168,716 research outputs found
Principles and Concepts of Agent-Based Modelling for Developing Geospatial Simulations
The aim of this paper is to outline fundamental concepts and principles of the Agent-Based Modelling (ABM) paradigm, with particular reference to the development of geospatial simulations. The paper begins with a brief definition of modelling, followed by a classification of model types, and a comment regarding a shift (in certain circumstances) towards modelling systems at the individual-level. In particular, automata approaches (e.g. Cellular Automata, CA, and ABM) have been particularly popular, with ABM moving to the fore. A definition of agents and agent-based models is given; identifying their advantages and disadvantages, especially in relation to geospatial modelling. The potential use of agent-based models is discussed, and how-to instructions for developing an agent-based model are provided. Types of simulation / modelling systems available for ABM are defined, supplemented with criteria to consider before choosing a particular system for a modelling endeavour. Information pertaining to a selection of simulation / modelling systems (Swarm, MASON, Repast, StarLogo, NetLogo, OBEUS, AgentSheets and AnyLogic) is provided, categorised by their licensing policy (open source, shareware / freeware and proprietary systems). The evaluation (i.e. verification, calibration, validation and analysis) of agent-based models and their output is examined, and noteworthy applications are discussed.Geographical Information Systems (GIS) are a particularly useful medium for representing model input and output of a geospatial nature. However, GIS are not well suited to dynamic modelling (e.g. ABM). In particular, problems of representing time and change within GIS are highlighted. Consequently, this paper explores the opportunity of linking (through coupling or integration / embedding) a GIS with a simulation / modelling system purposely built, and therefore better suited to supporting the requirements of ABM. This paper concludes with a synthesis of the discussion that has proceeded. The aim of this paper is to outline fundamental concepts and principles of the Agent-Based Modelling (ABM) paradigm, with particular reference to the development of geospatial simulations. The paper begins with a brief definition of modelling, followed by a classification of model types, and a comment regarding a shift (in certain circumstances) towards modelling systems at the individual-level. In particular, automata approaches (e.g. Cellular Automata, CA, and ABM) have been particularly popular, with ABM moving to the fore. A definition of agents and agent-based models is given; identifying their advantages and disadvantages, especially in relation to geospatial modelling. The potential use of agent-based models is discussed, and how-to instructions for developing an agent-based model are provided. Types of simulation / modelling systems available for ABM are defined, supplemented with criteria to consider before choosing a particular system for a modelling endeavour. Information pertaining to a selection of simulation / modelling systems (Swarm, MASON, Repast, StarLogo, NetLogo, OBEUS, AgentSheets and AnyLogic) is provided, categorised by their licensing policy (open source, shareware / freeware and proprietary systems). The evaluation (i.e. verification, calibration, validation and analysis) of agent-based models and their output is examined, and noteworthy applications are discussed.Geographical Information Systems (GIS) are a particularly useful medium for representing model input and output of a geospatial nature. However, GIS are not well suited to dynamic modelling (e.g. ABM). In particular, problems of representing time and change within GIS are highlighted. Consequently, this paper explores the opportunity of linking (through coupling or integration / embedding) a GIS with a simulation / modelling system purposely built, and therefore better suited to supporting the requirements of ABM. This paper concludes with a synthesis of the discussion that has proceeded
Modelling Reactive and Proactive Behaviour in Simulation
This research investigated the simulation model behaviour of a traditional
and combined discrete event as well as agent based simulation models when
modelling human reactive and proactive behaviour in human centric complex
systems. A departmental store was chosen as human centric complex case study
where the operation system of a fitting room in WomensWear department was
investigated. We have looked at ways to determine the efficiency of new
management policies for the fitting room operation through simulating the
reactive and proactive behaviour of staff towards customers. Once development
of the simulation models and their verification had been done, we carried out a
validation experiment in the form of a sensitivity analysis. Subsequently, we
executed a statistical analysis where the mixed reactive and proactive
behaviour experimental results were compared with some reactive experimental
results from previously published works. Generally, this case study discovered
that simple proactive individual behaviour could be modelled in both simulation
models. In addition, we found the traditional discrete event model performed
similar in the simulation model output compared to the combined discrete event
and agent based simulation when modelling similar human behaviour.Comment: 9 pages, 7 figures, Operational Research Society 5th Simulation
Workshop (SW10
Uncertainty and Variability Analysis of Agent-Based Transport Models
This paper presents an analysis of the output variability of agent-based transport models. We simulated a MATSim model of the city of Hanover multiple times with identical input and evaluated the resulting travel times on different level of aggregation. On a global level, we observed minor variations of travel times. However, the results show an increased variation when examining the output on the level of districts or for individual agents. A recommendation for estimating the required number of simulation runs for a stable output of travel time for the purposed aggregation level is derived from our case study
Adaptive Capacity through Complex Adaptive System
Problem: The corrugated board industry is highly affected by customer uncertainty, various demands and short delivery times. In combination with a complex multi-step production process managers have to be able to identify bottle-necks and gain knowledge and understanding of how different changes in process will affect the production output. Purpose: The purpose of this master thesis is twofold, (1) to examine applicability of complexity theory through agent-based modelling on a production process (2) to identify improvement areas in order to increase the production output at SKS production site in Eslöv, by modelling and simulating the production process through an agent-based model. Method: The chosen method of this study is a combination of a case study and a complex system approach. The empirical data was collected through interviews, observation and document studies which were analysed through an agent-based simulation model. Conclusions: Through the holistic complex system approach and by iteratively exploring the SKS production process’s components the authors were able to distinguished essential factors of the production process wherefrom the complexity emerged. The production process exhibited several internal complex properties whereby the authors consider that SKS production process can undoubtedly be consider as a Complex Adaptive Systems (CAS). By mapping and utilising agent-based modelling the complexity of the system could be transferred to an agent-based model. Through analysis of the agent-based model the authors identified and simulated four improvement areas which provide possibility of an increased capacity utilisation and production output. Based on the results the authors recommend a higher individual freedom for the machines and a greater interaction both within the company and with the customer
Modelling reactive and proactive behaviour in simulation
This research investigated the simulation model behaviour of a traditional and combined discrete event as well as agent based simulation models when modelling human reactive and proactive behaviour in human centric complex systems. A
departmental store was chosen as human centric complex case study where the operation system of a fitting room in WomensWear department was investigated. We have looked at ways to determine the efficiency of new management policies for the fitting room operation through simulating the reactive and proactive behaviour of staff towards customers. Once development of the simulation models and their verification had been done, we carried out a validation experiment in the form of a sensitivity analysis.
