arXiv subjects

Yasuko Kawahata

Publications and source records attributed to Yasuko Kawahata.

At least 19 recordsLinked to original sources

arXiv2024

Socio-Physical Approach to Consensus Building and the Occurrence of Opinion Divisions Based on External Efficacy

The proliferation of public networks has enabled instantaneous and interactive communication that transcends temporal and spatial constraints. The vast amount of textual data on the Web has facilitated the study of quantitative analysis of public opinion, which could not be visualized before. In this paper, we propose a new theory of opinion dynamics. This theory is designed to explain consensus building and opinion splitting in opinion exchanges on social media such as Twitter. With the spread of public networks, immediate and interactive communication that transcends temporal and spatial constraints has become possible, and research is underway to quantitatively analyze the distribution of public opinion, which has not been visualized until now, using vast amounts of text data. In this paper, we propose a model based on the Like Bounded Confidence Model, which represents opinions as continuous quantities. However, the Bounded Confidence mModel assumes that people with different opinions move without regard to their opinions, rather than ignoring them. Furthermore, our theory modeled the phenomenon in such a way that it can incorporate and represent the effects of external external pressure and dependence on surrounding conditions. This paper is a revised version of a paper submitted in December 2018(Opinion Dynamics Theory for Analysis of Consensus Formation and Division of Opinion on the Internet).

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arXiv2024

Polemical Case Study of Opinion Dynamics:Patterns of Filter Bubbles in Non-Consensus, Rewire Phenomena

In this paper, we will review some of the issues that have been raised by opinion dynamics theory to date. In particular, we conducted a hypothesis-based simulation using a socio-physical approach regarding the filter bubble phenomenon that tends to occur under special conditions such as (1) Distance, (2) Time, and (3) Existence of strong opinion clusters in the barriers to consensus building (4) Place where opinions are not influenced In particular, this paper discusses the hypothesis and simulations of filter bubbles, in which opinions diverge or converge without reaching consensus under conditions in which non-consensus is likely to be emphasized. This article serves as an Appendix in "Case Study On Opinion Dynamics In Dyadic Risk On Possibilities Of Trust-Distrust Model."

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arXiv2024

Massive Case Study of Opinion Distribution in a Relationship with Mixed Trust and Distrust

The simulations in this paper are based on the theory of opinion dynamics, which incorporates both Opinion A and Opinion B, a case that is the inverse of Opinion A, in human relationships. It was confirmed that aspects of consensus building depend on the ratio of the trust coefficient to the distrust coefficient. In this study, the ratio of trust to distrust tended to vary like a phase transition around 55%, but we wanted to see if the same phenomenon could be confirmed in large-scale cases. In the previous case studies, this tendency has been observed from N = 300 to N = 1600 , and we will discuss the case of N = 10000 with N = 3000. By verifying the extent of the phenomenon on the social scale, we intend to consider simulation items for consensus building, such as consideration of the sensitivities of topics in online and offline opinion formation.

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arXiv2024

Discussion of the Effect of Inter-group Sub-groups Using a Consensus Model Incorporating External Effective or Immobile Magnetic Fields

Individuals belong to certain social groups in search of a sense of belonging, pride, stability, and significance. Perceiving the group to which one belongs as an "in-group" and other groups as "out-groups" often leads to harmful and discriminatory attitudes. In-group consciousness reinforces a sense of unity within the group and promotes commitment to group goals and problem solving. Identification with the in-group also shapes the social cognitive framework (norms, values, and beliefs) that determine group behavior. In fact, identification with an in-group often leads to prejudice, ethnocentrism, stereotyping, and discrimination, even in the absence of physical conflict or hostility. Social scientists have conducted thousands of empirical studies to elucidate the mechanisms behind these prejudices and discriminations and the social conflicts they generate. These studies are essential to understanding the processes by which group membership and self-categorization create prejudice and discrimination, which in turn lead to social conflict. However, there remain many unanswered questions about howin-groups and out-groups canmove beyond conflict to build harmony and avoid social conflict. According to existing research, it is difficult to establish harmonious relationships between in-groups and out-groups. This study proposes an approach using opinion dynamics theory and social simulation to examine these issues. We examine the possibility of simulating the movement of opinions between and within groups and applying the considerations to cases of social conflict. The model analyzes the severity of conflict within a society with two groups on the basis of intragroup and intergroup trust.

