scholarly journals Remarks on the Behavior of an Agent-Based Model of Spatial Distribution of Species

2021 ◽  
Vol 5 (2) ◽  
pp. 37-49
Author(s):  
João Bioco ◽  
Paula Prata ◽  
Fernando Cánovas ◽  
Paulo Fazendeiro
Keyword(s):  
Complex System ◽  
Spatial Distribution ◽  
Life Cycle ◽  
Point Of View ◽  
Emergent Behavior ◽  
Model Parameters ◽  
Simulation Approach ◽  
Agent Based Model ◽  
Agent Based ◽  
Environment Variables

Agent-based models have gained considerable notoriety in ecological modeling as well as in several other fields yearning for the ability to capture the emergent behavior of a complex system in which individuals interact with each other and with their environment. These models are implemented by applying a bottom-up approach, where the entire behavior of the system emerges from the local interaction between their components (agents or individuals). Usually, these interactions between individuals and their enclosing environment are modeled by very simple local rules. From the conceptual point of view, another appealing characteristic of this simulation approach is that it is well aligned with the reality whenever the system is composed of a multitude of individuals (behavioral units) that can be flexibly combined and placed in the environment. Due to their inherent flexibility, and despite of their simplicity, it is necessary to pay attention to the adjustments in their parameters which may result in unforeseen changes on the overall behavior of these models. In this paper we study the behavior of an agent-based model of spatial distribution of species, by analyzing the effects of the model parameters and the implications of the environment variables (that compose the environment where the species lives) on the models’ output. The presented experiments show that the behavior of the model depends mainly on the conditions of the environment where the species live, and the main parameters presented in life cycle of the species.

An Autonomous Multi-Agent Simulation Model for Acute Inflammatory Response

2013 ◽  
pp. 218-233 ◽  
Author(s):  
John Wu ◽  
David Ben-Arieh ◽  
Zhenzhen Shi
Keyword(s):  
Simulation Model ◽  
Focal Point ◽  
Therapeutic Interventions ◽  
Prognostic Indicators ◽  
Simulation Approach ◽  
Agent Based Model ◽  
Agent Based ◽  
Initial Stage ◽  
Agent Simulation ◽  
Multi Agent

This research proposes an agent-based simulation model combined with the strength of systemic dynamic mathematical model, providing a new modeling and simulation approach of the pathogenesis of AIR. AIR is the initial stage of a typical sepsis episode, often leading to severe sepsis or septic shocks. The process of AIR has been in the focal point affecting more than 750,000 patients annually in the United State alone. Based on the agent-based model presented herein, clinicians can predict the sepsis pathogenesis for patients using the prognostic indicators from the simulation results, planning the proper therapeutic interventions accordingly. Impressively, the modeling approach presented creates a friendly user-interface allowing physicians to visualize and capture the potential AIR progression patterns. Based on the computational studies, the simulated behavior of the agent–based model conforms to the mechanisms described by the system dynamics mathematical models established in previous research.

Agent Based Model to Analyze Consumer Behavior in Consuming the Electricity Energy

2015 ◽  
Vol 72 (4) ◽  
Author(s):  
Erma Suryani ◽  
Rully Agus Hendrawan ◽  
Umi Salama ◽  
Lily Puspa Dewi
Keyword(s):  
Consumer Behavior ◽  
Simulation Model ◽  
Environmental Awareness ◽  
Electricity Consumption ◽  
Point Of View ◽  
Agent Based Model ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Save Energy ◽  
The Impact

Several studies have been conducted regarding save energy in consuming the electricity through the simple changes in routines and habits. In the case of electricity consumption, consumer behavior might influenced by several factors such as consumer profession, season, and environmental awareness. In this paper, we developed an Agent Based Model (ABM) to analyze the behavior of different agents in consuming the electricity energy for each type of profession (agent) as well as their interaction with the environment. This paper demonstrates a prototype agent based simulation model to estimate the electricity consumption based on the existing condition and some scenarios to reduce the electricity consumption from consumer point of view. From the scenario results, we analyzed the impact of the save energy to increase the electrification ratio. 

