Using SysML for Conceptual Representation of Agent-Based Models

2011 ◽  
Author(s):  
Zhenghui Sha ◽  
Qize Le ◽  
Jitesh H. Panchal
Keyword(s):  
Conceptual Models ◽  
Agent Based Modeling ◽  
Modeling Language ◽  
Automatic Extraction ◽  
Systems Modeling ◽  
Conceptual Representation ◽  
Agent Based Models ◽  
Agent Based ◽  
Collaborative Processes ◽  
Limited Support

Agent-based modeling (ABM) is a technique used to simulate systems consisting of autonomous interacting entities called agents. It has shown great advantages in modeling complex systems with independent but interacting actors. ABM has been successfully applied to a variety of systems. Despite the availability of a large number of tools for ABM, there is limited support for the conceptual design of agent-based models. Further, the currently available tools capture both the model information and the tool-specific execution information in an integrated manner. This limits model reusability, which is an impediment to systematic validation of models. In this paper, we use the systems modeling language (SysML) for building conceptual models of agent-based models. We discuss how the different diagrams in the SysML language can be used to represent different aspects of agent-based models. Further, we propose an approach for automatically generating executable agent-based models from their conceptual SysML representations. The proposed approach is illustrated using a model of mass-collaborative processes as an example. The proposed approach for conceptual representation of agent-based models in SysML and automatic extraction of executable models has the potential to greatly improve reuse, reconfiguration, and validation of agent-based models.

scholarly journals Agent Based Models of Polymicrobial Biofilms and the Microbiome—A Review

2021 ◽  
Vol 9 (2) ◽  
pp. 417
Author(s):  
Sherli Koshy-Chenthittayil ◽  
Linda Archambault ◽  
Dhananjai Senthilkumar ◽  
Reinhard Laubenbacher ◽  
Pedro Mendes ◽  
...  
Keyword(s):  
Species Interactions ◽  
Computational Models ◽  
Human Microbiome ◽  
Agent Based Modeling ◽  
Autonomous Agent ◽  
Agent Based Models ◽  
Agent Based ◽  
Complex Interactions ◽  
Mucosal Surfaces ◽  
Living Organisms

The human microbiome has been a focus of intense study in recent years. Most of the living organisms comprising the microbiome exist in the form of biofilms on mucosal surfaces lining our digestive, respiratory, and genito-urinary tracts. While health-associated microbiota contribute to digestion, provide essential nutrients, and protect us from pathogens, disturbances due to illness or medical interventions contribute to infections, some that can be fatal. Myriad biological processes influence the make-up of the microbiota, for example: growth, division, death, and production of extracellular polymers (EPS), and metabolites. Inter-species interactions include competition, inhibition, and symbiosis. Computational models are becoming widely used to better understand these interactions. Agent-based modeling is a particularly useful computational approach to implement the various complex interactions in microbial communities when appropriately combined with an experimental approach. In these models, each cell is represented as an autonomous agent with its own set of rules, with different rules for each species. In this review, we will discuss innovations in agent-based modeling of biofilms and the microbiota in the past five years from the biological and mathematical perspectives and discuss how agent-based models can be further utilized to enhance our comprehension of the complex world of polymicrobial biofilms and the microbiome.

The Simulation Presented in the ODD Protocol

2017 ◽  
pp. 52-67
Keyword(s):  
Model Simulation ◽  
Behavioral Outcomes ◽  
Behavioral Model ◽  
Agent Based Modeling ◽  
State Variables ◽  
Agent Based Models ◽  
Agent Based ◽  
Design Concepts ◽  
Modeling Simulation ◽  
Area Of Interest

The ODD Protocol has become a standard for documenting and describing agent based models. The protocol is organized around three main elements, from which the ODD acronym originates: Overview, Design concepts, and Details. This chapter is organized around these primary elements and further broken down into seven sub-elements to provide a clear purpose and understanding of the model simulation. The sub-elements are: Purpose, State Variables and Scales, Process Overview and Scheduling, Design Concepts, Initialization, Input, and Sub-models. The model presented is a proto-agent behavioral model and is utilized in an agent based modeling simulation to help identify possible emergent behavioral outcomes of the populations in the area of interest. By varying the rules governing the interactions of the multinational and incumbent government proto-agents, different strategies can be identified for increasing the effectiveness of those proto-agents and the utilization of resources.

