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Abstract: A general discussion of topics in theoretical science.
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Abstract: A smaller piece between complexes.
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Abstract: A general discussion of topics in theoretical science.
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The Need for Systematic Thinking
Alex Buckley
Central Organizer of New General Management
5/1/25-5/10/25
Dedicated to the Kiwi Farms
This paper will look at how scientific reasoning underlies not only academic science but also the thinking used in everyday life, and will discuss this relationship through its theoretical roots, modeling strategies, and practical applications.
Theory
Whether you realize it or not, we all use a kind of theoretical organization in the contemplation of our thought: “theory” here being defined as an abstract thing that we can assume acts as a framework unifying all of human knowledge. We each use different management techniques in the manipulation of our own symbolic universes, and I would be tempted to call each of them sciences themselves, but we are usually not aware of how deep theoretical considerations run enough to capitalize that soft everyday science into something harder. Allow me to elaborate.
1. The Structure and Purpose of Theory
A functional theory systematizes observations and reasoning through categorization, classification, and hierarchical organization, enabling explanation, description, and prediction of experienced phenomena. A science is simply that: a coherent system of thought managing the flow of ideas and interrelation of conceptual structures, making apparent some results and hiding others. This is suitably abstract enough that I feel confident in claiming that every system of thought used by every thinking organism is each its own science, but with the caveat that some systems will have a greater explicational power than others.
These lower, unknown but already existing scientific modeling practices that we all engage in everyday but are otherwise unaware of I will call primitive science, in the same sense as that held by the assumed primitive definition. We will assume that these boundaryless, meaningless systems are already there, but could be otherwise realized to a new potential when recognized.
Systematics is the disciplined practice of organizing these concepts, phenomena, or entities into coherent, structured systems based on their relationships. It is crucial for clear thought because it provides scaffolding for our understanding: it helps us see patterns in reality, and avoid confusion or contradiction. It serves as a mechanism for our mind.
I would like to describe each of these six systematic ideas briefly in turn.
Categorization is the process by which we group phenomena based on perceived similarities, differences, or functions. It is foundational to all theoretical work, as it determines what counts as a distinct entity, kind, or phenomenon worth thinking about.
Classification is the act of assigning categories, concepts, or theoretical entities Into systems of shared characteristics. It differs from mere grouping in that it implies a systematic structure: a stable framework in which things are not only collected together but given new order, definitions and relations.
Hierarchical organization is the next higher abstraction, arranging classifications into levels of increasing generality or inclusion, where higher levels encompass and organize the lower ones. It’s a core method of structuring knowledge, and a very important one.
Explanation is a kind of argumentation by way of making something intelligible by showing what it is or how it works. It serves its purpose in identifying causes, structures, mechanisms, or principles that render a particular theoretical structure meaningful and understandable within a framework.
Description is the careful articulation of “why” type questions. It deals in qualities, forms, relations, and appearances: it offers the reader a narrative of reality as encountered.
Prediction is the projection of what comes next based on a given theory, or model. It extends explication (the combination of explanation and description) into the future, and provides its investigator with usable results, although these are not always necessarily positive. It works by applying known structures to anticipate outcomes.
Explication is the theoretical process of taking vague, intuitive, or pretheoretical ideas and clarifying them through precise definition, formalization, or systematic development, as well as providing an explanation. Explication is essential to scientific method, and further in peer review, where clarity, testability, and coherence matter.
These organized systems, managed by their arrangement, are theory. Theories exist within and extend the scientific traditions humans carry and give them meaning.
2. Intellectual Traditions
Scientific theories are hereditary in the same way that genetics are, and are carried from one user to another for as long as the lineage has reproducing members: hence, the inherited connection of all things. These lineages can split, converge, compete, cooperate, and engage in every other ecological interaction known to man. Each new researcher acts as both a custodian and a mutator, transmitting knowledge while also altering it, much like transposons shaping their genomic environment through genetic interactions. These intellectual lineages undergo speciation, splitting into subfields, merging across disciplines, and adapting to new conceptual ecosystems. They engage in competition for explanatory power, cooperation through interdisciplinary synthesis, and face extinction when no longer viable.
This view parallels the historical development of scientific reasoning through various philosophical traditions. Like genes, ideas persist not by being “true” in an absolute sense, but by being functional within a given intellectual ecology.
Scientific theory evolves during its existence by building on previous frameworks: this is a process of descent and refinement. Each new theory inherits structures, assumptions, and methods from its predecessors, modifying them in light of new evidence or conceptual needs. This inheritance mirrors biological descent, where continuity is maintained even as variation introduces novelty. Books could act as the storage mechanism analog for a genome.
Over time, these theoretical lineages form intellectual traditions that behave like evolving populations. They split into competing schools of thought, merge through synthesis, and adapt to new problems and technologies. The history of science is thus not a linear march but an intricate genealogy, shaped by the philosophical traditions that have framed reason itself.
3. Theoretical Descent
Theories do not arise ex nihilo; instead they descend in very much the same way that living organisms do. This may be because the living mind carries its thought, so that the two are really the same, but I'll ignore that for now. Ideas trace their lineage through earlier forms, absorbing, mutating, and sometimes discarding inherited structures. The change of ideas is continuous, but you'll likely find them discreetly carried by unchanging textbooks. All ideas that exist today come from an earlier idea.
Ideas, and their theoretical models, follow an evolutionary trajectory similar to that found in biological theory. This process mirrors Darwinian evolution to an extent, notably in areas like descent with acquired characteristics or variation and differential survival. The difference here is that the change is not genetic (although it may be related), but conceptual. Theories compete in an open ecosystem of minds and institutions, each vying for survival based on explanatory power, communicability, scope, and elegance. They replicate through pedagogy and publication, mutate in interpretation, and speciate through divergence across disciplines.
To be clear, and to strengthen the analogy, the replicative process of theory descent is exactly that: replication. It is not reproduction, either asexual or sexual, as there is no mechanism for budding and there are no breeding partners present in theoretical systems. Instead, and especially in philosophy, systems are copied wholesale and modified for use in whatever case that may be.
Surprisingly, just as in Darwinian evolution, survival of theory is local and contextual: Newtonian mechanics still reigns over much of engineering and daily physics, even as it has been absorbed into the broader, subtler and more general explanation of Einstein’s spacetime. The fitness of a theory is never universal: it is only appropriate to the problem space in which it operates.
“Primitive science” is what I will call the everyday theorizing we all engage in, and it is from these that we find modern science descended. It’s the kind of thinking behind planning today’s errands, arranging seating at a dinner party, or the method in following a cake recipe properly. This “science” is obviously in no way comparable to the “hard” (outsider) science, but is an important stepping stone to such thought. Before scientific instruments are brought to bear on an experiment, before the logician can use his syllogism, and before the investigator can begin arranging the ideas of his thought, he must first begin to think.
This thinking is unsystematic, inconsistent, and incoherent, but it works in everyday use, and can even provide for its user predictable results. For example, the postman couldn't do his job if he didn't understand what to put in the mailboxes or where they even where, and this would require him to carry a model of such in his head, even if it didn’t actually relate to anything at all. He must have a theory of his route, even if he never writes it down or openly acknowledges it.1
A primitive theory survives as long as it works within a given problem space. But when anomalies accumulate or a new conceptual “mutation” offers a better explanatory fit, descent-with-modification occurs. The Copernican model descended from the Ptolemaic, Darwin’s natural selection from Lamarckian inheritance, the new physics from classical physics: not by total replacement, but by subsumption, extension, and revision.
