arXiv subjects

Anders Karlström

Publications and source records attributed to Anders Karlström.

4 recordsLinked to original sources

arXiv2026

Information Geometry of Bounded Rationality: Entropy--Regularised Choice with Hyperbolic and Elliptic Quantum Geometries

Models of bounded rationality include quantum--like (QL) models, which use Hilbert--space amplitudes to represent context and order effects, and entropy--regularised (ER) models, including rational inattention, which smooth expected utility by adding an information cost. We develop a unified information--geometric framework in which both arise from the same structure on the probability simplex. Starting from the Fisher--Rao geometry of the open simplex $Δ^{n-1}$, we formulate \emph{least--action rationality} (LAR) as a variational principle for decision dynamics in amplitude (square--root) coordinates and lift it to the cotangent phase space $N:=T^*\mathbb R^n$ of unnormalised amplitudes. The lift carries its canonical symplectic form and a para--Kähler geometry. For a linear evaluator $\widehat V=\widehat S+\widehat F$ with symmetric part $\widehat S$ and skew part $\widehat F$, the dynamics separate an evaluative channel from a circulatory (co--utility) channel. On a distinguished zero--residual Lagrangian leaf the flow closes as a split--complex (hyperbolic) Schrödinger--type evolution, and observable probabilities follow from a quadratic (Born--type) normalisation. When reduced to the simplex, the induced preference one--form decomposes into an exact utility component and a divergence--free co--utility component whose curvature measures path dependence. Context effects, order effects, and interference--like deviations from the law of total probability emerge as projections of this latent rational flow. Finally, standard complex (elliptic) quantum dynamics arises within this real symplectic phase space by imposing an additional Kähler polarisation that restricts admissible variations. Unitary evolution is thus a coherent restriction of the underlying least--action framework rather than a primitive postulate.

econ.EM↗

arXiv2025

Workplace Location Choice Model based on Deep Neural Network

Discrete choice models (DCMs) have long been used to analyze workplace location decisions, but they face challenges in accurately mirroring individual decision-making processes. This paper presents a deep neural network (DNN) method for modeling workplace location choices, which aims to better understand complex decision patterns and provides better results than traditional discrete choice models (DCMs). The study demonstrates that DNNs show significant potential as a robust alternative to DCMs in this domain. While both models effectively replicate the impact of job opportunities on workplace location choices, the DNN outperforms the DCM in certain aspects. However, the DCM better aligns with data when assessing the influence of individual attributes on workplace distance. Notably, DCMs excel at shorter distances, while DNNs perform comparably to both data and DCMs for longer distances. These findings underscore the importance of selecting the appropriate model based on specific application requirements in workplace location choice analysis.

cs.LG↗

arXiv2025

Model-based traffic state estimation using camera-equipped probe vehicles

This study addresses the challenge of estimating traffic states for road links. We propose an innovative approach that leverages partial trajectory data captured by camera-equipped probe vehicles traveling in the opposite lane. The methodology combines state-of-the-art computer vision algorithms for extracting vehicle trajectories from street-view video sequences with a novel estimation technique based on the Cell Transmission Model (CTM) and Genetic Algorithms (GA). Our approach first calibrates Fundamental Diagram (FD) parameters using observed cell densities, then estimates boundary conditions for all space-time diagrams. We validate the method using simulated traffic data from three different types of links and parameter settings. Results show that the proposed methodology can estimate traffic densities in unobserved regions, even with limited data availability. This research contributes to the field by introducing a cost-effective, high-resolution traffic data collection method and a robust estimation technique for comprehensive traffic state information. While the study shows promising results, it also identifies areas for improvement, including refining models, optimizing processes, and testing with real-world data to enhance accuracy and scalability.

eess.SP↗

arXiv2025

Population Synthesis using Incomplete Information

This paper presents a population synthesis model that utilizes the Wasserstein Generative-Adversarial Network (WGAN) for training on incomplete microsamples. By using a mask matrix to represent missing values, the study proposes a WGAN training algorithm that lets the model learn from a training dataset that has some missing information. The proposed method aims to address the challenge of missing information in microsamples on one or more attributes due to privacy concerns or data collection constraints. The paper contrasts WGAN models trained on incomplete microsamples with those trained on complete microsamples, creating a synthetic population. We conducted a series of evaluations of the proposed method using a Swedish national travel survey. We validate the efficacy of the proposed method by generating synthetic populations from all the models and comparing them to the actual population dataset. The results from the experiments showed that the proposed methodology successfully generates synthetic data that closely resembles a model trained with complete data as well as the actual population. The paper contributes to the field by providing a robust solution for population synthesis with incomplete data, opening avenues for future research, and highlighting the potential of deep generative models in advancing population synthesis capabilities.

cs.LG↗