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arXiv2024

Pre-Construction of Opinion Dynamics Considering Structural Inequality: Interdisciplinary Analysis of Complex Social Stratification, Media Influence, and Functionalism

This study analyzes the role of meritocracy, media influence, and scheduled theory from multiple perspectives as mechanisms that maintain inequality in social classes. Social inequality exists in complex forms in the educational, media, and political spheres. The study focuses on how inequality in society is structured and reproduced and how the theory of scheduled harmony justifies this.

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arXiv2024

Detection Method for Social Subsets Consisting of Anti-Network Construction for Unilateral Preference Behavior on Directed Temporal Networks

In existing research, as an example of one-sided preference, a conversation structure in which a person who is assumed to be an adult mainly sends one-sided messages to a person who is assumed to be a minor was observed. If subgraphs composed based on such unilateral preferences could be automatically extracted from the network structure, it would be possible to automatically detect communication conducted based on specific motivations from a vast amount of conversation data. In this study, we construct a bottom-up method to detect subgraphs composed of unilateral preferences in Greedy, and discuss the subset of unilateral preferences detected in this simulation. (This paper is partially an attempt to utilize "Generative AI" and was written with educational intent. There are currently no plans for it to become a peer-reviewed paper.)

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arXiv2024

Deciphering Unilateral Communication Patterns in Directed Temporal Networks: Network Role Distribution Approach

In the vast expanse of online communication, identifying unilateral preference patterns can be pivotal in understanding and mitigating risks such as predatory behavior. This paper presents a comprehensive approach to dissect and visualize such patterns in social networks. Through the lens of a directed network model, we simulate a scenario where a predominant cluster 'A' disperses information unilaterally towards a much larger, but passive, cluster 'B', while being overseen by a vigilant cluster 'C', restricted by an information blocking cluster 'D', and countered by an alerting cluster 'E'. Incorporated into this study is a simulation framework that models the flow of information across a directed network comprising various clusters with distinct roles and communication behaviors. The simulation employs a dynamic system where clusters 'A' through 'E' interact over a series of time steps, with each cluster's activity shaped by both intrinsic message-generation rules and external media influences. By tracking the accumulated media influence on each cluster, we gain a nuanced understanding of the long-term effects of media on communication patterns. The results provide a window into the cyclical nature of influence and the propagation of information, with potential applications in detecting and mitigating unilateral communication patterns that could signal harmful activities such as online predation. This study, therefore, presents a comprehensive approach that combines network theory, simulation modeling, and dynamic media influence analysis to explore and understand the complexities of unilateral preference communication within social networks.This paper is partially an attempt to utilize "Generative AI" and was written with educational intent. There are currently no plans for it to become a peer-reviewed paper.

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arXiv2024

Phase Field Modeling in Social Media Dynamics:Simulation of Opinion Evolution with Feedback, Separation

This study introduces a new numerical model to simulate how information is comprehended and processed on social networks, using continuous "Phase Field Modeling" variables (phiA, phiB, phiC) to represent individual users' opinions. It captures the immediate and two-way nature of social media interactions, reproducing the spread and feedback of information. The model incorporates psychological and social factors like confirmation bias and opinion rigidity to analyze information processing and opinion development among users. It also explores the dynamics of opinion segregation and interaction in and out of filter bubbles, offering a quantitative view of opinion dynamics on platforms like social networking services (SNS). This approach combines theoretical models with real-world social network data to study the effects of information concentration on opinion formation and the phenome Phase Field Modeling of opinion polarization and echo chamber effects on SNS. This paper is partially an attempt to utilize "Generative AI" and was written with educational intent. There are currently no plans for it to become a peer-reviewed paper.

