arXiv subjects

Chainarong Amornbunchornvej

Publications and source records attributed to Chainarong Amornbunchornvej.

At least 19 recordsLinked to original sources

arXiv2026

Collective Counterfactual Planning: Coordination, Consent, and Verification under Representational Constraints

Groups routinely complete projects that no single member can plan, execute, or verify alone. We propose a formal model of this phenomenon, Collective Counterfactual Planning (CCP), in which the binding limitation on each agent is neither capability, knowledge, nor observability, but representational geometry: each agent perceives the state, conceives moves, consents to actions, and certifies goal requirements only through a projection onto an agent-specific subspace of a common task space. Four gates jointly determine whether a team can reach a conjunctive goal and legitimately recognize that it has done so: the exogenous implementation coalitions required to perform each action, together with three representational gates -- conception, consent, and task-relative verification qualification. We define the Collective Counterfactual Solvability (CCS) problem, separating geometric feasibility, executable attainment, and validated completion. The results expose a positive-negative duality. Iterated cross-agent relay can unlock a solution that no one-shot pooling of individual plans contains, but any goal requirement depending essentially on the subspace dark to the entire team is unverifiable and therefore not validly completable, even when the trajectory accidentally attains it. Memoryless and audited consent further constrain different objects -- action directions versus cumulative trajectory states -- and neither dominates the other. A four-step exhaustive horizon-bounded solvability scheme is sound and complete under exact representation of the relay closure; restricted implementations remain sound on returned plans but need not be complete. The model gives one geometry for sequential mutual enabling, competent execution of steps whose purpose is invisible to the executor, forced sub-teaming at expertise boundaries, and completion that cannot be validly declared.

cs.MA↗

arXiv2026

LGTD: Local-Global Trend Decomposition for Season-Length-Free Time Series Analysis

Time series decomposition into trend, seasonal, and residual components is a fundamental primitive in data mining and analytics pipelines, underpinning anomaly detection, change-point analysis, and forecasting. Most existing methods require a user-specified or estimated season length and assume stable periodic structure. In large, heterogeneous collections, where recurring patterns drift, appear intermittently, or operate at multiple nonstationary scales, period selection becomes brittle and per-series tuning does not scale. We propose LGTD (Local-Global Trend Decomposition), a season-length-free decomposition framework that requires no period specification and operates with a single fixed default configuration across datasets. LGTD represents a series as the sum of (i) a smooth global trend capturing long-term evolution, (ii) adaptive local trends inferred by an error-driven local linear segmentation procedure, and (iii) a residual component. Rather than modeling seasonality through an explicit periodic basis, LGTD treats it as an emergent property arising from the recurrence of local trend regimes, decoupling decomposition quality from any estimated season length. We prove that the local trend inference procedure terminates in a bounded number of iterations and runs in linear time in the series length, independent of any seasonal parameter, and confirm this empirically: LGTD scales linearly in runtime and memory and is the fastest method across all tested lengths, while several baselines degrade super-linearly. On synthetic benchmarks LGTD achieves balanced accuracy across fixed, transitive, and variable season-length regimes, particularly where period-based methods degrade, and on real-world data it yields interpretable components and low-structure residuals. Source code and datasets are available at https://github.com/chotanansub/LGTD.

cs.DB↗

arXiv2026

Interpretation as Linear Transformation: A Cognitive-Geometric Model of Concepts and Meaning

This paper develops a geometric framework for modeling concepts, motivation, and influence across cognitively heterogeneous agents. Each agent is represented by a personalized value space, a vector space encoding the internal dimensions through which the agent interprets and evaluates meaning. Evaluative concepts are formalized as structured vectors, abstract beings, whose transmission is mediated by linear interpretation maps. An abstract being survives communication only if it avoids the null spaces of these maps, yielding a structural criterion for intelligibility, miscommunication, and concept death. Within this framework, I show how conceptual distortion, motivational drift, and the limits of mutual understanding arise from purely algebraic constraints. A central result, the No-Null-Space Leadership Condition, characterizes leadership as a property of representational reachability rather than persuasion or authority. More broadly, the model explains how abstract beings can propagate, mutate, or disappear as they traverse diverse cognitive geometries. The account unifies insights from conceptual spaces, social epistemology, and AI value alignment by grounding meaning preservation in structural compatibility rather than shared information or rationality. I argue that this cognitive-geometric perspective clarifies the epistemic boundaries of influence in both human and artificial systems, and offers a general foundation for analyzing conceptual dynamics across heterogeneous agents.