After a time, “advanced science” started to appear, most notably among the Greeks but in several other places as well. This is what happens when a systematic method or model is able to encapsulate that everyday thinking into something more rigorous. This is mainly in the form of writing: the first necessary step is to write down what one thinks, so that another may read it and understand the thoughts carried therein. After that we get things like reference and citation and review. From there scientific organization is achieved, and so are gained the benefit that it provides. Finally the system will be able to give testable results for queried problems, and, as far as physical theory is concerned, this is the legitimating test of any “hard” science.
4. Theoretical Transitions
Thomas Kuhn, as notorious as he is in some circles, did have a point to make in his 1962 book, “The Structure of Scientific Revolutions”. He was chiefly concerned here with the way that science transitions from what he calls one “paradigm” to another. He argued that science does not evolve (my term) through a steady accumulation of facts, but by radical transformations in the foundational frameworks that govern scientific inquiry.
Paradigms are frameworks for the meta categorization and classification of ideas and trends in scientific thought. These are sets of assumptions, practices, and values that shape how individual scientists perceive and engage with the world. To live within a paradigm is to interpret events, objects, and actions according to its codes and accepted ways of thinking, without always realizing that one is interpreting with blinders at all.
Capture occurs when these paradigms no longer simply guide perception but try to confine and control it. It starts as a way of organizing experience but won’t end there: it usually slips into ideology. New progress is no longer made, and novel results stop being produced.
Eventually anomalies will start to accumulate, and while they may be ignored or explained away for some time, the problem persists and becomes worse. Ad hoc explanations are created to try to patch up the sinking ship, but it's usually to no avail. The historical norm is that a newer, more general theory will subsume the older and more specific one. This is a theoretical transition.
When the crisis reaches a tipping point, a scientific revolution becomes possible. After the revolution, a new phase of assumed normative science begins under the fresh paradigm. But the cycle remains. Over time, every paradigm drifts toward capture. The only way to deal with this constant boom-bust cycle is with the collective management of an organizer, and the top-down revolution he could pursue with his height.
5. The economy of Thought
The economy of thought refers to the use of theories as efficient tools to simplify and understand complex phenomena. By identifying patterns and underlying principles, theories allow us to make sense of the intricate systems that we find in nature. I am specifically relating economics with thinking because this relationship provides very useful analogies that we can use in describing some parts of the abstract scientific process. Both thought and economics aim to optimize how we deal with complexity under constraints, whether cognitive or monetary, and I will show how the terms of one field can clarify the complexity of the other.
First I will define the mixed terms used in describing this combined subject. Theory will be the senior partner in this relationship, so I will define economic words in theoretical terms, while retaining their economic heritage.
Money: this term will be used as a stand-in for intellectual capital: time, attention, effort, or funding, anything that is employed in the pursuit of knowledge.
Transactions: any theoretical investment, measured in how much conceptual return, insight, clarity, or further questions, it yields.
Economy: the sum total of concerns related to any scientist or organization of scientists that will have to be dealt with in the engagement of their work.
The “economy” here is the full ecological landscape of constraints, opportunities, risks, and rewards that a thinker or scientific community must navigate. This means thinking economically while planning, acting strategically, and being resource aware with available capital.
Efficiency is not just in reducing costs alone but in wisely paying the opportunity cost: every hour spent reading one text is an hour not spent reading another, and every chosen theory comes at the cost of alternatives left unpursued. Transaction costs, then, appear as the mental friction and effort required to acquire, process, or test an idea
This efficiency is not just intellectual, but also practical: theories reduce transaction costs in the pursuit of knowledge, guiding research, shaping expectations, and coordinating collaboration.
Every theory carries an opportunity cost: to commit to one explanatory framework is to forgo others. The more efficient the theory, the greater its return on that cost. In this sense, theories are investments—adaptive strategies aimed at maximizing explanatory power while minimizing interpretive burden.
Some investigators like to ease the burden of systematic thought and the construction of theoretical models by engaging in something that I call “cheap thinking”. A good description of this phenomena would be that of “cutting corners”: ignoring issues, pushing established boundaries, and otherwise cheating in the effort to pass peer review. The need for scientists to publish probably encourages this behavior.
The production-consumption cycle becomes visible in the creation and absorption of academic products, theories, publications, and textbooks, that feed and are fed by these economic concerns.
6. Organizational Capacity
Organizational capacity is The measure of both how well an individual or institution manages complexity, coordinates theory-building, and scales their efforts without collapse, while at the same time being able to quickly and easily process not only posed questions but also their answers too. This capacity is the ability of the system to handle information, both external and internal. This capacity reflects a system’s ability to handle internal and external information: its cognitive, logistical, and communicative bandwidth.
Empires of thought, those dominant structures within disciplines, expand through mechanisms of increasing returns. Each new idea reinforces and legitimizes the existing body of knowledge, attracting more thinkers, resources, and recognition. The larger the intellectual empire, the greater its pull and the more exponential its returns.
Organizers of grand intellectual systems may find it useful to engage in a practice of self-consistency within their system. Self-consistency is the inner architecture of organizational capacity. It ensures that each answer aligns with the questions posed, that each new theory strengthens rather than undermines the whole.
7. The Self Consistency of Theory
A well made theory will have a few attributes that make them valuable, and the first and most important of those is self consistency. This is when the theory’s components support each other logically, forming a stable and unified framework. This quality is crucial because contradictions within a theory undermine its explanatory power and reliability. If a theory asserts mutually exclusive claims, it cannot be trusted to yield meaningful predictions or insights.2
Self-consistency also plays a crucial role in achieving consilience, which is the alignment of knowledge across different domains. A self-consistent theory can more easily integrate with other coherent theories, enabling a convergence of explanations from diverse fields.
Coherence extends the demands of self consistency to the theory’s relation with other accepted bodies of knowledge and observed experience, most notably the opinion of others. A coherent theory not only fits together logically within itself but also aligns with what is already known or reliably believed. Coherence serves as a connection between internal logic and external plausibility. A coherent theory is one that integrates well into a broader web of belief, usually held by others. It evaluates how well the theory complements, supports, and is supported by other theories, data, and frameworks already in place. Coherence also strengthens consilience, because it enables theories from separate domains to speak to one another without conflict.
Concision refers to a theory’s ability to express its core principles, mechanisms, and implications using the fewest assumptions and simplest formulations necessary, without sacrificing clarity or explanatory power. It is not merely brevity for its own sake, but an economy of structure that avoids redundancy, extraneous parts, and theoretical bloat. A concise theory reflects and supports self-consistency. When a theory is cluttered with unnecessary components or ad hoc additions, it becomes harder to detect contradictions and easier for hidden inconsistencies to fester. In contrast, concision forces each component to be logically justified and mutually supportive, which enhances the internal coherence of the whole.
Theoretical reduction is a concept in the philosophy of science where one theory, which is typically more complex, is shown to be explainable in terms of another, simpler theory. What was previously an overwhelming complex, even if it is self consistent, becomes reduced to a simple and elegant explanation able to describe all the previous theoretical states with fewer descriptive statements. This usually involves demonstrating that the terms, laws, and predictions of one theory can be derived from or mapped onto those of another. For such a reduction to be meaningful, the reduced (higher level) theory must be derivable or at least compatible with the lower-level theory without contradiction. This is where self-consistency becomes critical.