From KISS to TASS Modeling: A Preliminary Analysis of the Segregation Model Incorporated with Spatial Data on Chicago

2015 ◽  
Vol 16 (4) ◽  
pp. 553-573 ◽  
Author(s):  
GAKU ITO ◽  
SUSUMU YAMAKAGE
Keyword(s):  
Residential Segregation ◽  
Spatial Data ◽  
Demographic Data ◽  
Theoretical Models ◽  
Agent Based Modeling ◽  
Agent Based Models ◽  
Agent Based ◽  
The Us ◽  
Us Census ◽  
The City

AbstractThe ‘keep it simple, stupid’ slogan, or the KISS principle has been the basic guideline in agent-based modeling (ABM). While the KISS principle or parsimony is vital in modeling attempts, conventional agent-based models remain abstract and are rarely incorporated or validated with empirical data, leaving the links between theoretical models and empirical phenomena rather loose. This article reexamines the KISS principle and discusses the recent modeling attempts that incorporate and validate agent-based models with spatial (geo-referenced) data, moving beyond the KISS principle. This article also provides a working example of such time and space specified (TASS) agent-based models that incorporates Schelling's (1971) classic model of residential segregation with detailed geo-referenced demographic data on the city of Chicago derived from the US Census 2010.

COMPUTATIONAL FEATURES OF AGENT-BASED MODELS

2005 ◽  
Vol 02 (01) ◽  
pp. 33-48 ◽  
Author(s):  
MASSIMO BERNASCHI ◽  
FILIPPO CASTIGLIONE
Keyword(s):  
Data Structures ◽  
Large Scale ◽  
Agent Based Modeling ◽  
System Response ◽  
Agent Based Models ◽  
Design Data ◽  
Agent Based ◽  
Data Structures And Algorithms ◽  
Immune System Response ◽  
Large Scale Simulations

Agent-based modeling allows the description of very complex systems. To run large scale simulations of agent-based models in a reasonable time, it is crucial to carefully design data structures and algorithms. We describe the main computational features of agent-based models and report about the solutions we adopted in two applications: The simulation of the immune system response and the simulation of the stock market dynamics.

Book Reviews

2017 ◽  
Vol 55 (2) ◽  
pp. 644-647
Keyword(s):  
Agent Based Modeling ◽  
Agent Based Models ◽  
Agent Based ◽  
Practical Guide ◽  
Interacting Agents ◽  
Hamilton College ◽  
Heterogeneous Interacting Agents

Christophre Georges of the Department of Economics, Hamilton College reviews “Economics with Heterogeneous Interacting Agents: A Practical Guide to Agent-Based Modeling,” edited by Alessandro Caiani, Alberto Russo, Antonio Palestrini, and Mauro Gallegati. The Econlit abstract for this book begins: “Text for graduate and PhD students, as well as undergraduates with some knowledge of computers and economics comprises four papers emerging from a workshop on agent-based modeling held by the Dipartimento di Scienze Economiche e Sociali at the Università Politecnica delle Marche. Presents a guide to agent-based models (ABM) and the technicalities that need to be solved in order to evaluate the effect of different rules and their switching.”

scholarly journals Improving agent-based models of diffusion

2016 ◽  
Vol 50 (3/4) ◽  
pp. 639-646 ◽  
Author(s):  
Robert East ◽  
Mark D. Uncles ◽  
Jenni Romaniuk ◽  
Wendy Lomax
Keyword(s):  
Design Methodology ◽  
Agent Based Modeling ◽  
Diffusion Modeling ◽  
Agent Based Models ◽  
Content Type ◽  
Agent Based ◽  
Made In