cs.AI↗

arXiv2026

Interpretation, Learning, and Empathy as One Constraint: A Residual-Adequacy Architecture with Accountable Abstention

An agent must act on the situation before it, learn what it cannot yet represent, and model other agents well enough to coordinate. These faculties are usually realized by separate mechanisms, yet they share a failure mode: the situation can exceed what the agent can currently represent, and the honest response is then a principled refusal that says what was missing. We develop a small cognitive architecture in which these limits arise from a single quantity. An Interpretation-Decision Unit (IDU) interprets a content vector through a family of regimes - local representational frames with private bases - and decides which actions it licenses; a scalar residual of the content against the active regimes' representational scope drives the unit. Low residual with a clean licensing emits an action; otherwise the unit re-interprets, attempts a description-length-justified expansion, or halts with a typed, witnessed terminal. We prove the unit is total and deterministic: for any content and fixed configuration it halts in finitely many bounded-cost steps with a unique terminal witness, so abstention carries its cause by construction. By binding the architecture's open parameters without changing its mechanics, the same residual-against-scope constraint recovers three documented phenomena at three scopes: the typology of not-knowing (typed abstention); a forced misunderstanding between agents, localized to one shared concept and invisible to the agent committing it (bounded empathy); and prerequisite dependence in learning derived from a bounded focus window rather than posited (developmental prerequisites). Each instantiation is worked for a natural and an artificial agent and states a falsifiable prediction, so one constraint can model limits in both human and machine cognition. The account contributes a unification and a notion of accountable abstention, typed and witnessed by construction.

q-bio.NC↗

arXiv2026

Modeling ICD-10 Morbidity and Multidimensional Poverty as a Spatial Network: Evidence from Thailand

Health and poverty in Thailand exhibit pronounced geographic structuring, yet the extent to which they operate as interconnected regional systems remains insufficiently understood. This study analyzes ICD-10 chapter-level morbidity and multidimensional poverty as outcomes embedded in a spatial interaction network. Interpreting Thailand's 76 provinces as nodes within a fixed-degree regional graph, we apply tools from spatial econometrics and social network analysis, including Moran's I, Local Indicators of Spatial Association (LISA), and Spatial Durbin Models (SDM), to assess spatial dependence and cross-provincial spillovers. Our findings reveal strong spatial clustering across multiple ICD-10 chapters, with persistent high-high morbidity zones, particularly for digestive, respiratory, musculoskeletal, and symptom-based diseases, emerging in well-defined regional belts. SDM estimates demonstrate that spillover effects from neighboring provinces frequently exceed the influence of local deprivation, especially for living-condition, health-access, accessibility, and poor-household indicators. These patterns are consistent with contagion and contextual influence processes well established in social network theory. By framing morbidity and poverty as interdependent attributes on a spatial network, this study contributes to the growing literature on structural diffusion, health inequality, and regional vulnerability. The results highlight the importance of coordinated policy interventions across provincial boundaries and demonstrate how network-based modeling can uncover the spatial dynamics of health and deprivation.

cs.SI↗

arXiv2025

Counterfactual Basis Extension and Representational Geometry: An MDL-Constrained Model of Conceptual Growth

Concept learning becomes possible only when existing representations fail to account for experience. Most models of learning and inference, however, presuppose a fixed representational basis within which belief updating occurs. In this paper, I address a prior question: under what structural conditions can the representational basis itself expand in a principled and selective way? I propose a geometric framework in which conceptual growth is modeled as admissible basis extension evaluated under a Minimum Description Length (MDL) criterion. Experience, whether externally observed or internally simulated, is represented as vectors relative to a current conceptual subspace. Residual components capture systematic representational failure, and candidate conceptual extensions are restricted to low-rank, admissible transformations. I show that any MDL-accepted extension can be chosen so that its novel directions lie entirely within the residual span induced by experience, while extensions orthogonal to this span strictly increase description length and are therefore rejected. This yields a conservative account of imagination and conceptual innovation. Internally generated counterfactual representations contribute to learning only insofar as they expose or amplify structured residual error, and cannot introduce arbitrary novelty. I further distinguish representational counterfactuals--counterfactuals over an agent's conceptual basis--from causal or value-level counterfactuals, and show how MDL provides a normative selection principle governing representational change. Overall, the framework characterizes conceptual development as an error-driven, geometry-constrained process of basis extension, clarifying both the role and the limits of imagination in learning and theory change.

cs.AI↗

arXiv2025

Multi-Band Variable-Lag Granger Causality: A Unified Framework for Causal Time Series Inference across Frequencies

