H.Wanhong

I am deeply interested in the Partially Observable Markov Decision Process (POMDP) model as developed in fields such as computational neuroscience and machine learning. Inspired by neuroscientific phenomena, this model captures how humans collect evidence, infer hidden states, and make decisions in an uncertain world.

Formally, the real world is assumed to be in a state ( s ), which evolves according to certain dynamics (( s \rightarrow s’ )). Based on observed phenomena (i.e., evidence), as well as the history of actions and inferred states, an agent estimates the true state of the world and forms a belief state ( b ). This belief state represents a probability distribution over possible true world states. Decisions are then made on the basis of this belief state ( b ), using a learnable policy to generate an action ( a ).

Citation: HUANG, W. (2026, January 20). [Book] Foundations of Quantitative Social Science Research. https://doi.org/10.17605/OSF.IO/EVR46

Epistemology of Measurement and Data Quality

Having established what we dataficate, namely entities and their associated states,
processes, properties, relations, and events, and how these phenomena are
represented in quantitative research, we now confront the epistemological
question: how do we know that our measurements validly capture what we claim
to measure? The transformation from lived phenomena to formal data always
involves interpretation. Every measurement embeds theoretical commitments,
every operationalization involves interpretive choices, every dataset reflects
decisions about what to include and exclude. This section examines the
epistemic foundations and quality criteria that distinguish rigorous
quantitative social science from mere data collection.

Measurement Theory and Measurement Types

Measurement is the systematic assignment of numbers or symbols to phenomena
according to rules that preserve meaningful relationships. This deceptively
simple definition conceals profound epistemological complexity. What makes
an assignment “systematic”? Which relationships count as “meaningful”? Who
decides the rules, and on what basis? These questions reveal measurement as an
inherently theoretical enterprise rather than a neutral recording of facts.

The dynamics of neural systems form geometric structures in phase space whose analysis offers potential for
identifying and predicting neurodynamics-related disorders such as epileptic seizures.
However, current analytical approaches predominantly focus on static geometric features, while the generative processes and dynamics underlying these structures can provide valuable information.

Yet current neurodynamical analysis frameworks seldom investigate the formal generative processes underlying phase space geometries.
Meanwhile, contemporary philosophy and modern physics (field theory, noncommutative geometry) increasingly indicate that
relational-ontological primitives provide powerful foundations for modeling generative dynamics. The potential value of relational ontology
as a generative basis for understanding neurodynamical geometry formation remains largely unexplored.

We seek a formally expressive, physics- and mathematics-grounded framework capable of describing both
neurodynamical geometries and their generative processes from a relational-ontological perspective.

We propose utilizing quantum field theory (QFT) as a formal language for describing neurodynamical
geometry and its formation. QFT offers three key advantages: (1) a relational ontology that is both
philosophically and physically expressive, (2) quantum-theoretic expressiveness for describing complex
geometry formation processes, and (3) a general mathematical language for geometric emergence. As a preliminary
investigation, we employ group field theory (GFT), a specific QFT framework that describes quantum geometry
emergence from symmetry and relations using algebraic groups as ontological primitives.

This work establishes a
constructivist and relational-ontological approach to neurodynamical analysis,
demonstrating how GFT’s formalism can capture both the generative dynamics and emergent geometric structures in neural phase spaces.
We provide a conceptual bridge between quantum geometric frameworks and neuroscience, opening pathways for processual understanding of neurodynamical disorders.

In the process of seeking self-subjectivity, humanity often requires the presence of an Other. The human being is an animal that witnesses its own subjectivity in the Other and witnesses its own existence in interaction with the Other.

We need to distinguish here between two concepts: “presence” and “being.” “Being” here represents metaphysical reality, while “presence” refers to the present manifestation of some metaphysical entity or its proxy.

Among all Others, there exists a special Other—the big Other. The big Other is the symbolic structure through which we confirm our own existence: perhaps the gaze of God, the objectivity of science, or the authority of law.

