Agent-based Modelling of Tourists Destination Decision-Making Process

This paper describes a model to simulate the decision-making process of consumers that adopts technology within a dynamic social network. The proposed model use theories and tools from the psychology of consumer behavior, social networks and complex dynamical systems like the Consumat framework and fuzzy logic. The model has been adjusted using real data, tested with the automobile market and it can recreate trends like those described in the world market.

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Research on Agent-Based Energy Saving System of Civil

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.816-817.1220 ◽

2013 ◽

Vol 816-817 ◽

pp. 1220-1224

Author(s):

Shou Cai Ma

Keyword(s):

Decision Making ◽

Decision Support ◽

Energy Saving ◽

Decision Making Process ◽

Agent Based ◽

Adjustment Mechanism ◽

Building Systems ◽

System Components ◽

System Energy ◽

The City

This paper deeply analyzes the urban civil system, energy-saving decision-making mechanism, the system components and the related energy-saving anti-adjustment mechanism based on the proposed energy-saving urban civil system's basis. It also presents the classification decision-making and decision-making process for the civil on various components on building systems in decision-making energy-saving features on the system proposed civil heat, urban heating network and the energy saving civil monomer decision making. It also builds the decision support for the city civil agent-based energy-saving system, realizing the basic institutions of the agent to propose the energy-saving urban civil decision.

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Agent-based decision-making process in airport ground handling management

Logistics Research ◽

10.1007/s12159-011-0052-y ◽

2011 ◽

Vol 3 (2-3) ◽

pp. 133-143 ◽

Cited By ~ 5

Author(s):

Pablo García Ansola ◽

Andrés García Higuera ◽

José Manuel Pastor ◽

F. Javier Otamendi

Keyword(s):

Decision Making ◽

Decision Making Process ◽

Agent Based ◽

Ground Handling

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Planning horizon affects prophylactic decision-making and epidemic dynamics

10.1101/069013 ◽

2016 ◽

Author(s):

Luis G. Nardin ◽

Craig R. Miller ◽

Benjamin J. Ridenhour ◽

Stephen M. Krone ◽

Paul Joyce ◽

...

Keyword(s):

Decision Making ◽

Perceived Risk ◽

Planning Horizon ◽

Decision Making Process ◽

Risk Of Infection ◽

Agent Based ◽

Epidemic Dynamics ◽

Behavioral Decision ◽

Protection Efficacy ◽

Planning Horizons

AbstractHuman behavior can change the spread of infectious disease. There is limited understanding of how the time in the future over which individuals make a behavioral decision, their planning horizon, affects epidemic dynamics. We developed an agent-based model (along with an ODE analog) to explore the decision-making of self-interested individuals on adopting prophylactic behavior. The decision-making process incorporates prophylaxis efficacy and disease prevalence with individuals' payoffs and planning horizon. Our results show that for short and long planning horizons individuals do not consider engaging in prophylactic behavior. In contrast, individuals adopt prophylactic behavior when considering intermediate planning horizons. Such adoption, however, is not always monotonically associated with the prevalence of the disease, depending on the perceived protection efficacy and the disease parameters. Adoption of prophylactic behavior reduces the peak size while prolonging the epidemic and potentially generates secondary waves of infection. These effects can be made stronger by increasing the behavioral decision frequency or distorting an individual’s perceived risk of infection.

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Agent based model

Radioprotection ◽

10.1051/radiopro/2020031 ◽

2020 ◽

Vol 55 ◽

pp. S187-S191 ◽

Cited By ~ 1

Author(s):

S. Bai ◽

W. Raskob ◽

T. Müller

Keyword(s):

Decision Making ◽

Emergency Management ◽

Intelligent Agents ◽

Decision Making Process ◽

Agent Based Model ◽

Radiological Emergency ◽

Agent Based ◽

Computational Implementation ◽

Protection Strategies ◽

Decision Making Processes

In the CONFIDENCE project, we developed an agent based model (ABM) to simulate the decision making process involving stakeholders of different interests. Our model aims to support decisions on the most suitable protection strategies in different accident phases. The intelligent agents and the models of the negotiation/voting process are described in the paper. Given five scenarios, the numerical results from the computational implementation of the ABM are visualized and analysed in order to better understand the negotiation and voting processes. Our ABM can be expanded in order to support the decision making processes of many different stakeholders of various types of risk management apart from nuclear and radiological emergency management.

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Simulation modeling of the smart city system: The concept, methods, and cases

Economic Analysis Theory and Practice ◽

10.24891/ea.19.2.226 ◽

2020 ◽

Vol 19 (2) ◽

pp. 226-250 ◽

Cited By ~ 2

Author(s):

V.L. Makarov ◽

R.A. Bakhtizin ◽

G.L. Beklaryan ◽

A.S. Akopov

Keyword(s):

Decision Making ◽

Urban Planning ◽

System Dynamics ◽

Smart City ◽

Simulation Modeling ◽

Simulation Models ◽

Agent Based Modeling ◽

Decision Making Process ◽

Agent Based ◽

City System

Subject. The research investigates key processes of urban life and its maintenance, including food supply, infrastructure, fire security, quality and accessibility of medical services, etc. The article also discusses the creation of a system supporting the Smart City decision-making process. Objectives. The research develops methods and tools to manage the Smart City system through system dynamics and agent-based modeling. Methods. Using simulation modeling, namely system dynamics and agent-based modeling (supported via Powersim and AnyLogic), we evaluate how multiple guiding parameters influence crucial characteristics of the Smart City system. Results. We devised an approach to designing the Smart City system through methods of system dynamics and agent-based modeling (supported via Powersim and AnyLogic) intended to streamline the decision making process for reasonable urban planning. Conclusions and Relevance. We propose the consolidated architecture of the Smart City decision-making system integrating the simulation models, data storage and city monitoring subsystem. The article describes the cases of simulation models implemented via Powersim and AnyLogic to support rational urban planning. The simulation models will significantly improve the quality of urban environment, satisfy the demand for food products, provide access to healthcare services and ensure effective rescue actions in case of emergency.

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