A very common form of theoretical reduction is through the mechanism of unification. This is when two otherwise disparate and widely separated phenomena are found to be directly related, and, more importantly, are found to be two different ways of describing the same underlying reality. Unification Benefits us because it reduces our theoretical load. Instead of managing multiple disconnected theories, we gain a denser, more interconnected framework.
8. The Influence of Philosophy upon Theory
Some scientists like to pretend that they are free of the influence of philosophy in their research. Maybe they imagine that their science may reduce to otherwise abstract philosophical contemplations. I don’t know how they can imagine such a thing, because it’s simply ridiculous: it simply has to be some kind of misunderstanding, whether on purpose or not.
Philosophy is not something science outgrows. It is something science stands on, even when it refuses to look down. Every research project, no matter how technical, is shaped by a host of philosophical commitments, many unspoken, some unconscious. These commitments shape the very framework of inquiry. It may seem abstract and of no importance, but it is very clear to me that thorough and rigorous systematic thinking needs to take place as early as the metaphysics of one’s own research project.
These philosophical foundations are as follows:
Metaphysics: What is the nature of reality? What kinds of things exist, fundamentally? What assumptions structure the world before observation even begins?
Ontology: What kinds of things are assumed to exist? What entities, forces, and structures are posited as real?
Epistemology: How is knowledge of things justified? What counts as evidence, and how is truth established?
And finally how are we to interpret theories? Are they literal descriptions, useful models, provisional tools, or something else entirely?
Even the act of reading a theory involves interpretive decisions. How we read, what we expect, what we emphasize, what we dismiss, this will all be itself shaped by philosophy. Researchers need to confront these kinds of assumptions.
9. The Limits of Theory
These reasons, and among many others, are why we will be constrained in our theoretical reasoning. Certain ways of thought will get us all tied up and with little room for maneuver.
Theoretical frameworks come with boundaries and constraints that define both their power and their limits. These frameworks are built on assumptions, idealizations, and specific domains of applicability, outside of which they may break down, lose explanatory power, or become outright false. The theoretical object being manipulated by these frameworks is the empirical data set, but inference and abduction play a part too.
Evidentiary data forms the empirical foundation upon which theoretical frameworks are tested, supported, or overturned. It consists of observations, measurements, and experimental results that either conform to the predictions of a theory or challenge its validity. But there are limitations to the data set.
Data alone does not determine theory. Multiple theories can explain the same set of observations. What guides selection among them is often philosophical: metaphysical commitments, epistemological standards, and interpretive frameworks.
Underdetermination of theory refers to the idea that empirical evidence alone may not be sufficient to determine which theory is correct when multiple theories can explain the same set of observations or data. This challenges the notion that science can always definitively prove a theory to be true based solely on evidence. In other words, there can be different, often competing, explanations that are equally consistent with the available evidence, and thus, the choice between these theories may depend on other factors, such as background assumptions, simplicity, or theoretical preference.
Some will like to argue for the statistical significance of some or another data set relative to the confirmation of some theory that suits another goal or agenda that one inevitably wishes to achieve. Anyway, this argument is usually held up by nothing more than the thin stick of empiricism. It is so commonly relied upon and most don’t even notice.
This is really all to say that the construction of theoretical models is never from an “objective” state but from a set of assumptions initiated by the investigator.
Theory ladenness is another form of problem. No observation is ever made free of theoretical underpinning. This is more than personal bias: it’s simply the way nature works. Information is not contained out there, external from us. Instead I argue that the information is interpreted.
There are similar limitations in the “theoretical” expression of formal mathematics, these having to do with something called Gödelian limits. Mathematical models suffer a similar limitation in their explanatory capacity as that afflicting physical theory and theoretical science in general. These limits can be stated as such: There will always be truths that lie beyond the system’s reach. In the context of mathematically backed scientific theories, this imposes a limit on theoretical reduction: if a theory aspires to be both complete (able to explain everything) and consistent (free from contradictions), Gödel shows it cannot be both: some truths will always escape formal proof.
Modeling
The real world, being too complex to lend itself easily to examination and discovery of its meaning, will frequently be simplified by the investigating scientist to make their work easier. They justify this by claiming increased clarity of certain things, but at the same time dismissing other kinds of evidence. This isn't necessarily bad, but could become a problem if used as justification for a certain metaphysical foundation, let's say, especially when that claimant denies metaphysics from their science.
1. Idealization
Idealization is the practice of creating simplified versions of real-world phenomena to make them more tractable for analysis and understanding. These simplifications, often referred to as abstractions, strip away the irrelevant parts in order to focus on the essential features of a system. By doing so, idealizations allow scientists to build models that are easier to manipulate, test, and refine, even if they do not capture every detail of the world as it actually is.
Many models rely on perfect conditions that rarely, if ever, occur in nature. Frictionless planes, rational agents, closed systems, and infinitely divisible goods are all examples of such idealizations. While these assumptions are unrealistic, they are not useless. In fact, they provide a baseline from which deviations can be meaningfully studied.
Boundary conditions also play a key role in idealization. These are the limits or constraints placed on a model to define its scope. In the context of theory development, opportunity cost represents the tradeoffs inherent in choosing one theoretical framework or explanatory model over another, another kind of limit.
2. Abstraction Process
Baudrillard provides an excellent model here for what I am trying to describe. His is essentially a game of whispers: it describes the deteriorating nature of communication, and how that information is transmitted: this applies not just to communication, but the nature of thinking too. It is common, especially in biological systems, for a message or idea to be in transit across many different mediums, and an interpretation step is taken upon each crossing. The meaning obviously gets muddled, and this can be more so when the channels of communications have not been pre arranged in advance.
I have already detailed the interpretation process before and to do so again here would be cumbersome. Instead I will describe the relationship between symbols, representation, and Interpretation.
Representation arises when symbols are used to represent or stand in for phenomena beyond their immediate scope. It’s through representation that abstract ideas and complex concepts are communicated. Interpretation is the act of making sense of these symbols and representations. It’s the process through which meaning is derived, whether consciously or unconsciously, from the symbols in use. This abstract complex can be thought of as personal constructions of a conceptual kind, or in another word, theory.
3. Conceptual Constructions
Conceptual constructions refer to the abstract representations or frameworks that are developed to understand and organize phenomena. These constructs are mental models, ideas, or theories that help translate complex or intangible realities into something more accessible for thought, discussion, or experimentation. By framing and simplifying what is often chaotic or ambiguous, conceptual constructions help us think about what we are trying to understand.
Metaphor plays a critical role in conceptual constructions by bridging the gap between the known and the unknown. A metaphor allows us to grasp abstract or complex ideas by relating them to familiar experiences or concrete concepts.
4. Model Systems
These are frameworks developed to study specific phenomena allowing for controlled investigation and comparative analysis. Not all models have to be formalized in the language of logic, or rigorously worked out with mathematical proof, but can be as simple as a metaphor, if it maintains the usual concerns of course.
While some models, particularly in physics, economics, or engineering, are expressed with high mathematical rigor, others may take the form of diagrams, analogies, or even narratives. As long as these models respect the core scientific concerns of coherence, explanatory power, and testability, they remain valid tools for investigation. A well-chosen metaphor, for example, can often illuminate a system more effectively than a complex equation, especially in early stages of theory development.