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arXiv2024

Perspective in Opinion Dynamics on Complex Convex Domains of Time Networks for Addiction, Forgetting

This paper revises previous work and introduces changes in spatio-temporal scales. The paper presents a model that includes layers A and B with varying degrees of forgetting and dependence over time. We also model changes in dependence and forgetting in layers A, A', B, and B' under certain conditions. In addition, to discuss the formation of opinion clusters that have reinforcing or obstructive behaviors of forgetting and dependence and are conservative or brainwashing or detoxifying and less prone to filter bubbling, new clusters C and D that recommend, obstruct, block, or incite forgetting and dependence over time are Introduction. This introduction allows us to test hypotheses regarding the expansion of opinions in two dimensions over time and space, the state of development of opinion space, and the expansion of public opinion. Challenges in consensus building will be highlighted, emphasizing the dynamic nature of opinions and the need to consider factors such as dissent, distrust, and media influence. The paper proposes an extended framework that incorporates trust, distrust, and media influence into the consensus building model. We introduce network analysis using dimerizing as a method to gain deeper insights. In this context, we discuss network clustering, media influence, and consensus building. The location and distribution of dimers will be analyzed to gain insight into the structure and dynamics of the network. Dimertiling has been applied in various fields other than network analysis, such as physics and sociology. The paper concludes by emphasizing the importance of diverse perspectives, network analysis, and influential entities in consensus building. It also introduces torus-based visualizations that aid in understanding complex network structures.

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arXiv2024

From Spin States to Socially Integrated Ising Models: Proposed Applications of Graph States, Stabilizer States, Toric States to Opinion Dynamics

Recent research has developed the Ising model from physics, especially statistical mechanics, and it plays an important role in quantum computing, especially quantum annealing and quantum Monte Carlo methods. The model has also been used in opinion dynamics as a powerful tool for simulating social interactions and opinion formation processes. Individual opinions and preferences correspond to spin states, and social pressure and communication dynamics are modeled through interactions between spins. Quantum computing makes it possible to efficiently simulate these interactions and analyze more complex social networks.Recent research has incorporated concepts from quantum information theory such as Graph State, Stabilizer State, and Surface Code (or Toric Code) into models of opinion dynamics. The incorporation of these concepts allows for a more detailed analysis of the process of opinion formation and the dynamics of social networks. The concepts lie at the intersection of graph theory and quantum theory, and the use of Graph State in opinion dynamics can represent the interdependence of opinions and networks of influence among individuals. It helps to represent the local stability of opinions and the mechanisms for correcting misunderstandings within a social network. It allows us to understand how individual opinions are subject to social pressures and cultural influences and how they change over time.Incorporating these quantum theory concepts into opinion dynamics allows for a deeper understanding of social interactions and opinion formation processes. Moreover, these concepts can provide new insights not only in the social sciences, but also in fields as diverse as political science, economics, marketing, and urban planning.

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arXiv2024

Convex Regions of Opinion Dynamics, Approaches to the Complexity of Binary Consensus with Reference to Addiction and Obliviousness: Integrated Dimer Model Perspective

The field of opinion dynamics has its roots in early research that applied methods from magnetic physics to gain insights into the formation of social opinions. A central challenge in this field lies in modeling how diverse opinions coexist and exert influence on each other. In the realm of social issues, it's In this study, we leverage the dimer construct and the dimer model to establish a theoretical framework. Through numerical simulations, we demonstrate how this proposed model can be applied to real-world scenarios of social opinion formation. The model involves the computation of the Castellain matrix (K), the distribution function (Z), and the probability of dimer configuration (P(D)) for convex regions with varying positions and distances. It explores how alterations in convex regions impact the probability of dimer configuration. Furthermore, our model takes into account two critical factors: "dependence" and "forgetting" in the process of opinion formation. It also delves into the concepts of "distance" and "location" of opinions. The results of numerical simulations shed light on how our model effectively captures the processes involved in real-world social opinion formation. This study lays the groundwork for a deeper comprehension of the social opinion formation process and the development of strategies to address real-world social issues. In essence, our research introduces a novel methodology for comprehending and dissecting the intricate dynamics of opinion formation within the realm of opinion dynamics. This theoretical framework, coupled with a numerical simulation-based approach, offers fresh insights that extend beyond the confines of opinion dynamics. It opens up new perspectives in the domains of social science, physics, and computational modeling, ultimately contributing to a more profound understanding of social opinion formation.

