How to keep it adequate: A protocol for ensuring validity in agent-based simulation

On the use of uncertainty analyses to test hypotheses regarding deterministic model predictions of environmental processes

Parameter estimation and uncertainty analysis for a watershed model

Ecosystem models are useful tools for understanding ecological processes and for sustainable management of resources. In biogeochemical field, numerical models have been widely used for investigating carbon dynamics under global changes from site to regional and global scales. However, it is still challenging to optimize parameters and estimate parameterization uncertainty for complex process‐based models such as the Erosion Deposition Carbon Model (EDCM), a modified version of CENTURY, that consider carbon, water, and nutrient cycles of ecosystems. This study was designed to conduct the parameter identifiability, optimization, sensitivity, and uncertainty analysis of EDCM using our developed EDCM‐Auto, which incorporated a comprehensive R package—Flexible Modeling Framework (FME) and the Shuffled Complex Evolution (SCE) algorithm. Using a forest flux tower site as a case study, we implemented a comprehensive modeling analysis involving nine parameters and four target variables (carbon and water fluxes) with their corresponding measurements based on the eddy covariance technique. The local sensitivity analysis shows that the plant production‐related parameters (e.g., PPDF1 and PRDX) are most sensitive to the model cost function. Both SCE and FME are comparable and performed well in deriving the optimal parameter set with satisfactory simulations of target variables. Global sensitivity and uncertainty analysis indicate that the parameter uncertainty and the resulting output uncertainty can be quantified, and that the magnitude of parameter‐uncertainty effects depends on variables and seasons. This study also demonstrates that using the cutting‐edge R functions such as FME can be feasible and attractive for conducting comprehensive parameter analysis for ecosystem modeling.

An interactive graphical interface tool for parameter calibration, sensitivity analysis, uncertainty analysis, and visualization for the Soil and Water Assessment Tool

Precipitation interpolation is widely used to generate continuous rainfall surfaces for hydrological simulations. However, increasing the precision of values at the unknown points generated by different spatial interpolation methods is challenging. This study used the Chaohe River Basin, which is an important source of Beijing’s drinking water, as a research area to comprehensively evaluate several precipitation interpolation methods (Thiessen polygon, inverse distance weighting, ordinary kriging and ANUSPLIN) for inputs in hydrological simulations. This research showed that the precipitation time-series surface generated using the ANUSPLIN interpolation method had higher accuracy and reliability. Using this precipitation input to drive the hydrological models, we explored the parameter uncertainties of four typical hydrological models (GR4J, IHACRES, Sacramento and MIKE SHE) based on the multi-objective generalized likelihood uncertainty estimation (GLUE) method. The GLUE method was used to study the parameter sensitivity and uncertainty of the model. Results showed that the ANUSPLIN precipitation interpolation surface combined with the Sacramento model performed best. The multi-objective GLUE method had obvious advantages in parameter uncertainty analysis in hydrological models. Simultaneously exploring the convex line and point density distributions of the behavioural parameters with multi-objective functions determined their distribution and sensitivity more effectively.

Engineering experience reminds us of the importance of uncertainty analysis in fatigue reliability based design. In the present paper, according to the procedure of fatigue reliability analysis, parameter uncertainty and model uncertainty are studied and the corresponding solutions are reviewed. The aim of parameter uncertainty analysis is to obtain reliability index to quantify uncertainties imported by parameters. The current research focuses on defining reliability index and putting forward different methods to handle different variable expressions (random, convex, fuzzy set). Different from parameter uncertainty analysis, model uncertainty analysis can reduce uncertainties imported by model by modifying model structure or selecting best model. In application, both methods can effectively quantify and reduce uncertainty. The development on both methods will contribute to understanding fatigue process and its causes.

A parametric uncertainty and sensitivity analysis of hydrodynamic processes was conducted for a large shallow freshwater lake, Lake Taihu, China. Ten commonly used parameters in five groups were considered including: air–water interface factor, water–sediment interface factor, surrounding terrain factor, turbulent diffusion parameters and turbulent intensity parameters. Latin hypercube sampling (LHS) was used for sampling the parametric combinations, which gave predictive uncertainty results directly without using surrogate models, and the impacts of different parametric distribution functions on the results were investigated. The results showed that the different parametric distribution functions (e.g. uniform, normal, lognormal and triangular) for sampling had very little impact on the uncertainty and sensitivity analysis of the lake hydrodynamic model. The air–water interface factor (wind drag coefficient) and surrounding terrain factor (wind shelter coefficient) had the greatest influence on the spatial distribution of lake hydrodynamic processes, especially in semi-closed bays and lake regions with complex topography, accounting for about 60–70% and 20%, respectively, of the uncertainty on the results. Vertically, velocity in the surface layer was also largely influenced by the two factors, followed by velocity in the bottom layer; the middle velocity had minimal impact. Likewise, the water–sediment interface factor (i.e. bottom roughness height) ranked third, contributing about 10% to the uncertainty of the hydrodynamic processes of the lake. In contrast, turbulent diffusion parameters and turbulent intensity parameters in the lake hydrodynamic model had little effect on the uncertainty of simulated results (less than 1% contribution). The findings were sufficiently significant to reduce the parameter uncertainties and calibration workload of the hydrodynamic model in large shallow lakes.Editor Z. W. Kundzewicz; Associate editor S. Grimaldi