Among the many types of model systems, computational models have become increasingly prominent. These use algorithms and simulations to mimic the behavior of systems over time, offering powerful ways to test scenarios, visualize outcomes, and exploring dynamics too complex for direct experimentation. Whether simulating cellular processes, ecosystems, or economic markets, computational models provide a bridge between abstract theory and observable behavior, adding to our understanding of the subject and guiding further empirical work.
5. Model Organisms
In biological science it often becomes necessary to employ our study in the search for regular experimental conditions. We do this, both subconsciously and with direct purpose, by the use of evolutionary tools like isolation and selection upon lab stable organisms. This happens in the laboratory, and has produced such “model species” as the lab rat or the cultured fruit fly.
These organisms, because of the above mentioned evolutionary pressures, have incredibly regular phenotypes, and thereby limit variation in experimental results.
We use these particular organisms to serve as stand ins for broader biological understanding of theoretical models. Often the nature of the study is simply too complex to study head on, and we, unlike our siblings in the physics department, lack practical methods of reduction and even the theoretical justification to do such a thing, so instead we try, in other ways, to limit variables in test, in this case by breeding organisms to be lab worthy and consistent.
Practice
Scientific Practice refers to the concrete activities scientists engage in to investigate, test, manipulate, and observe the world. It includes experimentation, measurement, modeling, data collection, instrumentation, fieldwork, and technological application. Practice is the embodied, operational side of science: the doing, as opposed to contemplation. Whereas theory is often abstract, general, and conceptual, practice is situated, specific, and procedural. It deals with messy reality: equipment malfunctions, noisy data, imperfect methods, and unexpected variables. Scientists will be tasked with making decisions in real time, solving practical problems, and adjusting methods based on experience and context. Practice is where theory is challenged and thereby made real.
1. Identifying Problems
In scientific practice, identifying problems is the crucial first step in formulating research questions. They build on previous knowledge, experience, and observations. The process by which scientists decide which questions to investigate is multifaceted and shaped by a combination of curiosity, previous research, societal needs, and theoretical frameworks. Science is inherently cumulative, and much of the questions that scientists explore emerge from gaps in or extensions of existing knowledge. The scientific community constantly builds on past discoveries, and new questions often arise from the limitations or unresolved aspects of previous research.
Sometimes, scientists identify research questions simply by observing the world around them. This curiosity driven approach is often fundamental, where an unexplained phenomenon or pattern catches attention.
Effective data collection requires careful planning, including the selection of appropriate instruments, determination of sampling methods, and establishment of clear procedures for recording and organizing observations. Whether through direct measurement, observation, or the use of sensors and automated tools, data collection ensures that experiments are grounded in objective, repeatable evidence. This process allows researchers to detect patterns, test predictions, and refine models with a high degree of confidence, and will allow the investigator to open up his inquiry to experimentation.
2. Experiment
Experiment is the process by which hypotheses are tested through structured inquiry. It involves the deliberate design and execution of procedures that aim to isolate variables, control conditions, and observe outcomes. An effective experiment provides a repeatable and supposedly objective means of evaluating whether a given proposition holds under specific circumstances.
Instrumentation refers to the selection and application of tools, devices, or systems that facilitate the observation, control, and recording of phenomena during the experiment. The reliability and precision of these instruments play a critical role in ensuring valid results. Whether simple or complex, the choice of instrumentation must align with the nature of the data being collected and the sensitivity required for detecting meaningful differences or patterns.
Measurement is the act of quantifying observations, converting qualitative experiences into numerical or standardized data that can be analyzed. It involves assigning values to variables in a consistent and interpretable manner. Accurate measurement is essential for comparison, replication, and statistical analysis, serving as the foundation for interpreting experimental results and drawing conclusions. This, and the above experimental methodology will allow the test of hypothesis.
3. Hypothesis Testing
Hypothesis testing is the structured method used to evaluate the validity of proposed explanations or predictions. It involves formulating a clear, testable statement, often in the form of a null and alternative hypothesis, and then using empirical data to assess whether the evidence supports or refutes the proposition. This process is unique to the scientific method, serving as a disciplined way to determine if observed outcomes can be attributed to chance or to the influence of specific variables.
4. Empirical Validation
Empirical validation is the process by which theories gain credibility through direct observation and measurable evidence. It involves testing theoretical claims against real world data to determine whether the predictions made by a theory hold true in practice. This connection to observable reality is what distinguishes scientific knowledge from speculation or abstract reasoning alone.
For a theory to be empirically validated, it must consistently account for phenomena across varied conditions and withstand repeated scrutiny through observation and experimental measurement. Empirical validation strengthens the explanatory power of a theory and increases confidence in its applicability, while failure to validate may lead to revision, refinement, or rejection of the theory altogether. In this way, empirical validation serves as the ultimate test of a theory’s robustness and relevance.
5. Replicability
We need replicability in the results of an experiment or study because we want it to be reproducible by others using the same methods and conditions. It serves as a cornerstone of empirical validity, providing assurance that findings are not merely coincidental or biased. When results can be consistently replicated, confidence in their accuracy and relevance increases.
However, achieving meaningful replicability often requires a large number of test instances, sometimes in the thousands or even millions, to account for variability and to ensure that findings are not artifacts of chance or specific conditions. This introduces the importance of sampling, generalization, and statistical power. A well designed experiment must select representative samples, aim for results that can extend beyond the specific test group, and have sufficient statistical power to detect genuine effects amidst background noise.
Recently there has been exposure of a particular problem known as the “replication crisis” and this has exposed the fragility of many published results, where repeated attempts to reproduce findings have failed. This, to me, seems to be a victim of falsification.
6. Fieldwork and Technology
Finally, fieldwork is the practice of collecting data and making observations in real-world, often uncontrolled environments. It contrasts with laboratory-based research by engaging directly with the complexities, variabilities, and nuances of natural or social settings. Fieldwork allows for the study of phenomena as they unfold in context, whether its ecology, anthropology, geology, or other disciplines: this will provide depth and realism to controlled experiments.
Notes
There is an alternative explanation here. It is possible that everything is simply the interaction of physical events, and that there is no other guiding force. It may well be that we are nothing but the confluence of happenstance, and if it were so we would likely have no way of knowing.
There is an interesting caveat in mathematics: quadratic equations for example will return two opposite but equally true answers. For practical purposes we only use the positive answer.
Alex Buckley
Central Organizer of New General Management
5/1/25-5/10/25
Dedicated to the Kiwi Farms
This paper will look at how scientific reasoning underlies not only academic science but also the thinking used in everyday life, and will discuss this relationship through its theoretical roots, modeling strategies, and practical applications.
Theory
Whether you realize it or not, we all use a kind of theoretical organization in the contemplation of our thought: “theory” here being defined as an abstract thing that we can assume acts as a framework unifying all of human knowledge. We each use different management techniques in the manipulation of our own symbolic universes, and I would be tempted to call each of them sciences themselves, but we are usually not aware of how deep theoretical considerations run enough to capitalize that soft everyday science into something harder. Allow me to elaborate.
1. The Structure and Purpose of Theory
A functional theory systematizes observations and reasoning through categorization, classification, and hierarchical organization, enabling explanation, description, and prediction of experienced phenomena. A science is simply that: a coherent system of thought managing the flow of ideas and interrelation of conceptual structures, making apparent some results and hiding others. This is suitably abstract enough that I feel confident in claiming that every system of thought used by every thinking organism is each its own science, but with the caveat that some systems will have a greater explicational power than others.
