A Spatial Modeling Framework

2021 ◽  
pp. 54-69
Author(s):  
Erik Voeten
Keyword(s):  
Spatial Modeling ◽  
Spatial Model ◽  
Modeling Framework ◽  
Cooperation And Competition ◽  
Battle Of The Sexes ◽  
Ideological Space ◽  
Ideal Points ◽  
Distributive Conflict ◽  
Low Dimensional ◽  
Multiple Actors

This chapter proposes a simple spatial modeling framework to analyze how variations in interdependence and ideology shape incentives for cooperation and competition. The goal is to present a framework that is as simple as the prisoner's dilemma, coordination dilemma, battle of the sexes, and other two-by-two games that have served as mental models for rationalist analyses of cooperation. The spatial model easily accommodates multiple actors and distributive conflict and allows for analyses of how institutions structure choices. It starts from the assumption that actors have ideal points in a common low-dimensional ideological space. Yet their utilities are determined not just by their own policies but also by the policies of other actors. This interdependence creates incentives for cooperation. In this context, institutions may help actors achieve mutually beneficial outcomes, but they also have distributive implications. Institutions help shift policy status quos in particular directions.

Introduction

2021 ◽  
pp. 1-16
Author(s):  
Erik Voeten
Keyword(s):  
World Trade Organization ◽  
World Trade ◽  
Modeling Framework ◽  
Multilateral Institutions ◽  
The World ◽  
Ideological Space ◽  
Distributive Conflict ◽  
Low Dimensional ◽  
Institutional Order

This introductory chapter provides an overview of the book's argument that much, though not all, distributive conflict over multilateral institutions takes place in a low-dimensional ideological space. Even if distributive conflict over institutions is not always about ideology, the geopolitical implications often are. The point of this book is not just to argue that ideological contestation matters but also to offer measures, a modeling framework, and empirical illustrations. The theoretical framework helps in better understanding how institutional commitments hang together and may unravel together as challenges to the liberal institutional order mount. If multilateralism is distinct because it advances general principles, then one must understand challenges to the multilateral order in terms of domestic and international challenges to those principles. The chapter then presents a brief illustration of the World Trade Organization.

Spatial modeling of air pollution based on data streams from geosensor networks

2018 ◽  
Vol 930 (12) ◽  
pp. 39-43 ◽  
Author(s):  
V.P. Savinikh ◽  
A.A. Maiorov ◽  
A.V. Materuhin
Keyword(s):  
Air Pollution ◽  
Data Streams ◽  
Spatial Modeling ◽  
Spatial Model ◽  
Large City ◽  
Field Tests ◽  
Processing Unit ◽  
Geosensor Network ◽  
Spatio Temporal ◽  
Different Sources

The article is a brief summary of current research results of the authors in the field of spatial modeling of air pollution based on spatio-temporal data streams from geosensor networks. The urban environment is characterized by the presence of a large number of different sources of emissions and rapidly proceeding processes of contamination spread. So for the development of an adequate spatial model is required to make measurements with a large spatial and temporal resolution. It is shown that geosensor network provide researchers with the opportunity to obtain data with the necessary spatio-temporal detail. The article describes a prototype of a geosensor network to build a detailed spatial model of air pollution in a large city. To create a geosensor in the prototype of the system, calibrated gas sensors for a nitrogen dioxide and carbon monoxide concentrations measurement were interfaced to the module, which consist of processing unit and communication unit. At present, the authors of the article conduct field tests of the prototype developed.

scholarly journals Global distribution of modern shallow-water marine carbonate factories: a spatial model based on environmental parameters

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Marie Laugié ◽  
Julien Michel ◽  
Alexandre Pohl ◽  
Emmanuelle Poli ◽  
Jean Borgomano
Keyword(s):  
Spatial Analysis ◽  
Shallow Water ◽  
Spatial Model ◽  
General Circulation ◽  
Carbonate Reservoir ◽  
Global Distribution ◽  
Sea Surface ◽  
Modeling Framework ◽  
Model Based ◽  
Marine Carbonates