Purpose This paper aims to review the validation of assumptions made in agent-based modeling of diffusion and the sufficiency (completeness) of the mechanisms assumed to operate. Design/methodology/approach One well-cited paper is examined. Findings Evidence is presented that casts doubt on the assumptions and mechanisms used. A range of mechanisms is suggested that should be evaluated for inclusion in diffusion modeling. Originality/value The need for validation of assumptions has been stressed elsewhere but there has been a lack of examples. This paper provides examples. The stress on the sufficiency of the mechanisms used is new.

scholarly journals GETTING AWAY FROM NUMBERS: USING QUALITATIVE OBSERVATION FOR AGENT-BASED MODELING

2008 ◽  
Vol 11 (02) ◽  
pp. 175-185 ◽  
Author(s):  
LU YANG ◽  
NIGEL GILBERT
Keyword(s):  
Social Sciences ◽  
Quantitative Data ◽  
Qualitative Data ◽  
Participant Observation ◽  
Agent Based Modeling ◽  
Agent Based Models ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Qualitative Observation

Although in many social sciences there is a radical division between studies based on quantitative (e.g. statistical) and qualitative (e.g. ethnographic) methodologies and their associated epistemological commitments, agent-based simulation fits into neither camp, and should be capable of modelling both quantitative and qualitative data. Nevertheless, most agent-based models (ABMs) are founded on quantitative data. This paper explores some of the methodological and practical problems involved in basing an ABM on qualitative participant observation and proposes some advice for modelers.

scholarly journals Simulation of trends in the use of e-payment using agent based models

2020 ◽  
Vol 1 (1) ◽  
pp. 29
Author(s):  
Elanjati Worldailmi ◽  
Ismianti Ismianti
Keyword(s):  
Economic Policy ◽  
Central Bank ◽  
Modeling And Simulation ◽  
Theoretical Basis ◽  
Agent Based Modeling ◽  
Agent Based Models ◽  
Economic Activities ◽  
Agent Based ◽  
Online Sales

Bank Indonesia (BI) as the central bank in Indonesia has launched a movement to use non-cash instruments in conducting transactions on economic activities. The majority of Indonesian people are increasingly ready to trade without cash or cashless society. The country's economic policy factors, the availability of various non-cash payments, and online sales and purchases, encourage the tendency to use non-cash transactions (e-payment). One way to find out these trends is to use a model. Models can help understand and explain real phenomena more easily and efficiently than directly observing. One model that can be used is Agent Based Modeling and Simulation (ABMS). By using ABMS, the development of models with complex behaviors, dependencies, and interactions can be developed more easily. ABMS is able to describe processes, phenomena, and situations. In this study, the factors that influence the tendency to use e-payment are obtained from various references. From these factors, then created a scenario as a sub-purpose of this model. In simulations using ABMS, detailed descriptions explained based on ODD Protocol elements can be more easily understood and complete. ODD systematically evaluates a model. The advantage is that ODD can improve the accuracy of model formulas and make the theoretical basis more visible.

Agent‐Based Modeling

2009 ◽  
Author(s):  
Scott de Marchi ◽  
Scott E. Page
Keyword(s):  
Mathematical Models ◽  
Computational Methods ◽  
Computational Models ◽  
Agent Based Modeling ◽  
Agent Based Models ◽  
Agent Based ◽  
Agent Interactions ◽  
Game Theoretic ◽  
Mathematical Analyses ◽  
The Stability

This article provides a discussion on agent-based modeling. Two examples that show the ability of computational methods to extend game-theoretic results are presented. It then discusses modeling agents, modeling agent interactions, and system behaviour. In addition, it describes how agent-based models differ from and complement mathematical models and concludes with some suggestions for how one might best leverage the strengths of agent-based models to advance political science. Most mathematical analyses of game-theoretic models do not look into the stability and attainability of their equilibria and would be made richer by complementing them with agent-based models that explored those properties. The ability of computational models to test the robustness of formal results would be reason alone to add them to tool kits. As a methodology, agent-based modeling should be considered as in its infancy, its enormous potential limited only by the scientific and creative talents of its practitioners.

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