These lower, unknown but already existing scientific modeling practices that we all engage in everyday but are otherwise unaware of I will call primitive science, in the same sense as that held by the assumed primitive definition. We will assume that these boundaryless, meaningless systems are already there, but could be otherwise realized to a new potential when recognized.
Systematics is the disciplined practice of organizing these concepts, phenomena, or entities into coherent, structured systems based on their relationships. It is crucial for clear thought because it provides scaffolding for our understanding: it helps us see patterns in reality, and avoid confusion or contradiction. It serves as a mechanism for our mind.
I would like to describe each of these six systematic ideas briefly in turn.
Categorization is the process by which we group phenomena based on perceived similarities, differences, or functions. It is foundational to all theoretical work, as it determines what counts as a distinct entity, kind, or phenomenon worth thinking about.
Classification is the act of assigning categories, concepts, or theoretical entities Into systems of shared characteristics. It differs from mere grouping in that it implies a systematic structure: a stable framework in which things are not only collected together but given new order, definitions and relations.
Hierarchical organization is the next higher abstraction, arranging classifications into levels of increasing generality or inclusion, where higher levels encompass and organize the lower ones. It’s a core method of structuring knowledge, and a very important one.
Explanation is a kind of argumentation by way of making something intelligible by showing what it is or how it works. It serves its purpose in identifying causes, structures, mechanisms, or principles that render a particular theoretical structure meaningful and understandable within a framework.
Description is the careful articulation of “why” type questions. It deals in qualities, forms, relations, and appearances: it offers the reader a narrative of reality as encountered.
Prediction is the projection of what comes next based on a given theory, or model. It extends explication (the combination of explanation and description) into the future, and provides its investigator with usable results, although these are not always necessarily positive. It works by applying known structures to anticipate outcomes.
Explication is the theoretical process of taking vague, intuitive, or pretheoretical ideas and clarifying them through precise definition, formalization, or systematic development, as well as providing an explanation. Explication is essential to scientific method, and further in peer review, where clarity, testability, and coherence matter.
These organized systems, managed by their arrangement, are theory. Theories exist within and extend the scientific traditions humans carry and give them meaning.
2. Intellectual Traditions
Scientific theories are hereditary in the same way that genetics are, and are carried from one user to another for as long as the lineage has reproducing members: hence, the inherited connection of all things. These lineages can split, converge, compete, cooperate, and engage in every other ecological interaction known to man. Each new researcher acts as both a custodian and a mutator, transmitting knowledge while also altering it, much like transposons shaping their genomic environment through genetic interactions. These intellectual lineages undergo speciation, splitting into subfields, merging across disciplines, and adapting to new conceptual ecosystems. They engage in competition for explanatory power, cooperation through interdisciplinary synthesis, and face extinction when no longer viable.
This view parallels the historical development of scientific reasoning through various philosophical traditions. Like genes, ideas persist not by being “true” in an absolute sense, but by being functional within a given intellectual ecology.
Scientific theory evolves during its existence by building on previous frameworks: this is a process of descent and refinement. Each new theory inherits structures, assumptions, and methods from its predecessors, modifying them in light of new evidence or conceptual needs. This inheritance mirrors biological descent, where continuity is maintained even as variation introduces novelty. Books could act as the storage mechanism analog for a genome.
Over time, these theoretical lineages form intellectual traditions that behave like evolving populations. They split into competing schools of thought, merge through synthesis, and adapt to new problems and technologies. The history of science is thus not a linear march but an intricate genealogy, shaped by the philosophical traditions that have framed reason itself.
3. Theoretical Descent
Theories do not arise ex nihilo; instead they descend in very much the same way that living organisms do. This may be because the living mind carries its thought, so that the two are really the same, but I'll ignore that for now. Ideas trace their lineage through earlier forms, absorbing, mutating, and sometimes discarding inherited structures. The change of ideas is continuous, but you'll likely find them discreetly carried by unchanging textbooks. All ideas that exist today come from an earlier idea.
Ideas, and their theoretical models, follow an evolutionary trajectory similar to that found in biological theory. This process mirrors Darwinian evolution to an extent, notably in areas like descent with acquired characteristics or variation and differential survival. The difference here is that the change is not genetic (although it may be related), but conceptual. Theories compete in an open ecosystem of minds and institutions, each vying for survival based on explanatory power, communicability, scope, and elegance. They replicate through pedagogy and publication, mutate in interpretation, and speciate through divergence across disciplines.
To be clear, and to strengthen the analogy, the replicative process of theory descent is exactly that: replication. It is not reproduction, either asexual or sexual, as there is no mechanism for budding and there are no breeding partners present in theoretical systems. Instead, and especially in philosophy, systems are copied wholesale and modified for use in whatever case that may be.
Surprisingly, just as in Darwinian evolution, survival of theory is local and contextual: Newtonian mechanics still reigns over much of engineering and daily physics, even as it has been absorbed into the broader, subtler and more general explanation of Einstein’s spacetime. The fitness of a theory is never universal: it is only appropriate to the problem space in which it operates.
“Primitive science” is what I will call the everyday theorizing we all engage in, and it is from these that we find modern science descended. It’s the kind of thinking behind planning today’s errands, arranging seating at a dinner party, or the method in following a cake recipe properly. This “science” is obviously in no way comparable to the “hard” (outsider) science, but is an important stepping stone to such thought. Before scientific instruments are brought to bear on an experiment, before the logician can use his syllogism, and before the investigator can begin arranging the ideas of his thought, he must first begin to think.
This thinking is unsystematic, inconsistent, and incoherent, but it works in everyday use, and can even provide for its user predictable results. For example, the postman couldn't do his job if he didn't understand what to put in the mailboxes or where they even where, and this would require him to carry a model of such in his head, even if it didn’t actually relate to anything at all. He must have a theory of his route, even if he never writes it down or openly acknowledges it.1
A primitive theory survives as long as it works within a given problem space. But when anomalies accumulate or a new conceptual “mutation” offers a better explanatory fit, descent-with-modification occurs. The Copernican model descended from the Ptolemaic, Darwin’s natural selection from Lamarckian inheritance, the new physics from classical physics: not by total replacement, but by subsumption, extension, and revision.
After a time, “advanced science” started to appear, most notably among the Greeks but in several other places as well. This is what happens when a systematic method or model is able to encapsulate that everyday thinking into something more rigorous. This is mainly in the form of writing: the first necessary step is to write down what one thinks, so that another may read it and understand the thoughts carried therein. After that we get things like reference and citation and review. From there scientific organization is achieved, and so are gained the benefit that it provides. Finally the system will be able to give testable results for queried problems, and, as far as physical theory is concerned, this is the legitimating test of any “hard” science.
4. Theoretical Transitions
Thomas Kuhn, as notorious as he is in some circles, did have a point to make in his 1962 book, “The Structure of Scientific Revolutions”. He was chiefly concerned here with the way that science transitions from what he calls one “paradigm” to another. He argued that science does not evolve (my term) through a steady accumulation of facts, but by radical transformations in the foundational frameworks that govern scientific inquiry.
Paradigms are frameworks for the meta categorization and classification of ideas and trends in scientific thought. These are sets of assumptions, practices, and values that shape how individual scientists perceive and engage with the world. To live within a paradigm is to interpret events, objects, and actions according to its codes and accepted ways of thinking, without always realizing that one is interpreting with blinders at all.
