Agent-Based and Individual-Based Modeling: A Practical Introduction

2011

658 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/c97daac354be8ab4510469bd2100fc1fe898ac50

Citations in the corpus

Agent-Based Modeling in Public Health: Current Applications and Future Directions (2018, unknown): “Applications of ABMs in ecology, business, political science, and the social sciences (7, 8, 30, 69, 73, 88) have been influential in the development of ABMs in public health, as well as in the formalization and standardization of these approaches across disciplines (46–48).”
Emerging issues and challenges in conservation of biodiversity in the rangelands of Tanzania (2013, unknown): “The advances for habitat monitoring achieved by the EBONE project (Brus et al. 2011, Metzger et al. 2013) will be taken up and EU BON will tackle these challenges with new statistical and virtual ecological approaches (Railsback and Grimm 2012).”
Towards better modelling and decision support: Documenting model development, testing, and analysis using TRACE (2014, unknown): “Therefore more formal tests are required that are based on multiple quantitative criteria for a model matching data (e.g., Railsback and Grimm, 2012, Chapter 20.4.2 Evaluation of output veriﬁcation needs to consider such concerns as over-ﬁtting and extrapolation The higher the proportion of…”
Rhizosphere Spatiotemporal Organization–A Key to Rhizosphere Functions (2018, unknown): “To study emerging properties, we see high potential for individual-based models (Railsback and Grimm, 2019) of the rhizosphere system, where the individuals can represent microorganisms (Kreft et al., 2013; Hellweger et al., 2016; Portell et al., 2018) but also individual root elements.”
Everything you need to know about agent-based modelling and simulation (2016, unknown): “Some well-cited introductory material on ABMS includes Axelrod and Tesfatsion (2016), Bonabeau (2001), Gilbert (2008), Gilbert and Troitzsch (2005), Helbing and Balietti (2011), Railsback and Grimm (2011), Wilensky and Rand (2015), and North and Macal (2007).”
Agent Based Modelling and Simulation tools: A review of the state-of-art software (2017, unknown): “ABMS can reproduce many characteristics of real outbreaks of diseases and the predictions are easy to comprehend, with disease control managers being accepting of the simulation results and thereafter successfully intervening with pre-emptive vaccination strategies [39].”
Representing the acquisition and use of energy by individuals in agent‐based models of animal populations (2013, unknown): “Comparisons of patterns with model outputs may eliminate many competing model structures and parameterizations (Wiegand et al. 2003; Wiegand, Revilla & Knauer 2004; Railsback & Grimm 2012).”
Constructing, conducting and interpreting animal social network analysis (2015, unknown): “Data-driven simulations involve building an agent-based model (Railsback & Grimm 2012) in which patterns of interactions are determined by the observed network.”
Predicting monarch butterfly (Danaus plexippus) movement and egg-laying with a spatially-explicit agent-based model: The role of monarch perceptual range and spatial memory (2018, unknown): “Agent-based modeling is well-suited to address the effects of different spatially-explicit landscape configurations on animal 4 population dynamics (DeAngelis and Mooij, 2005, Grimm et al., 2005, Railsback et al., 2006; Railsback and Grimm, 2011).”
Facilitating Parameter Estimation and Sensitivity Analysis of Agent-Based Models: A Cookbook Using NetLogo and 'R' (2014, unknown): “…can be limits to simplification, for example if a model is no longer able to reproduce multiple patterns simultaneously (Grimm & Railsback 2012; Railsback & Grimm 2012); in such cases, using computing clusters, which currently are often available, can help to still perform the high number of…”
Nanomedicine-Driven Modulation of the Gut–Brain Axis: Innovative Approaches to Managing Chronic Inflammation in Alzheimer’s and Parkinson’s Disease (2025, unknown): “It is particularly useful for extrapolating macro-level implications from micro-level assumptions [266].”