Joint analysis of input and parametric uncertainties in watershed water quality modeling: A formal Bayesian approach

With existence of the social custom or norm, Naylor demonstrates a possibility of stable long-run equilibria of support for a strike in a labor market, and this implies that at least some individuals will behave cooperatively and hence the prisoners' dilemma could be escaped. In this paper, we develop an agent-based simulation system in which artificial adaptive agents have mechanisms of decision making and learning based on neural networks and genetic algorithms, and compare the result of our simulation analysis with that of the mathematical model by Naylor. Especially, while the Naylor model is based on rationality about maximization of individual utility, our agent-based simulation model employs adaptive behavior of agents; agents make decisions by trials and errors and they learn from experiences to make better decisions.

Laterite-type bauxite deposits typically exhibit a highly irregular boundary between the bauxite and underlying ferricrete units. This irregularity cannot be accurately modeled using data collected from sparsely spaced drillholes (e.g., 76.2×76.2 m or 250×250 ft). Geological models that assume a sharp and nearly horizontal bauxite/ferricrete contact can result in significant errors when calculating the in situ bauxite resource (in volume) and in misclassifying ore and waste during mining operations. Two primary sources of uncertainty must be addressed when modeling lateritic bauxite deposits: (1) grade uncertainty associated with variations in Al2O3 and SiO2% concentrations, and (2) geometric uncertainty related to lateral variations in the bauxite/ferricrete contact. Among these, geometric uncertainty is more critical, as accurately estimating bauxite ore tonnage depends on the precise modeling of the lateral variation in the boundary between the bauxite and underlying ferricrete units. This study evaluates the uncertainty of the bauxite resource within a selected mine area in northern Queensland, Australia, particularly in cases where experimental data are sparse and limited. To address this, the position variable (bauxite floor elevations) and the thickness of the bauxite unit are jointly simulated under two scenarios. In the first scenario, the histograms, variogram model parameters, and the estimated trend of the variables of interest are assumed to be known with certainty; that is, parameter uncertainty is not considered in the modeling process. In the second scenario, the histograms, variogram model parameters, and the estimated trend are considered uncertain, and parameter uncertainty is explicitly incorporated into the modeling process using the multivariate spatial bootstrap procedure. The methodology is applied to both scenarios, showing that incorporating parameter uncertainty in geostatistical modeling results in greater dispersion of the uncertainty associated with the in situ bauxite resource. The results show that the 95% confidence intervals for the in situ bauxite ore volume, derived from bauxite thickness realizations, vary depending on whether parameter uncertainty is considered. When parameter uncertainty is incorporated, the interval is (390,123 m3 and 393,223 m3), whereas without parameter uncertainty, it is (382,332 m3 and 384,373 m3). This comparison highlights that incorporating parameter uncertainty provides a more realistic assessment of resource risk in the modeling process.

Groundwater is a crucial resource for water supply and irrigation in many parts of the world, especially in the Middle East. The Eocene aquifer, located in the northern part of the West Bank, Palestine, is threatened by unsustainable groundwater abstractions and on-ground pollution. Analysis and management of this aquifer are challenging because of limited data availability. This research contributes to the long-term sustainability of the aquifer by model-based design of future abstraction strategies considered within an uncertainty analysis framework. The methodology employed started with development of a single-layer steady-state MODFLOW groundwater model of the area, followed by uncertainty analysis of model parameters using Monte Carlo simulations. The same model was afterwards coupled with a Successive Linear Programming (SLP) optimization algorithm, implemented in the Groundwater Management tool (GWM) of the United States Geological Survey (USGS). The purpose of optimization was deriving five optimal abstraction strategies, each aiming to maximize groundwater abstraction, subject to different constraints regarding groundwater depletion. Given the uncertainty of model parameters, the sensitivity and reliability of these optimal strategies were then tested. Sensitivity was checked for two optimal strategies by performing re-optimization with different values of uncertain model parameters (one at a time). Reliability of the five strategies was tested by analyzing the extent of constraints’ violation for each strategy when varying the uncertain parameters using Monte Carlo simulations. Finally, the model was used for determining capture zones of wells for the five optimal abstraction strategies, land-use in these capture zones, and the associated estimates of on-ground nitrogen loading. The developed strategies were then deployed in a web-based decision support application (named Groundwater Decision Support System—GDSS), together with other relevant information. Users can analyze results of different optimal strategies in terms of groundwater level variations and total water balance results, and test consequences of uncertain parameters. Capture zones of wells for different abstraction strategies, together with land-use and on-ground nitrogen loading in these capture zones, are also presented. Results show that critical uncertain parameters are recharge, hydraulic conductivity, and conductance at key boundary condition locations. Optimal abstraction strategies results indicate that an increase in total abstractions could be between 5% and 20% from the current level (estimated at about 56 × 106 m3/year, which is about 74% of estimated annual recharge). The uncertain parameters, however, are impacting the sensitivity and the reliability of the optimal strategies to variable degrees. Recharge and hydraulic conductivity are the most critical uncertain parameters regarding sensitivity of the optimal strategies, while reliability is also impacted by the level of abstraction proposed in a given strategy (number, locations, and abstraction rates of new wells). The main novelty and contribution of this research is in combining modelling, uncertainty analysis, and optimization techniques in a comprehensive decision support system for the area of the Eocene aquifer, characterized with limited data availability.