Capture occurs when these paradigms no longer simply guide perception but try to confine and control it. It starts as a way of organizing experience but won’t end there: it usually slips into ideology. New progress is no longer made, and novel results stop being produced.
Eventually anomalies will start to accumulate, and while they may be ignored or explained away for some time, the problem persists and becomes worse. Ad hoc explanations are created to try to patch up the sinking ship, but it's usually to no avail. The historical norm is that a newer, more general theory will subsume the older and more specific one. This is a theoretical transition.
When the crisis reaches a tipping point, a scientific revolution becomes possible. After the revolution, a new phase of assumed normative science begins under the fresh paradigm. But the cycle remains. Over time, every paradigm drifts toward capture. The only way to deal with this constant boom-bust cycle is with the collective management of an organizer, and the top-down revolution he could pursue with his height.
5. The economy of Thought
The economy of thought refers to the use of theories as efficient tools to simplify and understand complex phenomena. By identifying patterns and underlying principles, theories allow us to make sense of the intricate systems that we find in nature. I am specifically relating economics with thinking because this relationship provides very useful analogies that we can use in describing some parts of the abstract scientific process. Both thought and economics aim to optimize how we deal with complexity under constraints, whether cognitive or monetary, and I will show how the terms of one field can clarify the complexity of the other.
First I will define the mixed terms used in describing this combined subject. Theory will be the senior partner in this relationship, so I will define economic words in theoretical terms, while retaining their economic heritage.
Money: this term will be used as a stand-in for intellectual capital: time, attention, effort, or funding, anything that is employed in the pursuit of knowledge.
Transactions: any theoretical investment, measured in how much conceptual return, insight, clarity, or further questions, it yields.
Economy: the sum total of concerns related to any scientist or organization of scientists that will have to be dealt with in the engagement of their work.
The “economy” here is the full ecological landscape of constraints, opportunities, risks, and rewards that a thinker or scientific community must navigate. This means thinking economically while planning, acting strategically, and being resource aware with available capital.
Efficiency is not just in reducing costs alone but in wisely paying the opportunity cost: every hour spent reading one text is an hour not spent reading another, and every chosen theory comes at the cost of alternatives left unpursued. Transaction costs, then, appear as the mental friction and effort required to acquire, process, or test an idea
This efficiency is not just intellectual, but also practical: theories reduce transaction costs in the pursuit of knowledge, guiding research, shaping expectations, and coordinating collaboration.
Every theory carries an opportunity cost: to commit to one explanatory framework is to forgo others. The more efficient the theory, the greater its return on that cost. In this sense, theories are investments—adaptive strategies aimed at maximizing explanatory power while minimizing interpretive burden.
Some investigators like to ease the burden of systematic thought and the construction of theoretical models by engaging in something that I call “cheap thinking”. A good description of this phenomena would be that of “cutting corners”: ignoring issues, pushing established boundaries, and otherwise cheating in the effort to pass peer review. The need for scientists to publish probably encourages this behavior.
The production-consumption cycle becomes visible in the creation and absorption of academic products, theories, publications, and textbooks, that feed and are fed by these economic concerns.
6. Organizational Capacity
Organizational capacity is The measure of both how well an individual or institution manages complexity, coordinates theory-building, and scales their efforts without collapse, while at the same time being able to quickly and easily process not only posed questions but also their answers too. This capacity is the ability of the system to handle information, both external and internal. This capacity reflects a system’s ability to handle internal and external information: its cognitive, logistical, and communicative bandwidth.
Empires of thought, those dominant structures within disciplines, expand through mechanisms of increasing returns. Each new idea reinforces and legitimizes the existing body of knowledge, attracting more thinkers, resources, and recognition. The larger the intellectual empire, the greater its pull and the more exponential its returns.
Organizers of grand intellectual systems may find it useful to engage in a practice of self-consistency within their system. Self-consistency is the inner architecture of organizational capacity. It ensures that each answer aligns with the questions posed, that each new theory strengthens rather than undermines the whole.
7. The Self Consistency of Theory
A well made theory will have a few attributes that make them valuable, and the first and most important of those is self consistency. This is when the theory’s components support each other logically, forming a stable and unified framework. This quality is crucial because contradictions within a theory undermine its explanatory power and reliability. If a theory asserts mutually exclusive claims, it cannot be trusted to yield meaningful predictions or insights.2
Self-consistency also plays a crucial role in achieving consilience, which is the alignment of knowledge across different domains. A self-consistent theory can more easily integrate with other coherent theories, enabling a convergence of explanations from diverse fields.
Coherence extends the demands of self consistency to the theory’s relation with other accepted bodies of knowledge and observed experience, most notably the opinion of others. A coherent theory not only fits together logically within itself but also aligns with what is already known or reliably believed. Coherence serves as a connection between internal logic and external plausibility. A coherent theory is one that integrates well into a broader web of belief, usually held by others. It evaluates how well the theory complements, supports, and is supported by other theories, data, and frameworks already in place. Coherence also strengthens consilience, because it enables theories from separate domains to speak to one another without conflict.
Concision refers to a theory’s ability to express its core principles, mechanisms, and implications using the fewest assumptions and simplest formulations necessary, without sacrificing clarity or explanatory power. It is not merely brevity for its own sake, but an economy of structure that avoids redundancy, extraneous parts, and theoretical bloat. A concise theory reflects and supports self-consistency. When a theory is cluttered with unnecessary components or ad hoc additions, it becomes harder to detect contradictions and easier for hidden inconsistencies to fester. In contrast, concision forces each component to be logically justified and mutually supportive, which enhances the internal coherence of the whole.
Theoretical reduction is a concept in the philosophy of science where one theory, which is typically more complex, is shown to be explainable in terms of another, simpler theory. What was previously an overwhelming complex, even if it is self consistent, becomes reduced to a simple and elegant explanation able to describe all the previous theoretical states with fewer descriptive statements. This usually involves demonstrating that the terms, laws, and predictions of one theory can be derived from or mapped onto those of another. For such a reduction to be meaningful, the reduced (higher level) theory must be derivable or at least compatible with the lower-level theory without contradiction. This is where self-consistency becomes critical.
A very common form of theoretical reduction is through the mechanism of unification. This is when two otherwise disparate and widely separated phenomena are found to be directly related, and, more importantly, are found to be two different ways of describing the same underlying reality. Unification Benefits us because it reduces our theoretical load. Instead of managing multiple disconnected theories, we gain a denser, more interconnected framework.
8. The Influence of Philosophy upon Theory
Some scientists like to pretend that they are free of the influence of philosophy in their research. Maybe they imagine that their science may reduce to otherwise abstract philosophical contemplations. I don’t know how they can imagine such a thing, because it’s simply ridiculous: it simply has to be some kind of misunderstanding, whether on purpose or not.
Philosophy is not something science outgrows. It is something science stands on, even when it refuses to look down. Every research project, no matter how technical, is shaped by a host of philosophical commitments, many unspoken, some unconscious. These commitments shape the very framework of inquiry. It may seem abstract and of no importance, but it is very clear to me that thorough and rigorous systematic thinking needs to take place as early as the metaphysics of one’s own research project.
These philosophical foundations are as follows:
Metaphysics: What is the nature of reality? What kinds of things exist, fundamentally? What assumptions structure the world before observation even begins?
