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.

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.

A Systematic Approach for Aligning ODE and Agent-Based Models: Application to Epidemiological Modeling

2008 ◽  
Author(s):  
Michael J. Leamy
Keyword(s):  
Autonomous Agents ◽  
Systematic Approach ◽  
Agent Based Modeling ◽  
Agent Based Models ◽  
Ode Model ◽  
Epidemiological Modeling ◽  
Agent Based ◽  
Agent Behavior ◽  
Time Histories ◽  
Good Agreement

Recent researchers active in the field of agent-based modeling have called for the alignment, or ‘docking,’ of models which simulate the same system using different techniques. Addressing this need, the present article details a systematic approach for docking models described by (nonlinear) ordinary differential equations with analogous models employing autonomous agents — i.e., agent-based models (ABMs). In particular, the approach is demonstrated by example for an epidemiological SEIR (Susceptible, Exposed, Infectious, Recovered) ODE model with a newly-developed agent-based model. The ABM is designed such that the assumptions present in the ODE model are matched by the actions of the ABM agents and the model. In addition, less-than-transparent coefficients present in the ODE model are examined via difference equations and then mapped to appropriate agent behavior. The result is very good agreement in comparisons made between ODE and ABM model-generated time-histories — i.e., successful alignment. It is anticipated that the systematic alignment approach described herein should be useful for aligning ODE and ABM models in other fields of study — e.g., Lanchester ODE combat models vice ABM combat models.

Dynamical Models in Geology: Sensitivity Analysis and Scientific Risk

1993 ◽  
Vol 11 (3-4) ◽  
pp. 329-356 ◽  
Author(s):  
Rene O. Thomsen
Keyword(s):  
Decision Making ◽  
Sensitivity Analysis ◽  
Mathematical Models ◽  
Systematic Approach ◽  
Probabilistic Approach ◽  
Sensitivity Analyses ◽  
System Response ◽  
Dynamical Models ◽  
Fundamental Limitations ◽  
Probabilistic Evaluation

Dynamical models are used routinely throughout various branches of geology and decisions are often based on the results of such models. Awareness of some fundamental limitations of any model used is therefore vital in order to avoid decision making based on highly uncertain results. Computer models are based on mathematical models which describe essential features of processes or system behaviour and a systematic approach to investigate and understand model limitations can therefore be applied. Sensitivity analysis provides a feel for the system response to uncertainties in both assumptions and observations. However, sensitivity analyses do not provide a feel for the level of confidence one should assign modelling results. A probabilistic approach to evaluation of uncertainties and modelling results is therefore demonstrated. Two cases are used as examples for the method and it is demonstrated how the combined sensitivity analyses and probabilistic evaluation greatly improve the use of even uncertain modelling results.

scholarly journals Using Uncertainty and Sensitivity Analyses in Socioecological Agent-Based Models to Improve Their Analytical Performance and Policy Relevance

PLoS ONE ◽  
2014 ◽  
Vol 9 (10) ◽  
pp. e109779 ◽  
Author(s):  
Arika Ligmann-Zielinska ◽  
Daniel B. Kramer ◽  
Kendra Spence Cheruvelil ◽  
Patricia A. Soranno
Keyword(s):  
Analytical Performance ◽  
Sensitivity Analyses ◽  
Agent Based Models ◽  
Agent Based ◽  
Policy Relevance

scholarly journals ‘One Size Does Not Fit All’: A Roadmap of Purpose-Driven Mixed-Method Pathways for Sensitivity Analysis of Agent-Based Models

2020 ◽  
Vol 23 (1) ◽  
Author(s):  
Arika Ligmann-Zielinska ◽  
Peer-Olaf Siebers ◽  
Nicholas Magliocca ◽  
Dawn C. Parker ◽  
Volker Grimm ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Mixed Method ◽  
Agent Based Models ◽  
Agent Based
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