Uncertainty quantification and global sensitivity analysis for economic models

Harenberg, Daniel; Marelli, Stefano; Sudret, Bruno; Winschel, Viktor

doi:10.3982/qe866

Cited by 31 publications

(12 citation statements)

References 81 publications

(99 reference statements)

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“…We assume that the input parameters except a and R are independent random variables. Due to the lack of knowledge on the specific distribution of most of the parameters, the most suitable probability distribution is the one which maximizes the continuous entropy ( 67 ), more precisely, the uniform distribution over the ranges defined in Table 1 . Therefore, the uncertainty in the parameter values is represented by uniform distributions for the parameters ( A , χ , D , A σ , γ , K ) and by log-normal distributions for the parameters a and R .…”

Section: Resultsmentioning

confidence: 99%

Analysis of the Equilibrium Phase in Immune-Controlled Tumors Provides Hints for Designing Better Strategies for Cancer Treatment

et al. 2022

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When it comes to improving cancer therapies, one challenge is to identify key biological parameters that prevent immune escape and maintain an equilibrium state characterized by a stable subclinical tumor mass, controlled by the immune cells. Based on a space and size structured partial differential equation model, we developed numerical methods that allow us to predict the shape of the equilibrium at low cost, without running simulations of the initial-boundary value problem. In turn, the computation of the equilibrium state allowed us to apply global sensitivity analysis methods that assess which and how parameters influence the residual tumor mass. This analysis reveals that the elimination rate of tumor cells by immune cells far exceeds the influence of the other parameters on the equilibrium size of the tumor. Moreover, combining parameters that sustain and strengthen the antitumor immune response also proves more efficient at maintaining the tumor in a long-lasting equilibrium state. Applied to the biological parameters that define each type of cancer, such numerical investigations can provide hints for the design and optimization of cancer treatments.

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Section: Resultsmentioning

confidence: 99%

Analysis of the Equilibrium Phase in Immune-Controlled Tumors Provides Hints for Designing Better Strategies for Cancer Treatment

et al. 2022

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“…This is possibly due to the loss of information caused by the uncertainty associated with each of the single runs in PCE (see Figure S1) and the errors associated with PCE approximation (Sobol, 1993). Though the loss of information is minor, they could still affect the estimation of the single effect of ρ (Harenberg et al, 2019). Both the results of the GSA and single factor sensitivity analysis suggest the central role of the population distribution in the performance and management of a reward-based crowdsourcing rainfall monitoring program.…”

Section: Water Resources Researchmentioning

confidence: 99%

Reward‐Based Participant Management for Crowdsourcing Rainfall Monitoring: An Agent‐Based Model Simulation

Yang

Cai

2019

Water Resources Research

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Crowdsourcing incorporates common citizens as rich sources of data and is promising for environmental monitoring. In this paper, we propose and test the idea of incorporating incentives to crowdsourcing management for rainfall monitoring. Specifically, we model the allocation of incentives (quantitatively measurable and limited rewards) among crowdsourcing participants for a theoretical rainfall monitoring case. For this purpose, we develop an integrated model comprising a reward allocation component to represent the decision‐making process of a central manager, an agent‐based model to simulate the interactions between the manager and participants, and a rainfall simulation model to evaluate the effectiveness of various reward allocation policies. We simulate six reward allocation policies of varying levels of administrative cost, and consideration of participant and rainfall spatial heterogeneities. The results suggest the performance of each policy to improve with the reward budget and their spatial uniformity. Among the six policies tested, we find that the participant density weighted maximum participation policy yields the most accurate estimation of rainfall intensity due to its more explicit consideration of the spatial distribution of participants; however, this policy associates with a high administrative cost. This highlights the trade‐off between performance and cost in designing effective reward allocation policies. This paper provides a physical and behavior simulation modeling tool to study the feasibility and complexity of reward‐based participant management for crowdsourcing rainfall monitoring. The proposed crowdsourcing method is beneficial for a wide range of applications that require rainfall data with fine resolution, such as storm water management and water availability and biomass assessment for food and energy crops.

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“…The Sobol' method (Sobol', 2001) is a classical way of doing SA and has been successfully employed in various application areas; see e.g., (Harenberg et al, 2019;Pianosi et al, 2016;Zhang et al, 2013). The Sobol' sensitivity indices benefit from several advantages including accuracy, clear interpretation and straightforward implementation (Burnaev et al, 2017).…”

Section: Sobol' Indicesmentioning

confidence: 99%

Variance-based global sensitivity analysis of numerical models using R

Mohammadi¹,

Challenor²,

Prieur³

2022

Preprint

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Sensitivity analysis plays an important role in the development of computer models/simulators through identifying the contribution of each (uncertain) input factor to the model output variability. This report investigates different aspects of the variance-based global sensitivity analysis in the context of complex black-box computer codes. The analysis is mainly conducted using two R packages, namely sensobol (Puy et al., 2021) and sensitivity (Iooss et al., 2021). While the package sensitivity is equipped with a rich set of methods to conduct sensitivity analysis, especially in the case of models with dependent inputs, the package sensobol offers a bunch of user-friendly tools for the visualisation purposes. Several illustrative examples are supplied that allow the user to learn both packages easily and benefit from their features.

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Uncertainty quantification and global sensitivity analysis for economic models

Cited by 31 publications

References 81 publications

Analysis of the Equilibrium Phase in Immune-Controlled Tumors Provides Hints for Designing Better Strategies for Cancer Treatment

Analysis of the Equilibrium Phase in Immune-Controlled Tumors Provides Hints for Designing Better Strategies for Cancer Treatment

Reward‐Based Participant Management for Crowdsourcing Rainfall Monitoring: An Agent‐Based Model Simulation

Variance-based global sensitivity analysis of numerical models using R

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