In the first period, this big Other was occupied by symbols such as the supreme deity. I believe this period can be called the “Period of Transcendent Divinity.”

After the Enlightenment, the transcendent God was gradually replaced by symbolic rationality. The symbolic rationality of natural and social sciences gradually came to occupy the position of the big Other.

The power of symbolic rationality is formidable—we cannot deny its force. Through symbolic rationality, humanity creates the order of the world, practices, creates, and comes to understand this world.

This is the second period, which I call the “Divinity of Symbolic Rationality.”

Symbolic rationality has long occupied the position of humanity’s big Other. It seemed that through symbols, humanity could achieve perfect order, access perfect knowledge, fully understand the subject itself, and create infinitely. The power of symbols has long caused people to overlook their inherent limitations—that they too are flawed and can never reach the Real.

Discussion Framework

“Future education” is a grand and expansive object. From a retrospective view of how human beings come to know the world and change the world, this process passes through at least three levels. The first is the ontological level, the level at which reality itself resides. In some philosophical frameworks, this level is referred to as the unknowable. In order to know such ontologies, we require epistemological frameworks, which generate knowledge about the world through interpretation and explanation. Epistemological frameworks function in a manner similar to language systems; indeed, they can be understood as representations of language systems themselves. Without them, things in reality remain unknown to us, akin to the claim that “there is nothing outside the text” (1967, Jacques Derrida). Something becomes known to us because there exist certain systems that produce meaning for it. Only then can we move from knowing the world to changing it at the third level, namely the level of practice.

In this article, we choose to discuss “future education” by reversing the above route. As Kant’s Copernican revolution demonstrates, we cannot directly access the thing-in-itself; we must begin with the conditions of possible experience. In our case, this condition can be understood as educational practice. Starting from the demands of practice, this article primarily focuses on United Nations Sustainable Development Goals 4 (Quality Education), 10 (Reduced Inequalities), and 9 (Industry, Innovation, and Infrastructure), and discusses how the involvement of artificial intelligence introduces both challenges and opportunities for these development goals.

Citation: HUANG, W. (2026, January 20). [Book] Foundations of Quantitative Social Science Research. https://doi.org/10.17605/OSF.IO/EVR46

2.1 The Definition of Data

Data is conceptualized differently across disciplines. In statistics, data are observations that can be analyzed to reveal patterns (Fisher, 1925). In computer science, data are discrete, machine-readable representations stored and processed algorithmically (Date, 2003). In the social sciences, data are empirical evidence collected through systematic methods to test hypotheses (King et al., 1994). Critical data studies scholars emphasize that data are not simply given but actively constructed through processes of selection, categorization, and measurement (Gitelman, 2013; Bowker & Star, 1999).

We adopt a phenomenological perspective: we live in a world of phenomena, the lived immediacy of experience. Data constitute an operational representation of these phenomena. This datafication process transforms the continuous, qualitative flux of experience into discrete, formalizable elements that can be manipulated, analyzed, and communicated. As Husserl argued, formal operations require idealization, the transformation of intuitive experience into exact, repeatable objects of thought (Husserl, 1970).

This definition lies in its recognition that phenomena in their immediacy are not directly operationable for systematic inquiry. Data provide a formal operational space, a symbolic domain where phenomena are re-presented in ways that enable knowledge production. This is not merely technical translation but ontological transformation: we move from the lifeworld (Lebenswelt) to a constructed space of measurable entities. What becomes data, and what resists datafication, is never neutral but reflects epistemic choices, power relations, and the limits of formalization itself (van Dijck, 2014).

Citation: HUANG, W. (2026, January 12). Anarchy, Nonlinearity, and Emergence in International Relations: A Spiking Neural Network Framework Approach. https://doi.org/10.17605/OSF.IO/XEKDA

Note: This is a preliminary research note documenting a research idea still in the feasibility assessment stage. Its purpose is to systematically organize a research concept that remains under evaluation.