scholarly journals A MODEL OF DYNAMIC REWIRING AND KNOWLEDGE EXCHANGE IN R&D NETWORKS

2016 ◽  
Vol 19 (01n02) ◽  
pp. 1650004 ◽  
Author(s):  
MARIO V. TOMASELLO ◽  
CLAUDIO J. TESSONE ◽  
FRANK SCHWEITZER
Keyword(s):  
Network Performance ◽  
Formation Rate ◽  
Knowledge Exchange ◽  
Model Parameters ◽  
Agent Based Model ◽  
Knowledge Space ◽  
Interaction Radius ◽  
Agent Based ◽  
Knowledge Stocks ◽  
Alliance Partners

This paper investigates the process of knowledge exchange in inter-firm Research and Development (R&D) alliances by means of an agent-based model. Extant research has pointed out that firms select alliance partners considering both network-related and network-unrelated features (e.g., social capital versus complementary knowledge stocks). In our agent-based model, firms are located in a metric knowledge space. The interaction rules incorporate an exploration phase and a knowledge transfer phase, during which firms search for a new partner and then evaluate whether they can establish an alliance to exchange their knowledge stocks. The model parameters determining the overall system properties are the rate at which alliances form and dissolve and the agents’ interaction radius. Next, we define a novel indicator of performance, based on the distance traveled by the firms in the knowledge space. Remarkably, we find that — depending on the alliance formation rate and the interaction radius — firms tend to cluster around one or more attractors in the knowledge space, whose position is an emergent property of the system. And, more importantly, we find that there exists an inverted U-shaped dependence of the network performance on both model parameters.

scholarly journals Understanding transmission and control of the pork tapeworm with CystiAgent: a spatially explicit agent-based model

2020 ◽  
Author(s):  
Ian Wright Pray ◽  
Wayne Wakeland ◽  
William Pan ◽  
William E. Lambert ◽  
Hector H. Garcia ◽  
...  
Keyword(s):  
Taenia Solium ◽  
Model Performance ◽  
Rank Correlation ◽  
Public Health Problem ◽  
Sensitivity Analyses ◽  
Transmission Model ◽  
Spatially Explicit ◽  
Model Parameters ◽  
Agent Based Model ◽  
Agent Based

Abstract Background The pork tapeworm ( Taenia solium ) is a serious public health problem in rural low-resource areas of Latin America, Africa, and Asia, where the associated conditions of nuerocysticercosis (NCC) and porcine cysticercosis cause substantial health and economic harms. An accurate and validated transmission model for T. solium would serve as an important new tool for control and elimination, as it would allow for comparison of available intervention strategies, and prioritization of the most effective strategies for control and elimination efforts. Methods We developed a spatially-explicit agent-based model (ABM) for T. solium (“CystiAgent”) that differs from prior T. solium models by including a spatial framework and behavioral parameters such as pig roaming, open human defecation, and human travel. In this article, we introduce the structure and function of the model, describe the data sources used to parameterize the model, and apply sensitivity analyses (Latin hypercube sampling–partial rank correlation coefficient (LHS-PRCC)) to evaluate model parameters. Results LHS-PRCC analysis of CystiAgent found that the parameters with the greatest impact on model uncertainty were the roaming range of pigs, the infectious duration of human taeniasis, use of latrines, and the set of “tuning” parameters defining the probabilities of infection in humans and pigs given exposure to T. solium. Conclusions CystiAgent is a novel ABM that has the ability to model spatial and behavioral features of T. solium transmission not available in other models. There is a small set of impactful model parameters that contribute uncertainty to the model and may impact the accuracy of model projections. Field and laboratory studies to better understand these key components of transmission may help reduce uncertainty, while current applications of CystiAgent may consider calibration of these parameters to improve model performance. These results will ultimately allow for improved interpretation of model validation results, and usage of the model to compare available control and elimination strategies for T. solium .