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arXiv2024

The Anatomy Spread of Online Opinion Polarization: The Pivotal Role of Super-Spreaders in Social Networks

The study investigates the role of 'superspreaders' in shaping opinions within networks, distinguishing three types: A, B, and C. Type A has a significant influence in shaping opinions, Type B acts as a counterbalance to A, and Type C functions like media, providing an objective viewpoint and potentially regulating A and B's influence. The research uses a confidence coefficient and z-score to survey superspreaders' behaviors, with a focus on the conditions affecting group dynamics and opinion formation, including environmental factors and forgetfulness over time. The findings offer insights for improving online communication security and understanding social influence. This paper is partially an attempt to utilize "Generative AI" and was written with educational intent. There are currently no plans for it to become a peer-reviewed paper.

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arXiv2024

From Qubits to Opinions: Operator and Error Syndrome Measurement in Quantum-Inspired Social Simulations on Transversal Gates

This paper delves into the history and integration of quantum theory into areas such as opinion dynamics, decision theory, and game theory, offering a novel framework for social simulations. It introduces a quantum perspective for analyzing information transfer and decision-making complexity within social systems, employing a toric code-based method for error discrimination.Central to this research is the use of toric codes, originally for quantum error correction, to detect and correct errors in social simulations, representing uncertainty in opinion formation and decision-making processes. Operator and error syndrome measurement, vital in quantum computation, help identify and analyze errors and uncertainty in social simulations. The paper also discusses fault-tolerant computation employing transversal gates, which protect against errors during quantum computation. In social simulations, transversal gates model protection from external interference and misinformation, enhancing the fidelity of decision-making and strategy formation processes.

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arXiv2024

Social Echo Chambers in Quantum Field Theory: Exploring Faddeev-Popov Ghosts Phenomena, Loop Diagrams, and Cut-off Energy Theory

This paper presents an interdisciplinary approach to analyze the emergence and impact of filter bubbles in social phenomena, especially in both digital and offline environments, by applying the concepts of quantum field theory. Filter bubbles tend to occur in digital and offline environments, targeting digital natives with extremely low media literacy and information immunity. In addition, in the aftermath of stealth marketing, fake news, "inspirational marketing," and other forms of stealth marketing that never exist are rampant and can lead to major social disruption and exploitation. These are the causes of various social risks, including declining information literacy and knowledge levels and academic achievement. By exploring quantum mechanical principles such as remote interaction, proximity interaction, Feynman diagrams, and loop diagrams, we aim to gain a better understanding of information dissemination and opinion formation in social contexts. Our model incorporates key parameters such as agents' opinions, interaction probabilities, and flexibility in changing opinions, facilitating the observation of opinion distributions, cluster formation, and polarization under a variety of conditions. The purpose of this paper is to mathematically model the filter bubble phenomenon using the concepts of quantum field theory and to analyze its social consequences. This is a discussion paper and the proposed approach offers an innovative perspective for understanding social phenomena, but its interpretation and application require careful consideration. This paper is partially an attempt to utilize "Generative AI" and was written with educational intent. There are currently no plans for it to become a peer-reviewed paper.

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arXiv2024

Quantum Modeling of Filter Bubbles Based on Kubo-Matsubara Form Green's Functions Considering Remote and Proximity Interactions:Ultraviolet Divergence to Indefinite Ghosting, Consideration of Cut Surfaces

