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Inclusive Decision-Making
Published in Rod D. Roscoe, Erin K. Chiou, Abigail R. Wooldridge, Advancing Diversity, Inclusion, and Social Justice Through Human Systems Engineering, 2019
Jacklin Stonewall, Michael C. Dorneich, Linda Shenk, Caroline C. Krejci, Ulrike Passe
Empirically driven agent-based models enable policymakers to test different policies in an environment that embraces the uniqueness of different agents, rather than designing systems for an “average” user. Human factors practitioners focus on understanding the actual needs of users, and thus are well placed to capture the voice and needs of residents of marginalized communities to inform decision-making. Applying human factors methods for human behavior data elicitation allowed for a more authentic representation of community members’ decision-making and behaviors in the ABM and improved the model’s validity and ability to predict the impacts of different policy implementations on community health and well-being. Because these models may be used at both the community and city level to inform policy, it is important that the model be based on accurate representation of the residents’ voice, behaviors, and attitudes.
Supporting Spaceflight Multiteam Systems throughout Long-Duration Exploration Missions
Published in Lauren Blackwell Landon, Kelley J. Slack, Eduardo Salas, Psychology and Human Performance in Space Programs, 2020
Jacob G. Pendergraft, Dorothy R. Carter, Hayley M. Trainer, Justin M. Jones, Aaron Schecter, Marissa L. Shuffler, Leslie A. DeChurch, Noshir S. Contractor
Agent-based models are computer simulations that afford insights into emergent behavior resulting from actions and interactions that occur within complex systems (Macy & Willer, 2002). In an agent-based model, a set of agents, for example, crew and MCC members, are seeded with a set of characteristics (e.g. demographics, personality, team memberships, training experience) which replicate the composition of actual SFMTS component teams, as well as a set of theoretically-derived rules guiding their actions and interactions with other agents. During the simulation, the agents interact with one another, in accordance with their rules, thus generating networks of relational states within and between teams.
Setting the Stage: Complex Systems, Emergence and Evolution
Published in Mariam Kiran, X-Machines for Agent-Based Modeling, 2017
Agent-based models encourage bottom-up approaches, allowing research to focus on individual elements interacting with each other, rather than looking at complete scenarios. Initially, pattern in models was proved using differential equations with common examples being found in economic modeling, where mathematical formulas are still being used to prove behavior of ideas. Miller and Page [131] and Epstein [56] have favored agent-based approaches by saying that research should be intensified to focus into agents rather than whole systems, realistically allowing humans to be modeled as agents rather than differential equations.
An agent-based simulation to analyze trucking sector regulation policies
Published in Transportation Letters, 2022
Sarvin Molaeinasab, Aria Dahimi, Amir Samimi
Agent-based models have solid methodological foundations and grant great freedom to researchers in terms of model design. Nevertheless, ABM in literature suffers from anarchy in design, analysis, and presentation. For instance, there is no precise classification of how agents can exchange and communicate: every model proposes its interaction structure (Richiardi et al. 2006). Therefore, verifying and validating agent-based models is a critical challenge, according to variant assumptions, parameters, and nonlinear behavior (Klugl 2008). Although the research has revealed different agent-based model validation methods, new complicated and detailed methods have not been used in the literature. Most studies are tailored to the output type and available system data and check the model validity with statistical comparisons. Sargent (2004) implemented a comprehensive and versatile approach for simulation validation regardless of the modeling method used. He offered a four-step validation method, including investigating data validity, conceptual model, computerized model, and operational validation. We followed this approach due to its explicit stepping, customizability to inferred reliability, and comprehensive utilization.
Battle damage-oriented spare parts forecasting method based on wartime influencing factors analysis and ε-support vector regression
Published in International Journal of Production Research, 2020
Xiong Li, Xiaodong Zhao, Wei Pu
Besides, we implement a typical simulation demonstration for battle damage-oriented spare parts forecasting in military industrial logistics for a combined army element by agent-based simulation technology, thus indicating the usability and applicability of our model. For some time now agent-based simulation in military field, has attracted the interest of researchers far beyond traditional computer science (Li and Dong 2012; Li, Chen, and Dong 2012). The agent-based simulation methodology relies on a population of autonomous entities called agents that behave according to rules and by interacting with other agents (Moya et al. 2017). Agent-based models provide a natural means to describe complex systems, as agents and their properties have a convenient mapping from the entities in real world systems (Shirazi et al. 2014).
A problem in human dynamics: modelling the population density of a social space
Published in Journal of Building Performance Simulation, 2020
Agent-based models simulate human behaviours with software agents that encode the behaviours of the systems being studied (Hamill and Gilbert 2016). Computer simulation of the resulting agent-based model can then be used to generate behavioural traces of occupants, which can be aggregated to form occupancy patterns of the space being modelled. Liao, Lin, and Barooah (2012) made use of agent-based modelling to extend the stochastic model of Page et al. (2008) to an arbitrary number of occupants and zones within a building. They also proposed a graphical model (Koller and Friedman 2009) with reduced complexity, which is shown to have comparable predictive accuracy to the agent-based model in describing the mean occupancy in the building. Chen, Hong, and Luo (2018) present an agent-based Occupancy Simulator that captures individual profiles of stochastic behaviours. The simulator is able to perform a detailed stochastic simulation of occupants' presence and movement within a building by integrating several existing stochastic occupancy models, in particular, those of Wang, Yan, and Jiang (2011) and Reinhart (2004). Luo et al. (2017) provided an evaluation of the Occupancy Simulator for a real-world occupancy data set, and showed that the simulator can accurately reproduce a wide variety of occupancy patterns.