Abstract Prediction of carbonate distributions at a global scale through geological time represents a challenging scientific issue, which is critical for carbonate reservoir studies and the understanding of past and future climate changes. Such prediction is even more challenging because no numerical spatial model allows for the prediction of shallow-water marine carbonates in the Modern. This study proposes to fill this gap by providing for the first time a global quantitative model based on the identification of carbonate factories and associated environmental affinities. The relationships among the four carbonate factories, i.e., “biochemical”, “photozoan-T”, “photo-C” and “heterozoan-C” factories, and sea-surface oceanographic parameters (i.e., temperature, salinity and marine primary productivity) is first studied using spatial analysis. The sea-surface temperature seasonality is shown to be the dominant steering parameter discriminating the carbonate factories. Then, spatial analysis is used to calibrate different carbonate factory functions that predict oceanic zones favorable to specific carbonate factories. Our model allows the mapping of the global distribution of modern carbonate factories with an 82% accuracy. This modeling framework represents a powerful tool that can be adapted and coupled to general circulation models to predict the spatial distribution of past and future shallow-water marine carbonates.

Bayesian analysis of longitudinal and multidimensional functional data

Biostatistics ◽  
2020 ◽  
Author(s):  
John Shamshoian ◽  
Damla Şentürk ◽  
Shafali Jeste ◽  
Donatello Telesca
Keyword(s):  
Functional Data ◽  
Observational Studies ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Computationally Efficient ◽  
Modeling Framework ◽  
Simulated Environment ◽  
First Case ◽  
Low Dimensional

Summary Multi-dimensional functional data arises in numerous modern scientific experimental and observational studies. In this article, we focus on longitudinal functional data, a structured form of multidimensional functional data. Operating within a longitudinal functional framework we aim to capture low dimensional interpretable features. We propose a computationally efficient nonparametric Bayesian method to simultaneously smooth observed data, estimate conditional functional means and functional covariance surfaces. Statistical inference is based on Monte Carlo samples from the posterior measure through adaptive blocked Gibbs sampling. Several operative characteristics associated with the proposed modeling framework are assessed comparatively in a simulated environment. We illustrate the application of our work in two case studies. The first case study involves age-specific fertility collected over time for various countries. The second case study is an implicit learning experiment in children with autism spectrum disorder.

What You Seek Is Who You Are: An Applied Spatial Model of Newspapers’ Ideological Slant

2020 ◽  
pp. 194016122097317
Author(s):  
Luigi Curini
Keyword(s):  
News Media ◽  
Spatial Model ◽  
Spatial Theory ◽  
Optimal Placement ◽  
Crucial Step ◽  
Italian Case ◽  
Recent Developments ◽  
Ideological Space

In recent years, the news media landscape has been characterized by two distinct patterns: a decline in newspaper circulation, and a persistent degree of ideological slant in newspapers’ position. We explore a possible nexus between these two phenomena by means of a model that extends some recent developments in the empirical spatial theory of voting to the reader’s choice with respect to newspapers. We assume that ideological proximity to a newspaper affects the choice made by a consumer to read it. Newspapers will then compete among themselves to maximize their respective readerships by finding an optimal placement in the ideological space. However, newspapers can also decide to target readers of a specific type. As we will show, this is a crucial step to take into consideration. We empirically apply our model to the Italian case. We show that Italian newspapers appear largely to behave as theoretically expected. However, the “ideological force” behind this conclusion must be sought in newspapers’ competition with respect to that subset of readers which can be identified as regular ones. This result highlights a possible mechanism driving a persistent newspaper ideological slant in time of lower newspaper circulation.

PEMODELAN SPASIAL BAHAYA LONGSOR DI DAS CILIWUNG HULU, KABUPATEN BOGOR

2020 ◽  
Vol 17 (1) ◽  
pp. 311-318
Author(s):  
Muhamad Rizal Gojali ◽  
Boedi Tjahjono ◽  
Ernan Rustiadi
Keyword(s):  
Land Use ◽  
Spatial Modeling ◽  
Spatial Model ◽  
Assessment Method ◽  
Landslide Hazard ◽  
Natural Phenomenon ◽  
Hazard Class ◽  
Highest Weight ◽  
Landslide Hazards ◽  
High Level