This research aims to model tracks the evolution of opinions among agents and their collective dynamics, and mathematically represents the resonance of opinions and echo chamber effects within the filter bubble by including non-physical factors such as misinformation and confirmation bias, known as FP ghosting phenomena.The indeterminate ghost phenomenon, a social science concept similar to the uncertainty principle, depicts the variability of social opinion by incorporating information uncertainty and nonlinearities in opinion formation into the model. Furthermore, by introducing the Kubo formula and the Matsubara form of the Green's function, we mathematically express temporal effects and model how past, present, and future opinions interact to reveal the mechanisms of opinion divergence and aggregation. Our model uses multiple parameters, including population density and extremes of opinion generated on a random number basis, to simulate the formation and growth of filter bubbles and their progression to ultraviolet divergence phenomena. In this process, we observe how resonance or disconnection of opinions within a society occurs via a disconnection function (type la, lb, ll, lll). However, the interpretation of the results requires careful consideration, and empirical verification is a future challenge.Finally, we will share our hypotheses and considerations for the model case of this paper, which is a close examination of regional differences in media coverage and its effectiveness and considerations unique to Japan, a disaster-prone country.

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arXiv2024

Approach to Toric Code Anyon Excitation, Indirect Effects of Kitaev Spin in Local Social Opinion Models

The study of Opinion Dynamics, which explores how individual opinions and beliefs evolve and how societal consensus is formed, has been examined across social science, physics, and mathematics. Historically based on statistical physics models like the Ising model, recent research integrates quantum information theory concepts, such as Graph States, Stabilizer States, and Toric Codes. These quantum approaches offer fresh perspectives for analyzing complex relationships and interactions in opinion formation, such as modeling local interactions, using topological features for error resistance, and applying quantum mechanics for deeper insights into opinion polarization and entanglement. However, these applications face challenges in complexity, interpretation, and empirical validation. Quantum concepts are abstract and not easily translated into social science contexts, and direct observation of social opinion processes differs significantly from quantum experiments, leading to a gap between theoretical models and real-world applicability. Despite its potential, the practical use of the Toric Code Hamiltonian in Opinion Dynamics requires further exploration and research.

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arXiv2024

Note: Evolutionary Game Theory Focus Informational Health: The Cocktail Party Effect Through Werewolfgame under Incomplete Information and ESS Search Method Using Expected Gains of Repeated Dilemmas

We explore the state of information disruption caused by the cocktail party effect within the framework of non-perfect information games and evolutive games with multiple werewolves. In particular, we mathematically model and analyze the effects on the gain of each strategy choice and the formation process of evolutionary stable strategies (ESS) under the assumption that the pollution risk of fake news is randomly assigned in the context of repeated dilemmas. We will develop the computational process in detail, starting with the construction of the gain matrix, modeling the evolutionary dynamics using the replicator equation, and identifying the ESS. In addition, numerical simulations will be performed to observe system behavior under different initial conditions and parameter settings to better understand the impact of the spread of fake news on strategy evolution. This research will provide theoretical insights into the complex issues of contemporary society regarding the authenticity of information and expand the range of applications of evolutionary game theory.This paper is partially an attempt to utilize "Generative AI" and was written with educational intent. There are currently no plans for it to become a peer-reviewed paper.

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arXiv2024

Entanglement: Balancing Punishment and Compensation, Repeated Dilemma Game-Theoretic Analysis of Maximum Compensation Problem for Bypass and Least Cost Paths in Fact-Checking, Case of Fake News with Weak Wallace's Law

This research note is organized with respect to a novel approach to solving problems related to the spread of fake news and effective fact-checking. Focusing on the least-cost routing problem, the discussion is organized with respect to the use of Metzler functions and Metzler matrices to model the dynamics of information propagation among news providers. With this approach, we designed a strategy to minimize the spread of fake news, which is detrimental to informational health, while at the same time maximizing the spread of credible information. In particular, through the punitive dominance problem and the maximum compensation problem, we developed and examined a path to reassess the incentives of news providers to act and to analyze their impact on the equilibrium of the information market. By applying the concept of entanglement to the context of information propagation, we shed light on the complexity of interactions among news providers and contribute to the formulation of more effective information management strategies. This study provides new theoretical and practical insights into issues related to fake news and fact-checking, and will be examined against improving informational health and public digital health.This paper is partially an attempt to utilize "Generative AI" and was written with educational intent. There are currently no plans for it to become a peer-reviewed paper.

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