Ontology: What kinds of things are assumed to exist? What entities, forces, and structures are posited as real?
Epistemology: How is knowledge of things justified? What counts as evidence, and how is truth established?
And finally how are we to interpret theories? Are they literal descriptions, useful models, provisional tools, or something else entirely?
Even the act of reading a theory involves interpretive decisions. How we read, what we expect, what we emphasize, what we dismiss, this will all be itself shaped by philosophy. Researchers need to confront these kinds of assumptions.
9. The Limits of Theory
These reasons, and among many others, are why we will be constrained in our theoretical reasoning. Certain ways of thought will get us all tied up and with little room for maneuver.
Theoretical frameworks come with boundaries and constraints that define both their power and their limits. These frameworks are built on assumptions, idealizations, and specific domains of applicability, outside of which they may break down, lose explanatory power, or become outright false. The theoretical object being manipulated by these frameworks is the empirical data set, but inference and abduction play a part too.
Evidentiary data forms the empirical foundation upon which theoretical frameworks are tested, supported, or overturned. It consists of observations, measurements, and experimental results that either conform to the predictions of a theory or challenge its validity. But there are limitations to the data set.
Data alone does not determine theory. Multiple theories can explain the same set of observations. What guides selection among them is often philosophical: metaphysical commitments, epistemological standards, and interpretive frameworks.
Underdetermination of theory refers to the idea that empirical evidence alone may not be sufficient to determine which theory is correct when multiple theories can explain the same set of observations or data. This challenges the notion that science can always definitively prove a theory to be true based solely on evidence. In other words, there can be different, often competing, explanations that are equally consistent with the available evidence, and thus, the choice between these theories may depend on other factors, such as background assumptions, simplicity, or theoretical preference.
Some will like to argue for the statistical significance of some or another data set relative to the confirmation of some theory that suits another goal or agenda that one inevitably wishes to achieve. Anyway, this argument is usually held up by nothing more than the thin stick of empiricism. It is so commonly relied upon and most don’t even notice.
This is really all to say that the construction of theoretical models is never from an “objective” state but from a set of assumptions initiated by the investigator.
Theory ladenness is another form of problem. No observation is ever made free of theoretical underpinning. This is more than personal bias: it’s simply the way nature works. Information is not contained out there, external from us. Instead I argue that the information is interpreted.
There are similar limitations in the “theoretical” expression of formal mathematics, these having to do with something called Gödelian limits. Mathematical models suffer a similar limitation in their explanatory capacity as that afflicting physical theory and theoretical science in general. These limits can be stated as such: There will always be truths that lie beyond the system’s reach. In the context of mathematically backed scientific theories, this imposes a limit on theoretical reduction: if a theory aspires to be both complete (able to explain everything) and consistent (free from contradictions), Gödel shows it cannot be both: some truths will always escape formal proof.
Modeling
The real world, being too complex to lend itself easily to examination and discovery of its meaning, will frequently be simplified by the investigating scientist to make their work easier. They justify this by claiming increased clarity of certain things, but at the same time dismissing other kinds of evidence. This isn't necessarily bad, but could become a problem if used as justification for a certain metaphysical foundation, let's say, especially when that claimant denies metaphysics from their science.
1. Idealization
Idealization is the practice of creating simplified versions of real-world phenomena to make them more tractable for analysis and understanding. These simplifications, often referred to as abstractions, strip away the irrelevant parts in order to focus on the essential features of a system. By doing so, idealizations allow scientists to build models that are easier to manipulate, test, and refine, even if they do not capture every detail of the world as it actually is.
Many models rely on perfect conditions that rarely, if ever, occur in nature. Frictionless planes, rational agents, closed systems, and infinitely divisible goods are all examples of such idealizations. While these assumptions are unrealistic, they are not useless. In fact, they provide a baseline from which deviations can be meaningfully studied.
Boundary conditions also play a key role in idealization. These are the limits or constraints placed on a model to define its scope. In the context of theory development, opportunity cost represents the tradeoffs inherent in choosing one theoretical framework or explanatory model over another, another kind of limit.
2. Abstraction Process
Baudrillard provides an excellent model here for what I am trying to describe. His is essentially a game of whispers: it describes the deteriorating nature of communication, and how that information is transmitted: this applies not just to communication, but the nature of thinking too. It is common, especially in biological systems, for a message or idea to be in transit across many different mediums, and an interpretation step is taken upon each crossing. The meaning obviously gets muddled, and this can be more so when the channels of communications have not been pre arranged in advance.
I have already detailed the interpretation process before and to do so again here would be cumbersome. Instead I will describe the relationship between symbols, representation, and Interpretation.
Representation arises when symbols are used to represent or stand in for phenomena beyond their immediate scope. It’s through representation that abstract ideas and complex concepts are communicated. Interpretation is the act of making sense of these symbols and representations. It’s the process through which meaning is derived, whether consciously or unconsciously, from the symbols in use. This abstract complex can be thought of as personal constructions of a conceptual kind, or in another word, theory.
3. Conceptual Constructions
Conceptual constructions refer to the abstract representations or frameworks that are developed to understand and organize phenomena. These constructs are mental models, ideas, or theories that help translate complex or intangible realities into something more accessible for thought, discussion, or experimentation. By framing and simplifying what is often chaotic or ambiguous, conceptual constructions help us think about what we are trying to understand.
Metaphor plays a critical role in conceptual constructions by bridging the gap between the known and the unknown. A metaphor allows us to grasp abstract or complex ideas by relating them to familiar experiences or concrete concepts.
4. Model Systems
These are frameworks developed to study specific phenomena allowing for controlled investigation and comparative analysis. Not all models have to be formalized in the language of logic, or rigorously worked out with mathematical proof, but can be as simple as a metaphor, if it maintains the usual concerns of course.
While some models, particularly in physics, economics, or engineering, are expressed with high mathematical rigor, others may take the form of diagrams, analogies, or even narratives. As long as these models respect the core scientific concerns of coherence, explanatory power, and testability, they remain valid tools for investigation. A well-chosen metaphor, for example, can often illuminate a system more effectively than a complex equation, especially in early stages of theory development.
Among the many types of model systems, computational models have become increasingly prominent. These use algorithms and simulations to mimic the behavior of systems over time, offering powerful ways to test scenarios, visualize outcomes, and exploring dynamics too complex for direct experimentation. Whether simulating cellular processes, ecosystems, or economic markets, computational models provide a bridge between abstract theory and observable behavior, adding to our understanding of the subject and guiding further empirical work.
5. Model Organisms
In biological science it often becomes necessary to employ our study in the search for regular experimental conditions. We do this, both subconsciously and with direct purpose, by the use of evolutionary tools like isolation and selection upon lab stable organisms. This happens in the laboratory, and has produced such “model species” as the lab rat or the cultured fruit fly.
These organisms, because of the above mentioned evolutionary pressures, have incredibly regular phenotypes, and thereby limit variation in experimental results.
We use these particular organisms to serve as stand ins for broader biological understanding of theoretical models. Often the nature of the study is simply too complex to study head on, and we, unlike our siblings in the physics department, lack practical methods of reduction and even the theoretical justification to do such a thing, so instead we try, in other ways, to limit variables in test, in this case by breeding organisms to be lab worthy and consistent.
