Spatially-invariant opinion dynamics on the circle
September 19, 2024 Β· Declared Dead Β· π IEEE Control Systems Letters
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Giovanna Amorim, Anastasia Bizyaeva, Alessio Franci, Naomi Ehrich Leonard
arXiv ID
2409.12420
Category
math.AP
Cross-listed
cs.SI
Citations
5
Venue
IEEE Control Systems Letters
Last Checked
3 months ago
Abstract
We propose and analyze a nonlinear opinion dynamics model for an agent making decisions about a continuous distribution of options in the presence of input. Inspired by perceptual decision-making, we develop new theory for opinion formation in response to inputs about options distributed on the circle. Options on the circle can represent, e.g., the possible directions of perceived objects and resulting heading directions in planar robotic navigation problems. Interactions among options are encoded through a spatially invariant kernel, which we design to ensure that only a small (finite) subset of options can be favored over the continuum. We leverage the spatial invariance of the model linearization to design flexible, distributed opinion-forming behaviors using spatiotemporal frequency domain and bifurcation analysis. We illustrate our model's versatility with an application to robotic navigation in crowded spaces.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β math.AP
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Consistency of Lipschitz learning with infinite unlabeled data and finite labeled data
R.I.P.
π»
Ghosted
Properly-weighted graph Laplacian for semi-supervised learning
R.I.P.
π»
Ghosted
Quantum optimal transport is cheaper
R.I.P.
π»
Ghosted
Graph clustering, variational image segmentation methods and Hough transform scale detection for object measurement in images
R.I.P.
π»
Ghosted
The limit shape of convex hull peeling
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted