Agent-Based Modeling of Automobile Producer and Consumer Behavior to Support Design for Market Systems Analysis

2017 ◽  
Author(s):  
Amineh Zadbood ◽  
Steven Hoffenson
Keyword(s):  
Systems Analysis ◽  
Agent Based Modeling ◽  
Emergent Behavior ◽  
Purchasing Decisions ◽  
Support Design ◽  
Agent Based ◽  
Regulatory Strategies ◽  
Design Attributes ◽  
Design For Market Systems ◽  
Future Work

Improving design for market systems analysis relies on understanding the motivations and interactions among producers and consumers. Producers should theoretically develop their strategies for designing new products based on consumer demand and the expected profits from their sales. In this study, an agent-based modeling approach is proposed to simulate consumer and producer behavior for use in market systems analysis, and it is demonstrated through a simplified automobile market. In the model, consumers make heterogeneous purchasing decisions based on product attributes, which provides the producers with insights into their preferences and how to improve upon these design attributes over time. Emergent behavior of the model shows that analyzing the behavior of consumers provides the opportunity for producers to compete which one another with different strategies to improve their designs by investing in technology improvements. This lays the foundation for future work that can model how different business and regulatory strategies, social structures, and policies influence consumer and producer behavior, which in turn influences economic, environmental, and social impacts.

Agent-Based Modeling the Emergent Behavior of A System-of-Systems

2009 ◽  
Vol 19 (1) ◽  
pp. 1581-1590 ◽  
Author(s):  
John C. Hsu ◽  
John R. Clymer ◽  
Jose Garcia ◽  
Efrain Gonzalez
Keyword(s):  
System Of Systems ◽  
Agent Based Modeling ◽  
Emergent Behavior ◽  
Agent Based

scholarly journals Agent-based modeling and simulation of emergent behavior in air transportation

2013 ◽  
Vol 1 (1) ◽  
Author(s):  
Soufiane Bouarfa ◽  
Henk AP Blom ◽  
Richard Curran ◽  
Mariken HC Everdij
Keyword(s):  
Modeling And Simulation ◽  
Air Transportation ◽  
Agent Based Modeling ◽  
Emergent Behavior ◽  
Agent Based

Emergent Behavior in a Population of Thermostatically Controlled Loads With Peer-to-Peer Communication

2019 ◽  
Author(s):  
Ryan Schwartz ◽  
John F. Gardner
Keyword(s):  
Demand Response ◽  
Air Conditioning ◽  
Large Scale ◽  
Large Population ◽  
Agent Based Modeling ◽  
Emergent Behavior ◽  
Peer Communication ◽  
Agent Based ◽  
Thermostatically Controlled Loads ◽  
Residential Air Conditioning

Abstract Thermostatically controlled loads (TCLs) are often considered as a possible resource for demand response (DR) events. However, it is well understood that coordinated control of a large population of previously un-coordinated TCLs may result in load synchronization that results in higher peaks and large uncontrolled swings in aggregate load. In this paper we use agent based modeling to simulate a number of residential air conditioning loads and allow each to communicate a limited amount of information with their nearest neighbors. As a result, we document emergent behavior of this large scale, distributed and nonlinear system. Using the techniques described here, the population of TCLs experienced up to a 30% reduction in peak demand following the DR event. This behavior is shown to be beneficial to the goals of balancing the grid and integrating increasing penetration of variable generators.

Spatial Units as Agents: Making the Landscape an Equal Player in Agent-Based Simulations

2002 ◽  
Author(s):  
Paul Box
Keyword(s):  
Cell Structure ◽  
Natural Extension ◽  
Data Gathering ◽  
Agent Based Modeling ◽  
Emergent Behavior ◽  
Modeling Tools ◽  
Bottom Up ◽  
Agent Based ◽  
Landscape Processes ◽  
Dynamic Landscape

Agent-based modeling has generated considerable interest in recent years as a tool for exploring many of the processes that can be modeled as bottom up processes. This has accelerated with the availability of software packages, such as Swarm and StarLogo, that allow for relatively complex simulations to be constructed by researchers with limited computer-programming backgrounds. A typical use of agent-based models is to simulate scenarios where large numbers of individuals are inhabiting a landscape, interacting with their landscape and each other by relatively simple rules, and observing the emergent behavior of the system (population) over time. It has been a natural extension in this sort of a study to create a landscape from a “real world” example, typically imported through a geographic information system (GIS). In most cases, the landscape is represented either as a static object, or a “stage” upon which the agents act (see Briggs et al. , Girnblett et al., and Remm). In some cases, an approximation of a dynamic landscape has been added to the simulation in a way that is completely exogenous to the population being simulated; the dynamic conditions are read from historical records, in effect “playing a tape” of conditions, to which the population reacts through time (such as Dean et al. and Kohler et al. ). There has also been many simulations where dynamic landscape processes have been modeled through “bottom up” processes, where localized processes in landscapes are simulated, and the global emergent processes are observed. Topmodel is a Fortran-based implementation of this concept for hydrologic processes; and PCRaster has used similar software constructs to simulate a variety of landscape processes, with sophisticated visualization and data-gathering tools. In both of these examples, the landscape is represented as a regular lattice or cell structure. There are also many examples of “home grown” tools (simulations created for a specific project), applying cellular automata (CA) rules to landscapes to simulate urban growth, wildfire , lava flows, and groundwater flow. There are also examples of how agent-based modeling tools were employed to model dynamic landscape processes such as forest dynamics, i.e., Arborgames. In these models the landscape was the object of the simulation, and free-roaming agents were not considered as part of the model.

QUANTIFYING THE IMPACT OF CONGESTION ON CONSTRUCTION LABOR PRODUCTIVITY USING AGENT-BASED MODELING

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Mohammad Rahal ◽  
Hiam Khoury
Keyword(s):  
Labor Productivity ◽  
Spatial Configuration ◽  
Quantitative Model ◽  
Agent Based Modeling ◽  
Direct Impact ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Future Work ◽  
The Impact ◽  
Construction Trades

Several findings from the construction field stipulate that productivity falloffs are primarily management-related; however, this notion does not consider the direct impact of these same management decisions on the workers themselves. For instance, the planning of the workspace layout delves in a spatial configuration which if not properly managed can potentially result in congestion that, in turn, directly affects labor productivity. Previous research efforts developed models to analyze the effect of congestion on labor productivity but failed to capture all the complexities of this mechanism and its dynamics. Therefore, this paper puts forward the groundwork of an agent-based simulation model (ABM) and presents work targeted at quantifying the impact of congestion on the productivity of construction crews. More specifically, the ABM model takes into account two construction trades working in the same area and tackles five scenarios each depicting different congestion and interaction levels. At the heart of this simulation is a quantitative model that defines essential congestion metrics and outputs space interference values. Experiments were conducted and results highlighted that the higher the space interference values the less productive the crews become. Additionally, these values will constitute an integral part in future work when studying the impact of congestion on the crews' learning curve, whereby the latter being a major gauge for levels of productivity.

Multi-Agent Based Modeling Simulation about VANET

2013 ◽  
Vol 760-762 ◽  
pp. 680-684
Author(s):  
Shan Shan Wan ◽  
Dong Liang Wang ◽  
Qing Cao
Keyword(s):  
Ad Hoc Network ◽  
Ad Hoc ◽  
Agent Based Modeling ◽  
Emergent Behavior ◽  
Self Organization ◽  
Working Mechanism ◽  
Agent Based ◽  
Modeling Simulation ◽  
Large Numbers ◽  
Multi Agent

The self-organization characteristics and the interaction between a large numbers of self-organizing vehicles are complexity, to obtain a more accurate model of vehicular Ad-hoc network (VANET) and obtain a more profound comprehension of the complex behavior working mechanism of the vehicle in the VANET environment multi-agent based and bottom-up modeling approach is proposed here. It aims to describe the dynamics of VANET caused by the different behaviors of vehicular. The simulation tool for vehicular misbehaviors is developed with multi-agent. It aims to and be able to effectively reproduce the real VANET scene. Though the multi-agent based modeling the emergent behavior and sudden existing behaviors of VANET entities are well reflected.

scholarly journals Enabling the Analysis of Emergent Behavior in Future Electrical Distribution Systems Using Agent-Based Modeling and Simulation

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Sonja Kolen ◽  
Stefan Dähling ◽  
Timo Isermann ◽  
Antonello Monti
Keyword(s):  
Modeling And Simulation ◽  
Distribution Systems ◽  
Large Scale ◽  
Agent Based Modeling ◽  
Emergent Behavior ◽  
System Control ◽  
Distribution Grid ◽  
Agent Based ◽  
Electrical Distribution Systems ◽  
Electrical Distribution

In future electrical distribution systems, component heterogeneity and their cyber-physical interactions through electrical lines and communication lead to emergent system behavior. As the distribution systems represent the largest part of an energy system with respect to the number of nodes and components, large-scale studies of their emergent behavior are vital for the development of decentralized control strategies. This paper presents and evaluates DistAIX, a novel agent-based modeling and simulation tool to conduct such studies. The major novelty is a parallelization of the entire model—including the power system, communication system, control, and all interactions—using processes instead of threads. Thereby, a distribution of the simulation to multiple computing nodes with a distributed memory architecture becomes possible. This makes DistAIX scalable and allows the inclusion of as many processing units in the simulation as desired. The scalability of DistAIX is demonstrated by simulations of large-scale scenarios. Additionally, the capability of observing emergent behavior is demonstrated for an exemplary distribution grid with a large number of interacting components.

scholarly journals Heterogeneity: method and applications for complex systems analysis

2022 ◽  
Vol 2159 (1) ◽  
pp. 012013
Author(s):  
J M Redondo ◽  
J S Garcia ◽  
C Bustamante-Zamudio ◽  
M F Pereira ◽  
H F Trujillo
Keyword(s):  
Machine Learning ◽  
Complex Systems ◽  
Research And Development ◽  
Systems Analysis ◽  
Ecological Systems ◽  
Agent Based Modeling ◽  
Time Lags ◽  
Agent Based ◽  
Physical Systems ◽  
Sustainability Analysis

Abstract Socio-ecological systems like another physical systems are complex systems in which are required methods for analyzes their non-linearities, thresholds, feedbacks, time lags, and resilience. This involves understanding the heterogeneity of the interactions in time and space. In this article, we carry out the proposition and demonstration of two methods that allow the calculation of heterogeneity in different contexts. The practical effectiveness of the methods is presented through applications in sustainability analysis, land transport, and governance. It is concluded that the proposed methods can be used in various research and development areas due to their ease of being considered in broad modeling frameworks as agent-based modeling, system dynamics, or machine learning, although it could also be used to obtain point measurements only by replacing values.

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