Damping properties of assembled structures are largely influenced by frictional damping between joint interfaces. Therefore, these effects must be considered during the modeling process. Applying thin-layer elements (TLEs) with a linear, orthotropic material model on mechanical interfaces to incorporate joint damping has shown good agreement with experimental modal analysis in previous work. In the TLE model, constant hysteretic damping is assumed. The damping and stiffness parameters for the TLEs are experimentally identified on an isolated lap joint. Imprecisions caused by model simplifications and parameter uncertainty are addressed by model updating or uncertainty analysis. This requires multiple evaluations of models that are equivalent in all respects but their TLE parameterization. In this work, a model reduction technique for the TLE modeling approach is presented which significantly reduces computational cost for the re-calculation of eigenvalues after joint parameters are changed. The reduction is based on an eigensensitivity analysis and results in a single, linear equation for each eigenvalue. The presented approach is applied to a model updating example. Here, the model reduction allows for a much larger number of design variables which means experimental data can be reproduced more accurately with a physically more meaningful model.

<p>A scripted development and deployment approach was used for developing the next-generation groundwater flow and land-surface subsidence model of the region surrounding Houston, Texas, USA.  The area has historically experienced substantial land subsidence resulting from groundwater use. Python scripts leveraging the FloPy and PyEMU packages were written to build and run the MODFLOW 6 model, perform very-high-dimensional parameter estimation and uncertainty analysis using PEST++, and process results. Automating these processes allowed for fast and repeated iterations through all or part of the modeling workflow for purposes including: troubleshooting input errors, testing hypotheses about the hydrologic system characteristics, evaluating the influences of structural model assumptions, and experimenting with different and increasingly complex formulations of the prior parameter distribution and likelihood functions in Bayesian sense. Automated generation and storage of processed output allowed easy comparison between iterations of the modeling workflow, and Git version control software provided a self-documented model repository with full-featured “undo” for returning to previous states of the workflow and investigating outcomes. The modeling team convened regularly (monthly to twice-weekly) to review results of the latest iteration and decide the next course of action. Model performance was improved steadily and incrementally by focusing on one new feature or problem per workflow iteration until modeling goals were met. This workflow style fostered a sense of predictability and confidence in the project outcome, a welcome departure from the “typical” numerical modeling process of panic and despair.</p>

Why do people use social media, even though they must pay certain costs for generating content? The purpose of this study is to answer a more pointed question: What type of mechanism can encourage the continuous participation of users in social media? To address this question, we clarify various conditions that must be met to motivate users to voluntarily provide information in social media. In this work, we use the framework of ``the public goods game'' to describe user behaviors such as posting articles and comments. We first describe the communication carried out in specific social media by extending the metanorms game, which issues both rewards for cooperation and punishments for non-cooperation. We further develop a simulation model to clarify the conditions needed to create a cooperation-dominant environment, which we define as a world where nearly everyone cooperates in the sense used in game theory. From the results of our agent-based simulation, it is clear that the rewards for cooperation and the rewards for rewards given by others can work to encourage cooperation. Furthermore, we analyze the relationships between the costs and benefits of cooperation by using dynamics analysis. Consequently, we clarify that cooperative behaviors are dominant when the benefits of cooperation are larger than the costs of rewarding other users.

The central theme of this book is that representation matters. Consider the mixed strategy representations common in classical game theory—probability distributions on the set of moves for a game. This is a simple representation with the cardinal advantage that many aspects of it can be solved mathematically. As a representation, it also runs afoul of George E. P. Box’s dictum that all models are wrong, but some models are useful. The obvious way in which mixed models are wrong, as a representation for agents that simulate human beings, is that they simplify most of the qualities of humans that a researcher might want to model right out the modeling process. There is a problem in that models are considered acceptable in classical game theory only if they can be solved. With powerful computers available at nugatory prices, agent-based simulation is a viable and potentially valuable alternative to traditional theorem-and-proof methods.