Which Sensitivity Analysis Method Should I Use for My Agent-Based Model?

Broeke, Guus ten; Voorn, George van; Ligtenberg, A.

doi:10.18564/jasss.2857

Cited by 175 publications

(142 citation statements)

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“…It is thus not well-suited for detecting tipping points. A local analysis can be extended as a one-factor-at-a-time (OFAT) analysis [36], in which the sensitivity of the model to a certain parameter is investigated by going stepwise through parameter space, where at each parameter value simulation is used to determine the model output. In effect this methodology can be considered as a discrete, brute force analogue to bifurcation analysis.…”

Section: Discussionmentioning

confidence: 99%

“…In this paper, we show that OFAT can reveal separatrices and transcritical bifurcations. In [36] it was shown that OFAT can be applied to detect tipping points in non-ODE models with a comparable level of success.…”

Section: Discussionmentioning

confidence: 99%

“…OFAT is not directly aimed at quantifying sensitivities, but is useful for revealing qualitative relations between individual parameters and the model output. OFAT can reveal whether the output changes linearly or non-linearly as a function of changes in a single model parameter, and thus can help to reveal whether tipping points are crossed as the parameter is changed [36].…”

Section: Local Sensitivity Analysismentioning

confidence: 99%

“…Ten Broeke et al [36] concluded that a one-factor-at-a-time approach is the best methodology for locating tipping points in ABMs. It is however a brute force method (i.e.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Detecting Tipping points in Ecological Models with Sensitivity Analysis

Broeke

Voorn

Kooi³

et al. 2016

Math. Model. Nat. Phenom.

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Abstract. Simulation models are commonly used to understand and predict the development of ecological systems, for instance to study the occurrence of tipping points and their possible ecological effects. Sensitivity analysis is a key tool in the study of model responses to changes in conditions. The applicability of available methodologies for sensitivity analysis can be problematic if tipping points are involved. In this paper we demonstrate that not considering these tipping points may result in misleading statistics on model behaviour. In turn, this limits the applicability of simulation models in ecological research. Tipping points are best revealed when asymptotic model behaviour is considered, i.e. by applying bifurcation analysis. Bifurcation analysis, however, is limited to deterministic dynamic models, whereas many ecological simulation models are nondeterministic and can only be analysed using sensitivity analysis methodologies. In this paper we explore the possibilities for applying methodologies of sensitivity analysis to analyse models with tipping points. The Bazykin-Berezovskaya model, a deterministic ecological model of which the structure regarding tipping points is known a priori, is used as case study. We conclude that important clues about the occurrence of tippings points can be revealed from different sensitivity analysis methodologies, if proper statistical and graphical measures are used. The results raise awareness about how tipping points affect temporal model responses in ecological simulation models, and may also be more generally applicable for nondeterministic models that cannot be analysed using bifurcation analysis.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Local Sensitivity Analysismentioning

confidence: 99%

“…Ten Broeke et al [36] concluded that a one-factor-at-a-time approach is the best methodology for locating tipping points in ABMs. It is however a brute force method (i.e.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Detecting Tipping points in Ecological Models with Sensitivity Analysis

Broeke

Voorn

Kooi³

et al. 2016

Math. Model. Nat. Phenom.

View full text Add to dashboard Cite

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“…In contrast, the model outputs can be quite robust against the variations in other parameters [30]. Several approaches to studying parameter sensitivity have been developed [31] to understand the features of agent-based modeling. Because we need to consider the impacts of many parameters, we chose global sensitivity analysis, which is based on the regression wherein random samples are drawn from the parameter space as introduced in Table 1.…”

Section: Parameter Sensitivitymentioning

confidence: 99%

On the Interdependence of the Financial Market and Open Access Spectrum Market in the 5G Network

et al. 2017

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Modern 5G networks offer a large space for innovation and a completely new approach to addressing network functioning. A fixed spectrum assignment policy is a significant limitation of today's wireless communication network practice and is to be replaced by a completely new approach called dynamic spectrum access (DSA). However, there is no general agreement on the organization of the DSA. Some studies suggest that open access market can be inspired by the electricity or financial markets. It allows to treat operators with region coverage as investors entering the market and trading the spectra on an on-demand basis. Because investors operate in both the financial markets and the markets for spectra, new interference between both markets emerges. Our paper shows how the risk-free rate of return stemming from the financial markets influences the techno-economic properties of the network. We show that, for low risk-free returns, the spectrum market becomes oversupplied, which keeps service prices very low and spectrum trading volumes large. In contrast, if risk-free returns are high, then spectrum trading volumes decline and the market becomes price sensitive; in other words, economic rules begin to work better.

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A Comprehensive Physical‐Based Sensitivity Analysis of the Electrochemical Impedance Response of Lithium‐Ion Batteries

et al. 2021

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The electrochemical impedance spectroscopy (EIS) characterization technique, although widely adopted in electrochemistry for understanding operational issues and degradation, has a less consolidated physical interpretation in lithium‐ion batteries (LIBs), often relying on circuital methods. Herein, the Doyle–Fuller–Newman model is adapted and experimentally validated for the physical simulation of electrochemical impedance; then, it is applied in a comprehensive one‐factor‐at‐time sensitivity analysis on an impedance spectrum from 4 kHz to 0.005 Hz; 28 physical parameters, which represent the kinetic, resistive, diffusive, and geometric characteristics of the battery, are varied within broad literature‐based ranges of values, for each of the 20 analyzed battery states, characterized by different state‐of‐charge and temperature values. The results show a miscellaneous sensitivity of parameters on impedance spectra, which ranges from highly sensitive to negligible, often resulting in a strong dependence on operating conditions and impedance frequency. Such results consolidate the understanding of LIB electrochemical impedance and demonstrate that 40% of the parameters, 12 out of 28, can be considered poorly sensitive or insensitive parameters; therefore, fitting the experimental EIS data, their value can be assumed from the literature without significantly losing accuracy.

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Which Sensitivity Analysis Method Should I Use for My Agent-Based Model?

Cited by 175 publications

References 0 publications

Detecting Tipping points in Ecological Models with Sensitivity Analysis

Detecting Tipping points in Ecological Models with Sensitivity Analysis

On the Interdependence of the Financial Market and Open Access Spectrum Market in the 5G Network

A Comprehensive Physical‐Based Sensitivity Analysis of the Electrochemical Impedance Response of Lithium‐Ion Batteries

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