Quantitative agent-based firm dynamics simulation with parameters estimated by financial and transaction data analysis

2007 ◽  
Vol 375 (2) ◽  
pp. 651-667 ◽  
Author(s):  
Yuichi Ikeda ◽  
Wataru Souma ◽  
Hideaki Aoyama ◽  
Hiroshi Iyetomi ◽  
Yoshi Fujiwara ◽  
...  
Keyword(s):  
Data Analysis ◽  
Firm Dynamics ◽  
Dynamics Simulation ◽  
Agent Based ◽  
Transaction Data

scholarly journals Integrated IEW-TOPSIS and Fire Dynamics Simulation for Agent-Based Evacuation Modeling in Industrial Safety

Safety ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. 47
Author(s):  
Wattana Chanthakhot ◽  
Kasin Ransikarbum
Keyword(s):  
Emergency Evacuation ◽  
Industrial Sector ◽  
Industrial Safety ◽  
Dynamics Simulation ◽  
Entropy Weight ◽  
Fire Dynamics ◽  
Evacuation Simulation ◽  
Evacuation Modeling ◽  
Agent Based ◽  
The Impact

Emergency events in the industrial sector have been increasingly reported during the past decade. However, studies that focus on emergency evacuation to improve industrial safety are still scarce. Existing evacuation-related studies also lack a perspective of fire assembly point’s analysis. In this research, location of assembly points is analyzed using the multi-criteria decision analysis (MCDA) technique based on the integrated information entropy weight (IEW) and techniques for order preference by similarity to ideal solution (TOPSIS) to support the fire evacuation plan. Next, we propose a novel simulation model that integrates fire dynamics simulation coupled with agent-based evacuation simulation to evaluate the impact of smoke and visibility from fire on evacuee behavior. Factors related to agent and building characteristics are examined for fire perception of evacuees, evacuees with physical disabilities, escape door width, fire location, and occupancy density. Then, the proposed model is applied to a case study of a home appliance factory in Chachoengsao, Thailand. Finally, results for the total evacuation time and the number of remaining occupants are statistically examined to suggest proper evacuation planning.

Agents in bioinformatics

2005 ◽  
Vol 20 (2) ◽  
pp. 117-125 ◽  
Author(s):  
MICHAEL LUCK ◽  
EMANUELA MERELLI
Keyword(s):  
Data Analysis ◽  
Simulation Models ◽  
Biological Data ◽  
System Modelling ◽  
Agent Based Simulation ◽  
Huge Amount ◽  
Agent Based ◽  
Information Agents ◽  
New Knowledge ◽  
Definition Of

The scope of the Technical Forum Group (TFG) on Agents in Bioinformatics (BIOAGENTS) was to inspire collaboration between the agent and bioinformatics communities with the aim of creating an opportunity to propose a different (agent-based) approach to the development of computational frameworks both for data analysis in bioinformatics and for system modelling in computational biology. During the day, the participants examined the future of research on agents in bioinformatics primarily through 12 invited talks selected to cover the most relevant topics. From the discussions, it became clear that there are many perspectives to the field, ranging from bio-conceptual languages for agent-based simulation, to the definition of bio-ontology-based declarative languages for use by information agents, and to the use of Grid agents, each of which requires further exploration. The interactions between participants encouraged the development of applications that describe a way of creating agent-based simulation models of biological systems, starting from an hypothesis and inferring new knowledge (or relations) by mining and analysing the huge amount of public biological data. In this report we summarize and reflect on the presentations and discussions.

A prototype cloud-based reproducible data analysis and visualization platform for outputs of agent-based models

2017 ◽  
Vol 96 ◽  
pp. 172-180 ◽  
Author(s):  
Xiongbing Jin ◽  
Kirsten Robinson ◽  
Allen Lee ◽  
J. Gary Polhill ◽  
Calvin Pritchard ◽  
...  
Keyword(s):  
Data Analysis ◽  
Agent Based Models ◽  
Agent Based

scholarly journals Modeling the epidemic dynamics of COVID-19: Agent-based approach including molecular dynamics simulation and SEIR type methods

2020 ◽  
Author(s):  
Amir Lohrasebi ◽  
Fatemeh Aghaei
Keyword(s):  
Human Mobility ◽  
Local Population ◽  
Dynamics Simulation ◽  
Agent Based ◽  
Epidemic Dynamics ◽  
Everyday Activities ◽  
Health Strategy ◽  
Virus Spreading ◽  
Flexible Model ◽  
Individual Human

Abstract In this study we developed a SEIR model, including social interactions and individual human mobility in everyday activities. For this purpose, daily mobility of people was considered by using the molecular dynamic method and the virus spreading was modeled employing the ordinary SEIR scheme. Utilizing this model, the variation of population size, density, and health strategy as well as the effect of busy places such as malls, were considered. The results show that, our flexible model is able to consider the effects of different parameters such as distance between peoples, local population density and health strategy in the outbreak.

scholarly journals METODE CART UNTUK MENGIDENTIFIKASI FAKTOR-FAKTOR YANG MEMENGARUHI WAKTU PEMBELIAN KENDARAAN KEDUA

2021 ◽  
Vol 10 (2) ◽  
pp. 140-151
Author(s):  
Eka Setiawaty ◽  
Farit Mochamad Afendi ◽  
Cici Suhaeni
Keyword(s):  
Data Analysis ◽  
Regression Tree ◽  
Transaction Data ◽  
Independent Variables ◽  
Group A ◽  
Classification And Regression ◽  
Group B ◽  
The Right ◽  
The Relationship ◽  
Splitting Process

Increased competition between personal vehicle dealers make them need strategies to hold their customers and increase their sales. One of the strategies they could apply is prospecting their customers at the right time. We could predict the right time by identifying the relationship between the length of their purchase time and its factors based on the transaction data of Z Company from year 2002 to 2015 using Classification and Regression Trees (CART). Data analysis is separated between groups of customers who made the second purchase maximum of 10 years after the first purchase (group A) and more than 10 years after the first purchase (group B). Group A’s regression tree produces 8 terminal nodes with MAD value 1.84 years. The independent variables that plays a role are tenor, job, age, and brand. Group B’s regression tree produces 4 terminal nodes. Authorized service and job come out as independent variables which affect the splitting process. MAD value for Group B’s regression tree is 0.56 years.

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