An agent-based model approach to multi-phase life-cycle for contact inhibited, anchorage dependent cells

2014 ◽  
Vol 6 (4) ◽  
pp. 312-322
Author(s):  
Ross. D. Hoehn ◽  
Ashley. M. Schreder ◽  
Mohammed Fayez Al Rez ◽  
Sabre Kais
Keyword(s):  
Life Cycle ◽  
Agent Based Model ◽  
Agent Based ◽  
Multi Phase ◽  
Model Approach

Continuum versus discrete model: a comparison for multicellular tumour spheroids

2006 ◽  
Vol 364 (1843) ◽  
pp. 1443-1464 ◽  
Author(s):  
Gernot Schaller ◽  
Michael Meyer-Hermann
Keyword(s):  
Growth Curves ◽  
Cell Number ◽  
Nutrient Concentrations ◽  
Model Parameters ◽  
Agent Based Model ◽  
Growth Data ◽  
Agent Based ◽  
Pde Model ◽  
Viable Cells ◽  
Tumour Spheroids

We study multicellular tumour spheroids with a continuum model based on partial differential equations (PDEs). The model includes viable and necrotic cell densities, as well as oxygen and glucose concentrations. Viable cells consume nutrients and become necrotic below critical nutrient concentrations. Proliferation of viable cells is contact-inhibited if the total cellular density locally exceeds volume carrying capacity. The model is discussed under the assumption of spherical symmetry. Unknown model parameters are determined by simultaneously fitting the cell number to several experimental growth curves for different nutrient concentrations. The outcome of the PDE model is compared with an analogous off-lattice agent-based model for tumour growth. It turns out that the numerically more efficient PDE model suffices to explain the macroscopic growth data. As in the agent-based model, we find that the experimental growth curves are only reproduced when a necrotic core develops. However, evaluation of morphometric properties yields differences between the models and the experiment.

scholarly journals An Agent-Based Model of monocyte differentiation into Tumour-Associated Macrophages in Chronic Lymphocytic Leukemia

2021 ◽  
Author(s):  
Nina Verstraete ◽  
Malvina Marku ◽  
Marcin Domagala ◽  
Julie Bordenave ◽  
H&eacutelène Arduin ◽  
...  
Keyword(s):  
Chronic Lymphocytic Leukemia ◽  
Cancer Cells ◽  
Mononuclear Cells ◽  
Lymphocytic Leukemia ◽  
Leukemic Cells ◽  
Patient Specific ◽  
Model Parameters ◽  
Agent Based Model ◽  
Agent Based ◽  
Monocyte Differentiation

Monocyte-derived macrophages are immune cells which help maintain tissue homeostasis and defend the organism against pathogens. In solid tumours, recent studies have uncovered complex macrophage populations, among which tumour-associated macrophages, supporting tumorigenesis through multiple cancer hallmarks such as immunosuppression, angiogenesis or matrix remodelling. In the case of chronic lymphocytic leukemia, these macrophages are known as nurse-like cells and have been shown to protect leukemic cells from spontaneous apoptosis and contribute to their chemoresistance. We propose an agent-based model of monocytes differentiation into nurse-like cells upon contact with leukemic B cells in-vitro. We studied monocyte differentiation and cancer cells survival dynamics depending on diverse hypotheses on monocytes and cancer cells relative proportions, sensitivity to their surrounding environment and cell-cell interactions. Peripheral blood mononuclear cells from patients were cultured and monitored during 13 days to calibrate the model parameters, such as phagocytosis efficiency, death rates or protective effect from the nurse-like cells. Our model is able to reproduce experimental results and predict cancer cells survival dynamics in a patient-specific manner. Our results shed light on important factors at play in cancer cells survival, highlighting a potentially important role of phagocytosis.

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