Understanding causal relationships in time series is fundamental to many domains, including neuroscience, economics, and behavioral science. Granger causality is one of the well-known techniques for inferring causality in time series. Typically, Granger causality frameworks have a strong fix-lag assumption between cause and effect, which is often unrealistic in complex systems. While recent work on variable-lag Granger causality (VLGC) addresses this limitation by allowing a cause to influence an effect with different time lags at each time point, it fails to account for the fact that causal interactions may vary not only in time delay but also across frequency bands. For example, in brain signals, alpha-band activity may influence another region with a shorter delay than slower delta-band oscillations. In this work, we formalize Multi-Band Variable-Lag Granger Causality (MB-VLGC) and propose a novel framework that generalizes traditional VLGC by explicitly modeling frequency-dependent causal delays. We provide a formal definition of MB-VLGC, demonstrate its theoretical soundness, and propose an efficient inference pipeline. Extensive experiments across multiple domains demonstrate that our framework significantly outperforms existing methods on both synthetic and real-world datasets, confirming its broad applicability to any type of time series data. Code and datasets are publicly available.

cs.AI↗

arXiv2025

Reduced Efficiency in the Attentional Network During Distractor Suppression in Mild Cognitive Impairment

Mild Cognitive Impairment (MCI) is a critical transitional stage between normal cognitive aging and dementia, making its early detection essential. This study investigates the neural mechanisms of distractor suppression in MCI patients using EEG and behavioral data during an attention-cueing Eriksen flanker task. A cohort of 56 MCIs and 26 healthy controls (HCs) performed tasks with congruent and incongruent stimuli of varying saliency levels. During these tasks, EEG data were analyzed for alpha band coherence's functional connectivity, focusing on Global Efficiency (GE), while Reaction Time (RT) and Hit Rate (HR) were also collected. Our findings reveal significant interactions between congruency, saliency, and cognitive status on GE, RT, and HR. In HCs, congruent conditions resulted in higher GE (p = 0.0114, multivariate t-distribution correction, MVT), faster RTs (p < 0.0001, MVT), and higher HRs (p < 0.0001, MVT) compared to incongruent conditions. HCs also showed increased GE in salient conditions for incongruent trials (p = 0.0406, MVT). MCIs exhibited benefits from congruent conditions with shorter RTs and higher HRs (both p < 0.0001, MVT) compared to incongruent conditions but showed reduced adaptability in GE, with no significant GE differences between conditions. These results highlight the potential of alpha band coherence and GE as early markers for cognitive impairment. By integrating GE, RT, and HR, this study provides insights into the interplay between neural efficiency, processing speed, and task accuracy. This approach offers valuable insights into cognitive load management and interference effects, indicating benefits for interventions aimed at improving attentional control and processing speed in MCIs.

q-bio.NC↗

arXiv2025

From Features to Graphs: Exploring Graph Structures and Pairwise Interactions via GNNs

Feature interaction is crucial in predictive machine learning models, as it captures the relationships between features that influence model performance. In this work, we focus on pairwise interactions and investigate their importance in constructing feature graphs for Graph Neural Networks (GNNs). We leverage existing GNN models and tools to explore the relationship between feature graph structures and their effectiveness in modeling interactions. Through experiments on synthesized datasets, we uncover that edges between interacting features are important for enabling GNNs to model feature interactions effectively. We also observe that including non-interaction edges can act as noise, degrading model performance. Furthermore, we provide theoretical support for sparse feature graph selection using the Minimum Description Length (MDL) principle. We prove that feature graphs retaining only necessary interaction edges yield a more efficient and interpretable representation than complete graphs, aligning with Occam's Razor. Our findings offer both theoretical insights and practical guidelines for designing feature graphs that improve the performance and interpretability of GNN models.

cs.LG↗

arXiv2025

Inferring the Most Similar Variable-length Subsequences between Multidimensional Time Series

Finding the most similar subsequences between two multidimensional time series has many applications: e.g. capturing dependency in stock market or discovering coordinated movement of baboons. Considering one pattern occurring in one time series, we might be wondering whether the same pattern occurs in another time series with some distortion that might have a different length. Nevertheless, to the best of our knowledge, there is no efficient framework that deals with this problem yet. In this work, we propose an algorithm that provides the exact solution of finding the most similar multidimensional subsequences between time series where there is a difference in length both between time series and between subsequences. The algorithm is built based on theoretical guarantee of correctness and efficiency. The result in simulation datasets illustrated that our approach not just only provided correct solution, but it also utilized running time only quarter of time compared against the baseline approaches. In real-world datasets, it extracted the most similar subsequences even faster (up to 20 times faster against baseline methods) and provided insights regarding the situation in stock market and following relations of multidimensional time series of baboon movement. Our approach can be used for any time series. The code and datasets of this work are provided for the public use.

