Parameters Of Agent-based Models Research Articles

Understanding the future and evolution of agri-food systems: A combination of qualitative scenarios with agent-based modelling

The agri-food system is a vital and complex social-ecological system characterized by interactions between humans and the environment. For such systems, qualitative scenarios (QS) can generate pictures of possible futures, which combined with quantitative modelling allow identifying pathways towards improved resilience and exploring system uncertainties. However, the interpretation of qualitative narratives into quantitative simulation parameters remains challenging due to system complexity. In this study, we translate QS into quantitative agent-based model (ABM) parameters and estimate the likelihood of each scenario in the evolved agri-food system in response to individual actions. We implement a five-step approach consisting of: i) the generation of the QS, ii) the parameterization of the ABM iii) the translation of scenario assumptions into ABM parameters, iv) the validation of our results via an expert workshop, v) the assessment of the scenarios in the evolved system. The results of an illustrative example reveal that, with the implementation of cropping diversification, the system will evolve to a combination of scenario 1, 2, 3 and 4 at probabilities of 9 %, 35 %, 30 % and 26 %, respectively, which change under different management options. Overall, we conclude that QS-ABM combination is a promising approach to provide robust quantitative projections of the agri-food system future.

A predictive multiscale model of in-stent restenosis in femoral arteries: linking haemodynamics and gene expression with an agent-based model of cellular dynamics.

In-stent restenosis (ISR) is a maladaptive inflammatory-driven response of femoral arteries to percutaneous transluminal angioplasty and stent deployment, leading to lumen re-narrowing as consequence of excessive cellular proliferative and synthetic activities. A thorough understanding of the underlying mechanobiological factors contributing to ISR is still lacking. Computational multiscale models integrating both continuous- and agent-based approaches have been identified as promising tools to capture key aspects of the complex network of events encompassing molecular, cellular and tissue response to the intervention. In this regard, this work presents a multiscale framework integrating the effects of local haemodynamics and monocyte gene expression data on cellular dynamics to simulate ISR mechanobiological processes in a patient-specific model of stented superficial femoral artery. The framework is based on the coupling of computational fluid dynamics simulations (haemodynamics module) with an agent-based model (ABM) of cellular activities (tissue remodelling module). Sensitivity analysis and surrogate modelling combined with genetic algorithm optimization were adopted to explore the model behaviour and calibrate the ABM parameters. The proposed framework successfully described the patient lumen area reduction from baseline to one-month follow-up, demonstrating the potential capabilities of this approach in predicting the short-term arterial response to the endovascular procedure.

Connected communities improve hazard response: An agent-based model of social media behaviors during hurricanes

Social media platforms have a developing role in how people respond to hazards, providing a network to seek help and respond to help requests. Understanding the dynamics of behavior on social media networks can improve community-level hazard response efforts. People who experience damages may use social media to seek immediate help for debris removal, supplies delivery or emergency rescue, and peers connected on social media may respond by reposting the help request or providing help in person. This research develops an agent-based model (ABM) to simulate a community of individuals that use social media to seek help and respond to requests for help during a hurricane. Agents represent individuals that are in a community affected by a hurricane and share a social media network. Behavioral rules for seeking help and providing help are developed using the Theory of Planned Behavior and parametrized through analysis of a survey of social media use conducted in communities that were affected by 2018 Hurricanes Florence and Michael. The ABM simulates agents that post help requests, repost help requests, provide help in person, and receive help. A Design of Experiments approach is applied to assess how ABM parameters, including community size, connectivity of a network, damage rate, and propensity for using social media, influence the number of requests for help that are met through the social media network. Results demonstrate that high connectivity leads to rapid reposting and results in cascading responses to meet requests for help.

A fully coupled computational fluid dynamics – agent-based model of atherosclerotic plaque development: Multiscale modeling framework and parameter sensitivity analysis

BackgroundPeripheral Artery Disease (PAD) is an atherosclerotic disorder that leads to impaired lumen patency through intimal hyperplasia and the build-up of plaques, mainly localized in areas of disturbed flow. Computational models can provide valuable insights in the pathogenesis of atherosclerosis and act as a predictive tool to optimize current interventional techniques. Our hypothesis is that a reliable predictive model must include the atherosclerosis development history. Accordingly, we developed a multiscale modeling framework of atherosclerosis that replicates the hemodynamic-driven arterial wall remodeling and plaque formation. MethodsThe framework was based on the coupling of Computational Fluid Dynamics (CFD) simulations with an Agent-Based Model (ABM). The CFD simulation computed the hemodynamics in a 3D artery model, while 2D ABMs simulated cell, Extracellular Matrix (ECM) and lipid dynamics in multiple vessel cross-sections. A sensitivity analysis was also performed to evaluate the oscillation of the ABM output to variations in the inputs and to identify the most influencing ABM parameters. ResultsOur multiscale model qualitatively replicated both the physiologic and pathologic arterial configuration, capturing histological-like features. The ABM outputs were mostly driven by cell and ECM dynamics, largely affecting the lumen area. A subset of parameters was found to affect the final lipid core size, without influencing cell/ECM or lumen area trends. ConclusionThe fully coupled CFD-ABM framework described atherosclerotic morphological and compositional changes triggered by a disturbed hemodynamics.

A Fast Method for Agent-Based Model Fitting Aggregate-Level Diffusion Data

This paper provides theoretical arguments and simulation evidence regarding how a differential equation-based diffusion model (DE) can be used to improve the efficiency of an agent-based model (ABM) fitting market-level diffusion data. Using computational experiments, we observe that the DE fits ABM diffusion processes very well and that the linear correlativity between the ABM parameters and their corresponding DE estimates is very well in a wide range of settings. However, as significantly systematic biased forecasts of the DE for ABM diffusion processes exist, the ABM cannot be replaced by the DE to forecast real-world diffusion. Based on these findings, we design a fast parameter estimation method for the ABM by integrating the DE into a component to locate an initial point near the optimal solution.The empirical study demonstrates that the proposed procedure can search out the optimal solution by evaluating only a small number of points. Furthermore, the empirical study also demonstrates that certain ABMs and the simple averaging method have better explanatory and forecasting performance than the DE. This method prepares the ABM to forecast innovation diffusion and also makes a contribution to the literature on the validation of ABM.

Development of an Agent-Based Model (ABM) to Simulate the Immune System and Integration of a Regression Method to Estimate the Key ABM Parameters by Fitting the Experimental Data.

Agent-based models (ABM) and differential equations (DE) are two commonly used methods for immune system simulation. However, it is difficult for ABM to estimate key parameters of the model by incorporating experimental data, whereas the differential equation model is incapable of describing the complicated immune system in detail. To overcome these problems, we developed an integrated ABM regression model (IABMR). It can combine the advantages of ABM and DE by employing ABM to mimic the multi-scale immune system with various phenotypes and types of cells as well as using the input and output of ABM to build up the Loess regression for key parameter estimation. Next, we employed the greedy algorithm to estimate the key parameters of the ABM with respect to the same experimental data set and used ABM to describe a 3D immune system similar to previous studies that employed the DE model. These results indicate that IABMR not only has the potential to simulate the immune system at various scales, phenotypes and cell types, but can also accurately infer the key parameters like DE model. Therefore, this study innovatively developed a complex system development mechanism that could simulate the complicated immune system in detail like ABM and validate the reliability and efficiency of model like DE by fitting the experimental data.