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Executable Epistemology: The Structured Cognitive Loop as an Architecture of Intentional Understanding
Get PDFLarge language models exhibit intelligence without genuine epistemic understanding, revealing a fundamental philosophical gap: the absence of epistemic architecture. This paper introduces the Structured Cognitive Loop (SCL) as an executable epistemological framework for emergent intelligence.
Unlike traditional AI research that asks "what is intelligence?" (ontological), SCL asks "under what conditions does cognition emerge?" (epistemological). Situated within contemporary philosophy of mind and cognitive phenomenology, this framework bridges conceptual philosophy and implementable cognition. Drawing on process philosophy, enactive cognition, and extended mind theory, we reconceptualize intelligence not as a possessed property but as a performed process—a continuous loop of judgment, memory, control, action, and regulation.
SCL makes three interrelated contributions. First, it operationalizes philosophical insights into computationally interpretable structures, enabling what we term "executable epistemology"—philosophy as structural experiment. Second, it demonstrates that functional separation within cognitive architecture yields more coherent and interpretable behavior than monolithic prompt-based approaches, with empirical support from controlled agent evaluations. Third, it redefines the measure of intelligence: not representational accuracy but the capacity to reconstruct one's own epistemic state through intentional understanding.
This framework has implications across philosophy of mind, epistemology, and artificial intelligence. For philosophy of mind, it offers a new mode of engagement where theories of cognition can be enacted and tested. For AI, it grounds behavioral intelligence in epistemic structure rather than statistical regularity. For epistemology, it suggests that knowledge is best understood not as truth-possession but as continuous structural reconstruction within a phenomenologically coherent loop.
We situate SCL within debates on cognitive phenomenology, emergence, normativity, and intentionality, arguing that genuine progress requires not larger models but architectures that structurally realize cognitive science principles
The Typical Principle
Get PDFIf a proposition is typically true, given your evidence, then you should believe that proposition; or so I argue here. In particular, in this paper, I propose and defend a principle of rationality---call it the `Typical Principle'---which links rational belief to facts about what is typical. As I show, this principle avoids several problems that other, seemingly similar principles face. And as I show, in many cases, this principle implies the verdicts of the Principal Principle: so ultimately, the Typical Principle may be the more fundamental of the two
Qualification and explanation in the dynamical/geometrical debate
Get PDFWe consider the distinction between 'qualified' and 'unqualified' approaches introduced by Read (2020) in the context of the dynamical/geometrical debate. We show that one fruitful way in which to understand this distinction is in terms of what one takes the kinematically possible models of a given theory to represent; moreover, we show that the qualified/unqualified distinction is applicable not only to the geometrical approach (which is the case considered by Read (2020)), but also to the dynamical approach. Finally, having made these points, we connect them to other discussions of representation and of explanation in this corner of the literature
From Individuals to Persons in a Panarchic Social Ontology
Get PDFContemporary social theory and economics still take the “individual” as a basic ontological unit, even when they adopt systemic and emergentist vocabularies. I argue that this shared presupposition underlies both refined versions of methodological individualism and many of their holist critiques, and that it produces what I call an “emergence without ontology” of the individual. Drawing on complex systems theory, emergentist systemism, and recent work in social ontology, I develop a panarchic framework in which persons replace individuals as the basic units of analysis, understood as bio–social–ecological complex systems whose agency is an emergent property of nested configurations. I elaborate this view by reconstructing how the social sciences conceptualize the individual, showing why existing critiques fail to challenge its ontological status, and specifying how agency can be understood as a systemic property of person–systems. I close by outlining how this panarchic ontology reshapes debates on individualism and holism and reframes explanatory practice in social theory, economics, and public policy
How to Relate Major Transitions in Life and Cognition?