Practice
Scientific Practice refers to the concrete activities scientists engage in to investigate, test, manipulate, and observe the world. It includes experimentation, measurement, modeling, data collection, instrumentation, fieldwork, and technological application. Practice is the embodied, operational side of science: the doing, as opposed to contemplation. Whereas theory is often abstract, general, and conceptual, practice is situated, specific, and procedural. It deals with messy reality: equipment malfunctions, noisy data, imperfect methods, and unexpected variables. Scientists will be tasked with making decisions in real time, solving practical problems, and adjusting methods based on experience and context. Practice is where theory is challenged and thereby made real.
1. Identifying Problems
In scientific practice, identifying problems is the crucial first step in formulating research questions. They build on previous knowledge, experience, and observations. The process by which scientists decide which questions to investigate is multifaceted and shaped by a combination of curiosity, previous research, societal needs, and theoretical frameworks. Science is inherently cumulative, and much of the questions that scientists explore emerge from gaps in or extensions of existing knowledge. The scientific community constantly builds on past discoveries, and new questions often arise from the limitations or unresolved aspects of previous research.
Sometimes, scientists identify research questions simply by observing the world around them. This curiosity driven approach is often fundamental, where an unexplained phenomenon or pattern catches attention.
Effective data collection requires careful planning, including the selection of appropriate instruments, determination of sampling methods, and establishment of clear procedures for recording and organizing observations. Whether through direct measurement, observation, or the use of sensors and automated tools, data collection ensures that experiments are grounded in objective, repeatable evidence. This process allows researchers to detect patterns, test predictions, and refine models with a high degree of confidence, and will allow the investigator to open up his inquiry to experimentation.
2. Experiment
Experiment is the process by which hypotheses are tested through structured inquiry. It involves the deliberate design and execution of procedures that aim to isolate variables, control conditions, and observe outcomes. An effective experiment provides a repeatable and supposedly objective means of evaluating whether a given proposition holds under specific circumstances.
Instrumentation refers to the selection and application of tools, devices, or systems that facilitate the observation, control, and recording of phenomena during the experiment. The reliability and precision of these instruments play a critical role in ensuring valid results. Whether simple or complex, the choice of instrumentation must align with the nature of the data being collected and the sensitivity required for detecting meaningful differences or patterns.
Measurement is the act of quantifying observations, converting qualitative experiences into numerical or standardized data that can be analyzed. It involves assigning values to variables in a consistent and interpretable manner. Accurate measurement is essential for comparison, replication, and statistical analysis, serving as the foundation for interpreting experimental results and drawing conclusions. This, and the above experimental methodology will allow the test of hypothesis.
3. Hypothesis Testing
Hypothesis testing is the structured method used to evaluate the validity of proposed explanations or predictions. It involves formulating a clear, testable statement, often in the form of a null and alternative hypothesis, and then using empirical data to assess whether the evidence supports or refutes the proposition. This process is unique to the scientific method, serving as a disciplined way to determine if observed outcomes can be attributed to chance or to the influence of specific variables.
4. Empirical Validation
Empirical validation is the process by which theories gain credibility through direct observation and measurable evidence. It involves testing theoretical claims against real world data to determine whether the predictions made by a theory hold true in practice. This connection to observable reality is what distinguishes scientific knowledge from speculation or abstract reasoning alone.
For a theory to be empirically validated, it must consistently account for phenomena across varied conditions and withstand repeated scrutiny through observation and experimental measurement. Empirical validation strengthens the explanatory power of a theory and increases confidence in its applicability, while failure to validate may lead to revision, refinement, or rejection of the theory altogether. In this way, empirical validation serves as the ultimate test of a theory’s robustness and relevance.
5. Replicability
We need replicability in the results of an experiment or study because we want it to be reproducible by others using the same methods and conditions. It serves as a cornerstone of empirical validity, providing assurance that findings are not merely coincidental or biased. When results can be consistently replicated, confidence in their accuracy and relevance increases.
However, achieving meaningful replicability often requires a large number of test instances, sometimes in the thousands or even millions, to account for variability and to ensure that findings are not artifacts of chance or specific conditions. This introduces the importance of sampling, generalization, and statistical power. A well designed experiment must select representative samples, aim for results that can extend beyond the specific test group, and have sufficient statistical power to detect genuine effects amidst background noise.
Recently there has been exposure of a particular problem known as the “replication crisis” and this has exposed the fragility of many published results, where repeated attempts to reproduce findings have failed. This, to me, seems to be a victim of falsification.
6. Fieldwork and Technology
Finally, fieldwork is the practice of collecting data and making observations in real-world, often uncontrolled environments. It contrasts with laboratory-based research by engaging directly with the complexities, variabilities, and nuances of natural or social settings. Fieldwork allows for the study of phenomena as they unfold in context, whether its ecology, anthropology, geology, or other disciplines: this will provide depth and realism to controlled experiments.
Notes
There is an alternative explanation here. It is possible that everything is simply the interaction of physical events, and that there is no other guiding force. It may well be that we are nothing but the confluence of happenstance, and if it were so we would likely have no way of knowing.
There is an interesting caveat in mathematics: quadratic equations for example will return two opposite but equally true answers. For practical purposes we only use the positive answer.
In addition I also have this to offer.
Abstract: A smaller piece between complexes.
In plaintext:
A Monument to Nothing
Alex Buckley
Central Organizer of New General Management
5/8/25
Dedicated to the Kiwi Farms
A road runs between two of the major population centers on this part of the continent: little used, it nevertheless is in a near perfect state.
Its painted lines appear to have been done not but the day before, and the asphalt must have been laid sometime in the last month, at the very least. And, yet, not a car uses it.
The road bends through the countryside, meandering past small provincial towns, their villagers working fields and engaging in communal farm life. None of them use automobiles.
If one of those people ever were to look up from their job, had they found a vehicle and were driving along the road, traveling quite a ways before they ever found anything, for the two cities that they live between are far away.
They would, after a time, with the sun setting behind them, eventually come to find something. In the middle of nowhere, and for no apparent reason, a grand monument is dedicated by the side of the road.
It’s a horrible thing: a great bronze slab. It has equal proportions, maybe eight feet tall and twelve feet or so by length and width, with small details along the trim. Nothing sits atop it.
The following could be read when looking at the inscription chiseled into a plate on one of its sides:
“Gaze upon my work and despair!”
This monument to nothing, for someone, was everything. When he was done he looked upon his world, and it was good.
Alex Buckley
Central Organizer of New General Management
5/8/25
Dedicated to the Kiwi Farms
A road runs between two of the major population centers on this part of the continent: little used, it nevertheless is in a near perfect state.
Its painted lines appear to have been done not but the day before, and the asphalt must have been laid sometime in the last month, at the very least. And, yet, not a car uses it.
The road bends through the countryside, meandering past small provincial towns, their villagers working fields and engaging in communal farm life. None of them use automobiles.
If one of those people ever were to look up from their job, had they found a vehicle and were driving along the road, traveling quite a ways before they ever found anything, for the two cities that they live between are far away.
They would, after a time, with the sun setting behind them, eventually come to find something. In the middle of nowhere, and for no apparent reason, a grand monument is dedicated by the side of the road.
It’s a horrible thing: a great bronze slab. It has equal proportions, maybe eight feet tall and twelve feet or so by length and width, with small details along the trim. Nothing sits atop it.
The following could be read when looking at the inscription chiseled into a plate on one of its sides:
“Gaze upon my work and despair!”
This monument to nothing, for someone, was everything. When he was done he looked upon his world, and it was good.