Landslide is a natural phenomenon that occurs because nature is looking for a balance due to disturbance affecting the land at the point of the landslide. Bogor Regency is categorized into a medium to high level ground vulnerable zone by BNPB, in this case the Cilwung Hulu watershed is an area that often experiences landslides. This study aims to develop a spatial model of landslides in the Ciliwung Hulu watershed using a PCA-based assessment method of the factors causing landslides. The results showed that there are seven parameters that can be used for spatial modeling of landslides, namely landform, land use, slope, rainfall, straightness, soil type, and lithology. Based on the results of the analysis it was found that the weight of each parameter is 0.347; 0.223; 0,200; 0,100; 0.071; 0.049; and 0.010. In this case landform has the highest weight as a determinant of landslide hazards. The area of landslide hazard class (low, medium, and high) obtained from the results of modeling are 4,651.53 ha (31%), 6,637.72 ha (43%), and 3,941.41 ha (26%) with accuracy overall of 57.8.

The Impact of Voter Uncertainty and Alienation on Turnout and Candidate Policy Choice

2014 ◽  
Vol 14 (1) ◽  
pp. 273-292
Author(s):  
Natalya R. Brown
Keyword(s):  
Empirical Evidence ◽  
Voter Turnout ◽  
Spatial Model ◽  
Policy Preferences ◽  
Policy Choice ◽  
Formal Models ◽  
Positive Correlation ◽  
Policy Choices ◽  
Ideal Points ◽  
The Impact

AbstractFormal models of elections typically assume that voters are sure of their ideal points on the policy spectrum. Meanwhile, the empirical evidence suggests that voters are often uncertain about their ideal positions. In addition, alienation appears to play a key role in explaining voter turnout in elections. Using a spatial model that incorporates abstentions and the concepts of alienation and tolerance, I show that a positive correlation between extreme policy preferences and certainty among voters can affect voter turnout and result in the divergence of candidate policy choices.

scholarly journals MCell-R: A Particle-Resolution Network-Free Spatial Modeling Framework

2019 ◽  
pp. 203-229 ◽  
Author(s):  
Jose-Juan Tapia ◽  
Ali Sinan Saglam ◽  
Jacob Czech ◽  
Robert Kuczewski ◽  
Thomas M. Bartol ◽  
...  
Keyword(s):  
Spatial Modeling ◽  
Modeling Framework

A Spatial Modeling Framework for Siting Critical Supply Infrastructures

2010 ◽  
Vol 62 (3) ◽  
pp. 426-441 ◽  
Author(s):  
Paul J. Maliszewski ◽  
Mark W. Horner
Keyword(s):  
Spatial Modeling ◽  
Modeling Framework

scholarly journals Bayesian Decoder Models with a Discriminative Observation Process

2020 ◽  
Author(s):  
Mohammad R. Rezaei ◽  
Alex E. Hadjinicolaou ◽  
Sydney S. Cash ◽  
Uri T. Eden ◽  
Ali Yousefi
Keyword(s):  
State Space ◽  
Neural Activity ◽  
Model Development ◽  
High Dimensional ◽  
Modeling Framework ◽  
Movement Trajectories ◽  
State Process ◽  
Observation Process ◽  
Space Modeling ◽  
Low Dimensional

AbstractThe Bayesian state-space neural encoder-decoder modeling framework is an established solution to reveal how changes in brain dynamics encode physiological covariates like movement or cognition. Although the framework is increasingly being applied to progress the field of neuroscience, its application to modeling high-dimensional neural data continues to be a challenge. Here, we propose a novel solution that avoids the complexity of encoder models that characterize high-dimensional data as a function of the underlying state processes. We build a discriminative model to estimate state processes as a function of current and previous observations of neural activity. We then develop the filter and parameter estimation solutions for this new class of state-space modeling framework called the “direct decoder” model. We apply the model to decode movement trajectories of a rat in a W-shaped maze from the ensemble spiking activity of place cells and achieve comparable performance to modern decoding solutions, without needing an encoding step in the model development. We further demonstrate how a dynamical auto-encoder can be built using the direct decoder model; here, the underlying state process links the high-dimensional neural activity to the behavioral readout. The dynamical auto-encoder can optimally estimate the low-dimensional dynamical manifold which represents the relationship between brain and behavior.

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