scholarly journals Towards a Physiological Scale of Vocal Fold Agent-Based Models of Surgical Injury and Repair: Sensitivity Analysis, Calibration and Verification

2019 ◽  
Vol 9 (15) ◽  
pp. 2974 ◽  
Author(s):  
Aman Garg ◽  
Samson Yuen ◽  
Nuttiiya Seekhao ◽  
Grace Yu ◽  
Jeannie Karwowski ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Confidence Intervals ◽  
Empirical Data ◽  
Vocal Fold ◽  
Cell Populations ◽  
Model Parameters ◽  
Agent Based Models ◽  
Agent Based ◽  
Injury And Repair ◽  
Surgical Injury

Agent based models (ABM) were developed to numerically simulate the biological response to surgical vocal fold injury and repair at the physiological level. This study aimed to improve the representation of existing ABM through a combination of empirical and computational experiments. Empirical data of vocal fold cell populations including neutrophils, macrophages and fibroblasts were obtained using flow cytometry up to four weeks following surgical injury. Random Forests were used as a sensitivity analysis method to identify model parameters that were most influential to ABM outputs. Statistical Parameter Optimization Tool for Python was used to calibrate those parameter values to match the ABM-simulation data with the corresponding empirical data from Day 1 to Day 5 following surgery. Model performance was evaluated by verifying if the empirical data fell within the 95% confidence intervals of ABM outputs of cell quantities at Day 7, Week 2 and Week 4. For Day 7, all empirical data were within the ABM output ranges. The trends of ABM-simulated cell populations were also qualitatively comparable to those of the empirical data beyond Day 7. Exact values, however, fell outside of the 95% statistical confidence intervals. Parameters related to fibroblast proliferation were indicative to the ABM-simulation of fibroblast dynamics in final stages of wound healing.

scholarly journals Sensitivity analysis of agent-based models: a new protocol

2022 ◽  
Author(s):  
Emanuele Borgonovo ◽  
Marco Pangallo ◽  
Jan Rivkin ◽  
Leonardo Rizzo ◽  
Nicolaj Siggelkow
Keyword(s):  
Sensitivity Analysis ◽  
Factorial Design ◽  
Conditional Expectation ◽  
Systematic Approach ◽  
Sensitivity Analyses ◽  
Agent Based Models ◽  
Agent Based ◽  
Garbage Can Model ◽  
Stochastic Nature ◽  
Common Goals

AbstractAgent-based models (ABMs) are increasingly used in the management sciences. Though useful, ABMs are often critiqued: it is hard to discern why they produce the results they do and whether other assumptions would yield similar results. To help researchers address such critiques, we propose a systematic approach to conducting sensitivity analyses of ABMs. Our approach deals with a feature that can complicate sensitivity analyses: most ABMs include important non-parametric elements, while most sensitivity analysis methods are designed for parametric elements only. The approach moves from charting out the elements of an ABM through identifying the goal of the sensitivity analysis to specifying a method for the analysis. We focus on four common goals of sensitivity analysis: determining whether results are robust, which elements have the greatest impact on outcomes, how elements interact to shape outcomes, and which direction outcomes move when elements change. For the first three goals, we suggest a combination of randomized finite change indices calculation through a factorial design. For direction of change, we propose a modification of individual conditional expectation (ICE) plots to account for the stochastic nature of the ABM response. We illustrate our approach using the Garbage Can Model, a classic ABM that examines how organizations make decisions.

scholarly journals Book Review: "Uncertainty and sensitivity analysis in archaeological computational modeling" edited by Marieka Briuwer Burg, Hans Peeters, William A. Lovis

2017 ◽  
Author(s):  
Ben Marwick
Keyword(s):  
Land Use ◽  
Sensitivity Analysis ◽  
Computational Modeling ◽  
Cultural Transmission ◽  
Agent Based Models ◽  
Agent Based ◽  
Sources Of Uncertainty ◽  
Uncertainty And Sensitivity Analysis ◽  
Archaeological Models

This volume is collection of papers emerging from a forum at the 2014 SAA meetings. The papers are motivated by the question of how we can measure and interpret uncertainty in quantitative archaeological models, specifically by using sensitivity analysis. The types of models discussed in this volume include geo-referenced models of past environments to infer hunter-gather land use, and agent-based models of cultural transmission processes. They explore various sources of uncertainty, and implement sensitivity analysis by assessing how the output of the models varies according to changes in the inputs. The motivation for this collection is the editors' observations that archaeologists lack a discipline-based protocol for testing models.

Analytic Versus Computational Cognitive Models: Agent-Based Modeling as a Tool in Cognitive Sciences

2019 ◽  
Vol 28 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Jens Koed Madsen ◽  
Richard Bailey ◽  
Ernesto Carrella ◽  
Philipp Koralus
Keyword(s):  
Complex Systems ◽  
Closed Form ◽  
Model Development ◽  
Cognitive Models ◽  
System Level ◽  
Model Parameters ◽  
Social Psychological ◽  
Agent Based Models ◽  
Agent Based ◽  
Closed Form Solutions

Computational cognitive models typically focus on individual behavior in isolation. Models frequently employ closed-form solutions in which a state of the system can be computed if all parameters and functions are known. However, closed-form models are challenged when used to predict behaviors for dynamic, adaptive, and heterogeneous agents. Such systems are complex and typically cannot be predicted or explained by analytical solutions without application of significant simplifications. In addressing this problem, cognitive and social psychological sciences may profitably use agent-based models, which are widely employed to simulate complex systems. We show that these models can be used to explore how cognitive models scale in social networks to calibrate model parameters, to validate model predictions, and to engender model development. Agent-based models allow for controlled experiments of complex systems and can explore how changes in low-level parameters impact the behavior at a whole-system level. They can test predictions of cognitive models and may function as a bridge between individually and socially oriented models.

scholarly journals Behavioural synchronization in a multilevel society of feral horses

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258944
Author(s):  
Tamao Maeda ◽  
Cédric Sueur ◽  
Satoshi Hirata ◽  
Shinya Yamamoto
Keyword(s):  
Empirical Data ◽  
Group Living ◽  
Local Interaction ◽  
Agent Based Models ◽  
Feral Horses ◽  
Agent Based ◽  
Unit Level ◽  
Multilevel Society ◽  
Multi Agent ◽  
Living Organisms

Behavioural synchrony among individuals is essential for group-living organisms. The functioning of synchronization in a multilevel society, which is a nested assemblage of multiple social levels between many individuals, remains largely unknown. The aim of the present study was to build a model that explained the synchronization of activity in a multilevel society of feral horses. Multi-agent-based models were used based on four hypotheses: A) horses do not synchronize, B) horses synchronize with any individual in any unit, C) horses synchronize only within units, and D) horses synchronize across and within units, but internal synchronization is stronger. The empirical data obtained from drone observations best supported hypothesis D. This result suggests that animals in a multilevel society coordinate with other conspecifics not only within a unit but also at an inter-unit level. In this case, inter-individual distances are much longer than those in most previous models which only considered local interaction within a few body lengths.

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