cs.LG↗

arXiv2024

Regional and spatial dependence of poverty factors in Thailand, and its use into Bayesian hierarchical regression analysis

Poverty is a serious issue that harms humanity progression. The simplest solution is to use one-shirt-size policy to alleviate it. Nevertheless, each region has its unique issues, which require a unique solution to solve them. In the aspect of spatial analysis, neighbor regions can provide useful information to analyze issues of a given region. In this work, we proposed inferred boundaries of regions of Thailand that can explain better the poverty dynamics, instead of the usual government administrative regions. The proposed regions maximize a trade-off between poverty-related features and geographical coherence. We use a spatial analysis together with Moran's cluster algorithms and Bayesian hierarchical regression models, with the potential of assist the implementation of the right policy to alleviate the poverty phenomenon. We found that all variables considered show a positive spatial autocorrelation. The results of analysis illustrate that 1) Northern, Northeastern Thailand, and in less extend Northcentral Thailand are the regions that require more attention in the aspect of poverty issues, 2) Northcentral, Northeastern, Northern and Southern Thailand present dramatically low levels of education, income and amount of savings contrasted with large cities such as Bangkok-Pattaya and Central Thailand, and 3) Bangkok-Pattaya is the only region whose average years of education is above 12 years, which corresponds (approx.) with a complete senior high school.

stat.ME↗

arXiv2024

Framework for Variable-lag Motif Following Relation Inference In Time Series using Matrix Profile analysis

Knowing who follows whom and what patterns they are following are crucial steps to understand collective behaviors (e.g. a group of human, a school of fish, or a stock market). Time series is one of resources that can be used to get insight regarding following relations. However, the concept of following patterns or motifs and the solution to find them in time series are not obvious. In this work, we formalize a concept of following motifs between two time series and present a framework to infer following patterns between two time series. The framework utilizes one of efficient and scalable methods to retrieve motifs from time series called the Matrix Profile Method. We compare our proposed framework with several baselines. The framework performs better than baselines in the simulation datasets. In the dataset of sound recording, the framework is able to retrieve the following motifs within a pair of time series that two singers sing following each other. In the cryptocurrency dataset, the framework is capable of capturing the following motifs within a pair of time series from two digital currencies, which implies that the values of one currency follow the values of another currency patterns. Our framework can be utilized in any field of time series to get insight regarding following patterns between time series.

cs.LG↗

arXiv2023

Income, education, and other poverty-related variables: a journey through Bayesian hierarchical models

One-shirt-size policy cannot handle poverty issues well since each area has its unique challenges, while having a custom-made policy for each area separately is unrealistic due to limitation of resources as well as having issues of ignoring dependencies of characteristics between different areas. In this work, we propose to use Bayesian hierarchical models which can potentially explain the data regarding income and other poverty-related variables in the multi-resolution governing structural data of Thailand. We discuss the journey of how we design each model from simple to more complex ones, estimate their performance in terms of variable explanation and complexity, discuss models' drawbacks, as well as propose the solutions to fix issues in the lens of Bayesian hierarchical models in order to get insight from data. We found that Bayesian hierarchical models performed better than both complete pooling (single policy) and no pooling models (custom-made policy). Additionally, by adding the year-of-education variable, the hierarchical model enriches its performance of variable explanation. We found that having a higher education level increases significantly the households' income for all the regions in Thailand. The impact of the region in the households' income is almost vanished when education level or years of education are considered. Therefore, education might have a mediation role between regions and the income. Our work can serve as a guideline for other countries that require the Bayesian hierarchical approach to model their variables and get insight from data.

stat.ME↗

arXiv2023

Framework for inferring empirical causal graphs from binary data to support multidimensional poverty analysis

Poverty is one of the fundamental issues that mankind faces. To solve poverty issues, one needs to know how severe the issue is. The Multidimensional Poverty Index (MPI) is a well-known approach that is used to measure a degree of poverty issues in a given area. To compute MPI, it requires information of MPI indicators, which are \textbf{binary variables} collecting by surveys, that represent different aspects of poverty such as lacking of education, health, living conditions, etc. Inferring impacts of MPI indicators on MPI index can be solved by using traditional regression methods. However, it is not obvious that whether solving one MPI indicator might resolve or cause more issues in other MPI indicators and there is no framework dedicating to infer empirical causal relations among MPI indicators. In this work, we propose a framework to infer causal relations on binary variables in poverty surveys. Our approach performed better than baseline methods in simulated datasets that we know ground truth as well as correctly found a causal relation in the Twin births dataset. In Thailand poverty survey dataset, the framework found a causal relation between smoking and alcohol drinking issues. We provide R CRAN package `BiCausality' that can be used in any binary variables beyond the poverty analysis context.