Get PDFAbstract. Recent innovative research has focused on major transitions in cognitive evolution, drawing from the existing literature on major transitions in the evolution of life. This prompts a careful examination of the distinctions and similarities between these two types of transitions. In this paper, I present four claims. First, a theoretically fruitful approach to understanding major evolutionary transitions (METs) in life is to conceptualize them as a set of objectively similar events, akin to a natural kind concept. Second, this framework allows for discussing major cognitive transitions (MCTs) while emphasizing that METs and MCTs represent two distinct subsets of possibility-expanding evolutionary events, each defined by different criteria. Third, the recent works of Barron et al. (2023) and Ginsburg and Jablonka (2019, 2021) serve as successful examples of applying a transition-oriented approach to cognitive evolution. Both provide coherent definitions of MCTs along with fine-grained explanations of these events in unique ways. Finally, drawing on the tradition of dialectical thinking, specifically the method of climbing down the ladder of abstraction, I argue that their contributions can be viewed as complementary rather than competing alternatives
Theory Choice in Epistemic Networks: Five ways to avoid premature convergence
Get PDFIn this article, we study difficult theory-choice situations, where division of cognitive labor is needed. Network epistemology models suggest that reducing connectivity is needed to prevent premature convergence on bad theories. We compare how network density, community size, strength of prior beliefs, adaptive learning methods, and weak ties influence epistemic outcomes, and show that reducing connectivity is only one possible way to improve collective epistemic accuracy. Our findings suggest that gains in accuracy often come at a high cost in resources used, which should be considered when results from network epistemology models are used in applied settings
The Challenge from Expert Experience. On the Role of Qualitative Methods in Phenomenology of Science
Get PDFPhenomenology of science is supposed to return to the things themselves by getting as close as possible to the level of scientific practice. In doing so, it engages with a broader landscape of scholarship on science—from sociology and STS to analytic philosophy—that likewise seeks to clarify the epistemic structures of scientific practice. What sets phenomenology apart, however, is its aim of faithfully describing the essential structures of expert experience—the very experience scientists undergo as they engage in their research—by means of a first-person perspective. This paper identifies a central methodological difficulty in this regard: the challenge of expert experience, namely the difficulty of accessing and describing experiences that require domain-specific expertise. While introducing qualitative methods into the phenomenological toolbox seems a promising route for addressing this difficulty, it brings with it its own set of challenges. Although, as I will argue, there is no straightforward solution to the challenge, a potential way forward lies in focusing more on the collaborative interactions between phenomenologists and scientists during interview-based inquiry, with the aim of fostering interactional expertise in Harry Collins’s sense of the term
Everettian chance in no uncertain terms
Get PDFThe current landscape of views on chance in the Everett interpretation is rocky. Everettians (Wallace 2012, Sebens and Carroll 2018, McQueen and Vaidman 2019) agree that chance should be derived using principles governing uncertain or partial belief, but they cannot agree on how. Critics (Baker 2007, Dawid and Thébault 2015, Mandolesi 2019) maintain that any such approach is circular. We smooth the landscape by shifting focus from what Everettians take to be uncertain to what they should think is certain: namely, the conditions under which branches are isolated. Our approach to isolation resolves the main tensions among the different Everettian chance derivations while clarifying how they avoid circularity
Biological Object as Real Patterns: Reconciling Processualism and Scientific Realism
Get PDFIn recent years, the philosophy of biology has undergone a significant shift known as the
‘processual turn,’ largely influenced by the works of John Dupré and his collaborators
(Dupré, 2013, 2020; Nicholson and Dupré, 2018; Nicholson, 2019). Processualism argues
that dynamic processes, rather than static objects, form the fundamental ontology of
biological reality. This perspective, however, appears to be in tension with scientific realism,
at least in its standard form, which is committed to the existence of discrete, mind-
independent entities as posited in scientific theories. To reconcile these perspectives, I
propose that effective realism, particularly the framework based on the real pattern account
(Ladyman and Ross 2007; Wallace 2010), offers a promising solution. According to this
account, real patterns are defined by their utility—they are patterns that are indispensable for
formulating useful generalizations, enabling us to explain and predict phenomena efficiently.
These patterns are objective features of the world, but their recognition depends on the stance
or perspective we adopt, which is shaped by our explanatory purposes and cognitive tools. By
understanding biological objects as patterns of processes—stable, emergent features within
the dynamic flux of underlying processes—we can maintain a (minimal) realist commitment
to the entities posited by scientific theories without abandoning the processualist ontology.
Drawing on the real pattern account, I argue that interpreting objects as patterns of processes
resolves the tension between processualism and scientific realism, affirming the reality of
biological objects while preserving the primacy of processes. Meanwhile, this paper extends
the application of the real patterns framework by demonstrating its utility in addressing
specific challenges in the philosophy of biology. In doing so, it highlights how this
Dennettian legacy continues to profoundly enhance our understanding of the fundamental
reality revealed by the sciences
Still no peace on the lattice
Get PDFThe idea of using lattice methods to provide a mathematically well-defined formulation of realistic effective quantum field theories (QFTs) and clarify their physical content has gained traction in the last decades. In this paper, I argue that this strategy faces a two-sided obstacle: realistic lattice QFTs are (i) too different from their effective continuum counterparts even at low energies to serve as their foundational proxies and (ii) far from reproducing all of their empirical and explanatory successes to replace them altogether. I briefly conclude with some lessons for the foundations of QFT