ParaGlide: Interactive Parameter Space Partitioning for Computer Simulations

To model behavior, scientists need to know how models behave. This means learning what other behaviors a model can produce besides the one generated by participants in an experiment. This is a difficult problem because of the complexity of psychological models (e.g., their many parameters) and because the behavioral precision of models (e.g., interval-scale performance) often mismatches their testable precision in experiments, where qualitative, ordinal predictions are the norm. Parameter space partitioning is a solution that evaluates model performance at a qualitative level. There exists a partition on the model's parameter space that divides it into regions that correspond to each data pattern. Three application examples demonstrate its potential and versatility for studying the global behavior of psychological models.

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DataStorm: Coupled, Continuous Simulations for Complex Urban Environments

ACM/IMS Transactions on Data Science ◽

10.1145/3447572 ◽

2021 ◽

Vol 2 (3) ◽

pp. 1-37

Author(s):

Hans Walter Behrens ◽

K. Selçuk Candan ◽

Xilun Chen ◽

Yash Garg ◽

Mao-Lin Li ◽

...

Keyword(s):

Parameter Space ◽

Simulation Analysis ◽

Urban System ◽

Decision Criterion ◽

Urban Environments ◽

Parallel Execution ◽

Effective Density ◽

Urban Systems ◽

Space Partitioning ◽

Imperfect Knowledge

Urban systems are characterized by complexity and dynamicity. Data-driven simulations represent a promising approach in understanding and predicting complex dynamic processes in the presence of shifting demands of urban systems. Yet, today’s silo-based, de-coupled simulation engines fail to provide an end-to-end view of the complex urban system, preventing informed decision-making. In this article, we present DataStorm to support integration of existing simulation, analysis and visualization components into integrated workflows. DataStorm provides a flow engine, DataStorm-FE , for coordinating data and decision flows among multiple actors (each representing a model, analytic operation, or a decision criterion) and enables ensemble planning and optimization across cloud resources. DataStorm provides native support for simulation ensemble creation through parameter space sampling to decide which simulations to run, as well as distributed instantiation and parallel execution of simulation instances on cluster resources. Recognizing that simulation ensembles are inherently sparse relative to the potential parameter space, we also present a density-boosting partition-stitch sampling scheme to increase the effective density of the simulation ensemble through a sub-space partitioning scheme, complemented with an efficient stitching mechanism that leverages partial and imperfect knowledge from partial dynamical systems to effectively obtain a global view of the complex urban process being simulated.

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Social Influence of Competing Groups and Leaders in Opinion Dynamics

Computational Economics ◽

10.1007/s10614-020-10049-7 ◽

2020 ◽

Cited By ~ 1

Author(s):

Catherine A. Glass ◽

David H. Glass

Keyword(s):

Social Influence ◽

Group Size ◽

Computer Simulations ◽

Parameter Space ◽

Optimal Strategies ◽

Opinion Dynamics ◽

The Other ◽

Opinion Leaders ◽

The Impact

Abstract This paper explores the influence of two competing stubborn agent groups on the opinion dynamics of normal agents. Computer simulations are used to investigate the parameter space systematically in order to determine the impact of group size and extremeness on the dynamics and identify optimal strategies for maximizing numbers of followers and social influence. Results show that (a) there are many cases where a group that is neither too large nor too small and neither too extreme nor too central achieves the best outcome, (b) stubborn groups can have a moderating, rather than polarizing, effect on the society in a range of circumstances, and (c) small changes in parameters can lead to transitions from a state where one stubborn group attracts all the normal agents to a state where the other group does so. We also explore how these findings can be interpreted in terms of opinion leaders, truth, and campaigns.

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A High Resolution Study of G.P. Zones in Aluminum - 4.2 at % Silver

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100055904 ◽

1982 ◽

Vol 40 ◽

pp. 722-723 ◽

Cited By ~ 1

Author(s):

R. Gronsky

Keyword(s):

Electron Microscopy ◽

Transmission Electron Microscopy ◽

High Resolution ◽

Computer Simulations ◽

Structural Characteristics ◽

X Ray Diffraction ◽

X Ray ◽

Transmission Electron ◽

Resolution Study

The phenomenon of clustering in Al-Ag alloys has been extensively studied since the early work of Guinierl, wherein the pre-precipitation state was characterized as an assembly of spherical, ordered, silver-rich G.P. zones. Subsequent x-ray and TEM investigations yielded results in general agreement with this model. However, serious discrepancies were later revealed by the detailed x-ray diffraction - based computer simulations of Gragg and Cohen, i.e., the silver-rich clusters were instead octahedral in shape and fully disordered, atleast below 170°C. The object of the present investigation is to examine directly the structural characteristics of G.P. zones in Al-Ag by high resolution transmission electron microscopy.

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Multislice calculations for quasi-periodic and non-periodic objects

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100173820 ◽

1990 ◽

Vol 48 (4) ◽

pp. 138-139

Author(s):

R. Herrera ◽

A. Gómez

Keyword(s):

Electron Diffraction ◽

Computer Simulations ◽

Periodic Table ◽

Amorphous Materials ◽

Space Group ◽

Essential Step ◽

Shape Effects ◽

Atomic Scattering ◽

Diffraction Patterns ◽

Periodic Continuation

Computer simulations of electron diffraction patterns and images are an essential step in the process of structure and/or defect elucidation. So far most programs are designed to deal specifically with crystals, requiring frequently the space group as imput parameter. In such programs the deviations from perfect periodicity are dealt with by means of “periodic continuation”.However, for many applications involving amorphous materials, quasiperiodic materials or simply crystals with defects (including finite shape effects) it is convenient to have an algorithm capable of handling non-periodicity. Our program “HeGo” is an implementation of the well known multislice equations in which no periodicity assumption is made whatsoever. The salient features of our implementation are: 1) We made Gaussian fits to the atomic scattering factors for electrons covering the whole periodic table and the ranges [0-2]Å−1 and [2-6]Å−1.

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