Subsequently, we executed a statistical analysis where the mixed reactive and proactive behaviour experimental results were compared with some reactive experimental results from
previously published works. Generally, this case study discovered that simple proactive individual behaviour could be modelled in both simulation models. In addition, we found the traditional discrete event model performed similar in the simulation model output compared to the
combined discrete event and agent based simulation when modelling similar human behaviour
Landscape epidemiology modeling using an agent-based model and a geographic information system
A landscape epidemiology modeling framework is presented which integrates the
simulation outputs from an established spatial agent-based model (ABM) of malaria with a
geographic information system (GIS). For a study area in Kenya, five landscape scenarios
are constructed with varying coverage levels of two mosquito-control interventions. For
each scenario, maps are presented to show the average distributions of three output indices
obtained from the results of 750 simulation runs. Hot spot analysis is performed to detect
statistically significant hot spots and cold spots. Additional spatial analysis is conducted
using ordinary kriging with circular semivariograms for all scenarios. The integration of
epidemiological simulation-based results with spatial analyses techniques within a single
modeling framework can be a valuable tool for conducting a variety of disease control
activities such as exploring new biological insights, monitoring epidemiological landscape
changes, and guiding resource allocation for further investigation
Learning differential equation models from stochastic agent-based model simulations
Agent-based models provide a flexible framework that is frequently used for
modelling many biological systems, including cell migration, molecular
dynamics, ecology, and epidemiology. Analysis of the model dynamics can be
challenging due to their inherent stochasticity and heavy computational
requirements. Common approaches to the analysis of agent-based models include
extensive Monte Carlo simulation of the model or the derivation of
coarse-grained differential equation models to predict the expected or averaged
output from the agent-based model. Both of these approaches have limitations,
however, as extensive computation of complex agent-based models may be
infeasible, and coarse-grained differential equation models can fail to
accurately describe model dynamics in certain parameter regimes. We propose
that methods from the equation learning field provide a promising, novel, and
unifying approach for agent-based model analysis. Equation learning is a recent
field of research from data science that aims to infer differential equation
models directly from data. We use this tutorial to review how methods from
equation learning can be used to learn differential equation models from
agent-based model simulations. We demonstrate that this framework is easy to
use, requires few model simulations, and accurately predicts model dynamics in
parameter regions where coarse-grained differential equation models fail to do
so. We highlight these advantages through several case studies involving two
agent-based models that are broadly applicable to biological phenomena: a
birth-death-migration model commonly used to explore cell biology experiments
and a susceptible-infected-recovered model of infectious disease spread
Validating and Testing an Agent-Based Model for the Spread of COVID-19 in Ireland
Agent-based models can be used to better understand the impacts of lifting restrictions or implementing interventions during a pandemic. However, agent-based models are computationally expensive, and running a model of a large population can result in a simulation taking too long to run for the model to be a useful analysis tool during a public health crisis. To reduce computing time and power while running a detailed agent-based model for the spread of COVID-19 in the Republic of Ireland, we introduce a scaling factor that equates 1 agent to 100 people in the population. We present the results from model validation and show that the scaling factor increases the variability in the model output, but the average model results are similar in scaled and un-scaled models of the same population, and the scaled model is able to accurately simulate the number of cases per day in Ireland during the autumn of 2020. We then test the usability of the model by using the model to explore the likely impacts of increasing community mixing when schools reopen after summer holidays
An empirical learning-based validation procedure for simulation workflow
Simulation workflow is a top-level model for the design and control of
simulation process. It connects multiple simulation components with time and
interaction restrictions to form a complete simulation system. Before the
construction and evaluation of the component models, the validation of
upper-layer simulation workflow is of the most importance in a simulation
system. However, the methods especially for validating simulation workflow is
very limit. Many of the existing validation techniques are domain-dependent
with cumbersome questionnaire design and expert scoring. Therefore, this paper
present an empirical learning-based validation procedure to implement a
semi-automated evaluation for simulation workflow. First, representative
features of general simulation workflow and their relations with validation
indices are proposed. The calculation process of workflow credibility based on
Analytic Hierarchy Process (AHP) is then introduced. In order to make full use
of the historical data and implement more efficient validation, four learning
algorithms, including back propagation neural network (BPNN), extreme learning
machine (ELM), evolving new-neuron (eNFN) and fast incremental gaussian mixture
model (FIGMN), are introduced for constructing the empirical relation between
the workflow credibility and its features. A case study on a landing-process
simulation workflow is established to test the feasibility of the proposed
procedure. The experimental results also provide some useful overview of the
state-of-the-art learning algorithms on the credibility evaluation of
simulation models
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