Abstract

This study proposes an international relations modeling approach based on Quantum Spiking Neural Networks (QSNNs), combining the representational structures of quantum computation with event-driven neural dynamics. While existing systems theory and dynamical systems approaches have made important progress in international relations research, they face fundamental challenges when handling the discrete event-driven evolution processes that dominate international politics. These methods often require external time discretization or additional mechanisms to introduce events into models, making it difficult to naturally represent and evolve events as intrinsic constitutive elements of the dynamics.

Biological spiking neural networks, as event-driven dynamical system models, treat events themselves as the basic units for state updates and computational triggers, thereby providing a more natural modeling foundation for event sequences in international relations. To further enhance event representation capabilities within this framework, this study introduces quantum extensions to classical spiking neural networks, constructing a quantum spiking neural network framework. In this framework, each international event is encoded as a quantum state on the Bloch sphere, with phase and amplitude distributions characterizing differences between events, thus achieving continuous representation of event qualities while maintaining geometric structure. This quantum event representation formally enables researchers to distinguish events under different contexts and intensities, introduces theoretical physics tools for analyzing relational structures between states, and provides expressive space for characterizing superposition properties and path-dependent phenomena that may exist in international politics. Combined with event-driven computational architecture, QSNNs maintain computational efficiency when processing sparse international event data and provide a possible technical path for introducing advanced theoretical physics analysis methods into international relations modeling.

Furthermore, from a computational architecture perspective, spiking neural networks and their quantum extensions possess natural asynchronicity and distributed characteristics. No global clock or central control unit exists in the system; different actors update their own states only when events occur through local interactions. This centerless, asynchronously evolving computational structure bears formal structural similarity to the “anarchic conditions” commonly discussed in international relations research. This framework provides a possible research path for characterizing event propagation, feedback, and structural evolution under centerless interaction conditions at the computational model level.

The anticipated research contributions of this work include: a quantum-theoretical international relations modeling framework centered on events, methods for discovering dynamical equations, formalized analysis of international relations entities and structures, and exploratory tools for decision-making.

Citation: HUANG, W. (2026, January 12). Anarchy, Nonlinearity, and Emergence in International Relations: A Spiking Neural Network Framework Approach. https://doi.org/10.17605/OSF.IO/XEKDA

Abstract

This research proposes a novel approach to modeling international relations by leveraging Quantum Spiking Neural Networks (QSNNs), integrating quantum computing principles with event-based neural dynamics. While existing systems theory methods have provided valuable insights, they face critical limitations in representing the rich qualitative diversity of international events.

Traditional approaches treat events as scalar occurrences or categorical variables, unable to capture subtle gradations in event meaning, intensity, and strategic ambiguity. The QSNN framework addresses this through quantum state representation on the Bloch sphere: each event is encoded as a quantum state vector with unit norm but distinct phase and amplitude components, enabling continuous high-dimensional representation of event qualities while maintaining geometric structure.

This quantum encoding allows us to:

• Distinguish infinitely many event types via continuous phase space
• Apply quantum physics tools (geometric phases, entanglement, QFT) to IR
• Represent superposition-like political phenomena
• Model relational structures as quantum fields

Combined with event-based neural computation, QSNNs enable efficient processing of sparse IR event data while unlocking powerful theoretical physics toolkits.

Expected contributions include a quantum-theoretic paradigm for IR modeling, quantum-enhanced equation discovery, insights into entanglement structures in alliance networks, and policy simulation incorporating quantum interference effects.

Citation: HUANG, W. (2026, January 9). [Book] Foundations of Quantitative Social Science Research. https://doi.org/10.17605/OSF.IO/EVR46

PDF Source (PDF File Link)

This is a response to the question “What are the current barriers to unleashing society’s creativity and innovation? How can we remove these barriers to release the collective intelligence of society and form a world-class innovative society?” (URL: https://www.zhihu.com/question/4547080892/answer/1993820847903904975)