scholarly journals A Statistical Examination of Distinct Characteristics Influencing the Performance of Vector-Borne Epidemiological Agent-Based Simulation Models

Modelling ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 166-196
Author(s):  
Anna Paula Galvão Scheidegger ◽  
Henrique dos Santos Maxir ◽  
Amarnath Banerjee
Keyword(s):  
Decision Making ◽  
Data Quality ◽  
Human Behavior ◽  
Simulation Models ◽  
Disease Spread ◽  
Epidemiological Models ◽  
Agent Based Simulation ◽  
Single Strain ◽  
Agent Based ◽  
Vector Borne

The spread of infectious diseases is a complex system in which pathogens, humans, the environment, and sometimes vectors interact. Mathematical and simulation modelling is a suitable approach to investigate the dynamics of such complex systems. The 2019 novel coronavirus (COVID-19) pandemic reinforced the importance of agent-based simulation models to quickly and accurately provide information about the disease spread that would be otherwise hard or risky to obtain, and how this information can be used to support infectious disease control decisions. Due to the trade-offs between complexity, time, and accuracy, many assumptions are frequently made in epidemiological models. With respect to vector-borne diseases, these assumptions lead to epidemiological models that are usually bounded to single-strain and single-vector scenarios, where human behavior is modeled in a simplistic manner or ignored, and where data quality is usually not evaluated. In order to leverage these models from theoretical tools to decision-making support tools, it is important to understand how information quality, human behavior, multi-vector, and multi-strain affect the results. For this, an agent-based simulation model with different parameter values and different scenarios was considered. Its results were compared with the results of a traditional compartmental model with respect to three outputs: total number of infected individuals, duration of the epidemic, and number of epidemic waves. Paired t-test showed that, in most cases, data quality, human behavior, multi-vector, and multi-strain were characteristics that lead to statistically different results, while the computational costs to consider them were not high. Therefore, these characteristics should be investigated in more detail and be accounted for in epidemiological models in order to obtain more reliable results that can assist the decision-making process during epidemics.

EXTORTION RACKET SYSTEMS AS TARGETS FOR AGENT-BASED SIMULATION MODELS. COMPARING COMPETING SIMULATION MODELS AND EMPRICAL DATA

2015 ◽  
Vol 18 (05n06) ◽  
pp. 1550014 ◽  
Author(s):  
KLAUS G. TROITZSCH
Keyword(s):  
Decision Making ◽  
Input Parameter ◽  
Simulation Models ◽  
Central Research ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Legal Norms ◽  
Hells Angels ◽  
Research Questions ◽  
Set Up

Extortion racketeering is an industry not only practiced by mafia, but also in groups such as hells angels. It occurs in a complex setting of criminals, victims, police and society, and its framework is set up by legal norms as well as informal norms of the actor groups involved. The paper presents two agent-based simulation models which differ with respect to the decision making mode, which is either stochastical with fixed probabilities or deliberative where decisions depend on utility considerations and norms learned during the process. The central research questions of the paper — beside the question how extortion racket system can be appropriately modeled — concern the divergence of the results of the two model versions, the comparison of the input parameter combinations, the motivations of input parameters and the validation of the results by comparing them to available empirical data.

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.

An agent-based simulation model for IING’s adoption from a perspective of kinetic energy and potential energy

Kybernetes ◽  
2018 ◽  
Vol 47 (3) ◽  
pp. 605-635 ◽  
Author(s):  
Li Wang ◽  
Qingpu Zhang
Keyword(s):  
Decision Making ◽  
Kinetic Energy ◽  
Potential Energy ◽  
Agent Based Simulation ◽  
Content Type ◽  
Agent Based ◽  
Factors Influencing ◽  
Social Physics ◽  
Physical Energy ◽  
Energy Theory

Purpose Internet-based intangible network good (IING) has revolutionized multiple industries in recent years. This paper aims to reveal the laws of consumer’s decision-making on IING from a perspective of kinetic energy and potential energy. Design/methodology/approach In this paper, 4 aspects and 17 factors influencing IING adoption were generalized. Based on the theory of social physics, an agent-based simulation model, introducing physical energy theory to depict consumer’s decision-making, was built. An agent’s kinetic energy reflects the agent’s perceived effect of mass media on the agent’s decision-making on IING adoption. An agent’s potential energy reflects the agent’s perceived effect of social interactions on the agent’s decision-making on the adoption of IING. An agent’s final energy is the sum of the kinetic energy and potential energy, which reflects the agent’s final decision. Findings Some factors mainly influence the diffusion velocity, while other factors have a dramatic impact on both diffusion velocity and diffusion scale. The agent’s personality can make a difference at the early and middle stages of IING adoption, but a faint impact at the later stage because of the effects of network externalities and word of mouth. There is a critical value of the number of initial adopters which can dramatically speed up IING adoption. Practical implications This study provides new insights for firms on the effects of factors influencing consumers’ decision-making on IING adoption. Originality/value This paper defines a new kind of innovation, IING, and generalizes IING’s special characteristics. As a new application of social physics, the physical energy theory has been creatively introduced to depict consumer’s decision-making on IING adoption. A kinetic and potential energy model of IING adoption has been built. Based on simulation experiments, new insights of IING adoption have been gained.

Scoping the Future with Theory-Driven Models—Where’s the Uncertainty?: Commentary on Lustick and Tetlock 2021

2021 ◽  
Author(s):  
David R. Mandel
Keyword(s):  
National Security ◽  
Policy Decision ◽  
Simulation Models ◽  
Insufficient Data ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Strategic Intelligence ◽  
The Future ◽  
Policy Decision Making ◽  
Rich Data

Lustick and Tetlock outline an intellectually ambitious approach to scoping the future. They are particularly interested in sectors of national security and foreign policy decision-making that require anticipatory strategic intelligence that is difficult to produce because there is insufficient data, even if relevant theories are available. They propose that in these theory-rich/data-impoverished cases, there can be great value in developing agent-based simulation models that incorporate probabilistic rules that cohere with postulates of the theory or theories that are brought to bear on the intelligence challenge. This is the gist of the “simulation manifesto.” The aim of this commentary is to focus on the assessment and representation of key uncertainties in such models and I outline several ways in which uncertainty may arise in the process of simulation model construction.

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