stat.ME↗

arXiv2023

Register-based Census in Thailand: a Case Study in Chachoengsao Province

The use of registers has been increasingly popular in the field of population census because of its advantages over the traditional census. While the traditional census requires a large amount of fieldwork and data collection, the registered-based census can rely on pre-existing administrative data. As a result, the register-based census can save both time and budget. Thailand explored a use of the register-based census in 2020. In this paper, the authors layout the methodology in an aspect of data preparation and integration as well as analyze data quality of the register-based census compared with the traditional census in Chachoengsao province, Thailand. In addition, we compared conceptual frameworks that are commonly used for a register-based census in several countries and the number of databases (a recent single database VS multiple databases) used to construct the register-based census. We found that using a conceptual framework that counts the number of populations based on the main census variables on a single recent database is better than using a framework that counts population who appears on many registers in term of overcoverage and data distribution regarding to sex. This provides the evidence of using one recent and complete database is sufficient for conducting the register-based census. The authors end up with recommendations for conducing the register-based census.

stat.ME↗

arXiv2021

Occupational Income Inequality of Thailand: A Case Study of Exploratory Data Analysis beyond Gini Coefficient

Income inequality is an important issue that has to be solved in order to make progress in our society. The study of income inequality is well received through the Gini coefficient, which is used to measure degrees of inequality in general. While this method is effective in several aspects, the Gini coefficient alone inevitably overlooks minority subpopulations (e.g. occupations) which results in missing undetected patterns of inequality in minority. In this study, the surveys of incomes and occupations from more than 12 millions households across Thailand have been analyzed by using both Gini coefficient and network densities of income domination networks to get insight regarding the degrees of general and occupational income inequality issues. The results show that, in agricultural provinces, there are less issues in both types of inequality (low Gini coefficients and network densities), while some non-agricultural provinces face an issue of occupational income inequality (high network densities) without any symptom of general income inequality (low Gini coefficients). Moreover, the results also illustrate the gaps of income inequality using estimation statistics, which not only support whether income inequality exists, but that we are also able to tell the magnitudes of income gaps among occupations. These results cannot be obtained via Gini coefficients alone. This work serves as a use case of analyzing income inequality from both general population and subpopulations perspectives that can be utilized in studies of other countries.

econ.GN↗

arXiv2021

mFLICA: An R package for Inferring Leadership of Coordination From Time Series

Leadership is a process that leaders influence followers to achieve collective goals. One of special cases of leadership is the coordinated pattern initiation. In this context, leaders are initiators who initiate coordinated patterns that everyone follows. Given a set of individual-multivariate time series of real numbers, the mFLICA package provides a framework for R users to infer coordination events within time series, initiators and followers of these coordination events, as well as dynamics of group merging and splitting. The mFLICA package also has a visualization function to make results of leadership inference more understandable. The package is available on Comprehensive R Archive Network (CRAN) at https://CRAN.R-project.org/package=mFLICA.

cs.SI↗

arXiv2021

Framework for Inferring Leadership Dynamics of Complex Movement from Time Series

Leadership plays a key role in social animals, including humans, decision-making and coalescence in coordinated activities such as hunting, migration, sport, diplomatic negotiation etc. In these coordinated activities, leadership is a process that organizes interactions among members to make a group achieve collective goals. Understanding initiation of coordinated activities allows scientists to gain more insight into social species behaviors. However, by using only time series of activities data, inferring leadership as manifested by the initiation of coordinated activities faces many challenging issues. First, coordinated activities are dynamic and are changing over time. Second, several different coordinated activities might occur simultaneously among subgroups. Third, there is no fundamental concept to describe these activities computationally. In this paper, we formalize Faction Initiator Inference Problem and propose a leadership inference framework as a solution of this problem. The framework makes no assumption about the characteristics of a leader or the parameters of the coordination process. The framework performs better than our non-trivial baseline in both simulated and biological datasets (schools of fish). Moreover, we demonstrate the application of our framework as a tool to study group merging and splitting dynamics on another biological dataset of trajectories of wild baboons. In addition, our problem formalization and framework enable opportunities for scientists to analyze coordinated activities and generate scientific hypotheses about collective behaviors that can be tested statistically and in the field.

cs.SI↗