifestation of randomness in an ontological sense. This distinction marks the beginning of conceptual tension.
2. Pragmatic Causality versus Philosophical Causality
Causality, in a pragmatic sense, enables organisms to predict and control outcomes, responding to regularities in their environment. This pragmatic causality underlies survival, experimentation, and technological development. Philosophical causality, by contrast, seeks to identify necessary connections and ultimate explanations. While pragmatic causality succeeds within its empirical domain, philosophical causality runs into infinite regress and conceptual paradoxes—such as the search for a "first cause" or the necessity of causal chains extending infinitely. Thus, causality bifurcates into useful practice and problematic philosophy.
3. Randomness as a Category of Ignorance
The practical invocation of randomness frequently amounts to an admission of ignorance. When causal mechanisms are unknown or indeterminable, events are described as random. In weather systems, stock markets, or quantum events, randomness operates as a placeholder for epistemic limitation. The conceptual leap from "unknown cause" to "uncaused" is unjustified. This leap reflects a confusion between epistemology (what we can know) and ontology (what exists). Asserting true randomness—events that are fundamentally uncaused—extends beyond the limits of observation and ventures into metaphysical assertion.
4. Mathematical Randomness and Pseudorandomness
Mathematics offers formal definitions of randomness, often through probability theory and statistical mechanics. Pseudorandom number generators in computation simulate randomness through deterministic algorithms. These algorithms, while deterministic, produce sequences indistinguishable from "true" random processes for practical purposes. The distinction between pseudorandomness and "true" randomness is asserted, yet unverifiable. If true randomness exists, its verification lies beyond both mathematical proof and empirical demonstration.
5. The Illogical Leap to Ontological Randomness
From empirical unpredictability and mathematical abstraction, some infer ontological randomness. Yet the step from epistemic unpredictability to ontological randomness lacks logical necessity. In subatomic physics, quantum mechanics describes probabilistic behavior, but whether this reflects intrinsic randomness or hidden variables remains undecidable. Bell's theorem and related experiments close certain loopholes but fail to render a definitive account of causality at the quantum level. The invocation of randomness as a fundamental principle is a metaphysical commitment, not a settled fact.
6. Determinism and Its Limitations
Determinism asserts a world governed entirely by causal necessity, where every event is determined by prior conditions. This view aligns with classical mechanics but faces challenges in light of quantum theory and chaotic systems. Deterministic models in classical physics break down under chaotic conditions, where sensitivity to initial conditions renders long-term prediction impossible. At the quantum level, indeterminacy appears irreducible. Determinism, when extended universally, becomes a metaphysical stance unsupported by empirical completeness.
7. Bounded Variability and Probabilistic Regularities
Probability theory presupposes bounded variability. Probabilistic distributions describe frequencies and tendencies across many events. For example, atomic decay rates follow exponential distributions, allowing predictions about populations, though individual decay events remain unpredictable. This bounded variability implies regularity and structure even in systems labeled as random. Regularity in probabilistic outcomes undermines the concept of absolute randomness and suggests patterns amenable to description, if not causal explanation.
8. The Constraints of Measurement
Probabilistic reasoning applies to phenomena that can be measured, quantified, and subjected to statistical analysis. Yet most phenomena in the world elude such treatment. Human experiences, historical events, and unique occurrences resist statistical formalization. Philosophical interpretations that elevate mathematical abstractions to universal principles mistake narrow domains for the entirety of reality. Measurement, while central to scientific inquiry, captures only a fraction of existence.
9. Conceptual Problems in Experimental and Theoretical Physics
Modern physics presents models that challenge classical intuitions of causality and randomness. Quantum mechanics, relativity, and cosmology rely on mathematics that describes behaviors incompatible with everyday experience. These models succeed empirically but defy conceptual understanding. Quantum entanglement and wave-particle duality exemplify the breakdown of classical categories. As J.B.S. Haldane observed, reality may be "not only queerer than we suppose, but queerer than we can suppose."
10. Patterns in Apparent Randomness: Atomic Decay
Atomic decay demonstrates a domain where apparent randomness coexists with statistical regularity. The decay of individual atoms cannot be predicted, yet decay rates across large populations conform to precise mathematical laws. This suggests constraints operating within systems labeled as random. Whether these constraints imply causality or emergent regularity is unresolved. The existence of probabilistic order undermines claims of absolute randomness.
11. Infinite Regress and the Limits of Causal Explanation
Efforts to trace causal chains invariably encounter infinite regress. Each cause invites inquiry into its own cause, extending the chain indefinitely. Philosophers have attempted to resolve this regress through appeals to first causes, necessary beings, or self-causing entities. Yet these solutions remain speculative and metaphysical. Infinite regress reveals the limitations of causal explanation when extended beyond pragmatic boundaries.
12. Determinism and Randomness as Metaphysical Frameworks
Both determinism and randomness, when asserted universally, function as metaphysical frameworks rather than empirical conclusions. They offer explanatory paradigms but fail to satisfy criteria for empirical validation. Determinism reduces all phenomena to causal necessity; randomness reduces them to acausal unpredictability. Neither position accommodates the empirical mixture of regularity and uncertainty evident in experience.
13. Pragmatic Understanding and Conceptual Modesty
Pragmatic understanding accepts variability, seeks causal relationships, and employs probabilistic models within bounded domains. It avoids metaphysical overreach and acknowledges epistemic limitations. Conceptual modesty refrains from asserting universal determinism or randomness, focusing instead on what can be observed, measured, and pragmatically addressed.
Summary
The conceptual problems surrounding variability, causality, determinism, and randomness reveal the limits of human understanding and the dangers of metaphysical speculation. Variability is an observable fact, but its interpretation depends on frameworks of causality and randomness that often extend beyond empirical justification. Determinism and randomness, when asserted universally, become metaphysical commitments rather than empirically grounded conclusions. Pragmatic causality and probabilistic models offer effective tools for navigating uncertainty but cannot resolve ultimate questions about the nature of reality. Conceptual modesty and empirical humility provide the most coherent approach to these enduring philosophical challenges.
Relevant Readings
Bohr, N. (1958). Atomic physics and human knowledge. Wiley.
Earman, J. (1986). A primer on determinism. Reidel.
Hacking, I. (2001). An introduction to probability and inductive logic. Cambridge University Press.
Hume, D. (1748/2000). An enquiry concerning human understanding (T. L. Beauchamp, Ed.). Oxford University Press.
Popper, K. (1959). The logic of scientific discovery. Hutchinson.
Quine, W. V. O. (1960). Word and object. MIT Press.
Rescher, N. (1995). Luck: The brilliant randomness of everyday life. Farrar, Straus and Giroux.
Salmon, W. C. (1984). Scientific explanation and the causal structure of the world. Princeton University Press.
Taleb, N. N. (2007). The black swan: The impact of the highly improbable. Random House.
van Fraassen, B. C. (1980). The scientific image. Oxford University Press.