scholarly journals An agent-based computational framework for simulation of global pandemic and social response on planet X

2020 ◽  
Vol 66 (5) ◽  
pp. 1195-1209 ◽  
Author(s):  
T. I. Zohdi
Keyword(s):  
Social Response ◽  
Computational Framework ◽  
Agent Based ◽  
Global Pandemic ◽  
Planet X

scholarly journals A Computational Framework for Exploring the Socio-Cognitive Features of Teams and their Influence on Design Outcomes

2019 ◽  
Vol 1 (1) ◽  
pp. 1-10
Author(s):  
Harshika Singh ◽  
Gaetano Cascini ◽  
Hernan Casakin ◽  
Vishal Singh
Keyword(s):  
Size Structure ◽  
Critical Role ◽  
Computational Framework ◽  
New Members ◽  
Learning Abilities ◽  
Team Members ◽  
Agent Based ◽  
Proposed Model ◽  
Dependent Parameter ◽  
The Impact

AbstractThe dynamics of design teams play a critical role in product development, mainly in the early phases of the process. This paper presents a conceptual framework of a computational model about how cognitive and social features of a design team affect the quality of the produced design outcomes. The framework is based on various cognitive and social theories grounded in literature. Agent-Based Modelling (ABM) is used as a tool to evaluate the impact of design process organization and team dynamics on the design outcome. The model describes key research parameters, including dependent, independent, and intermediates. The independent parameters include: duration of a session, number of times a session is repeated, design task and team characteristics such as size, structure, old and new members. Intermediates include: features of team members (experience, learning abilities, and importance in the team) and social influence. The dependent parameter is the task outcome, represented by creativity and accuracy. The paper aims at laying the computational foundations for validating the proposed model in the future.

The Order of Human Interdependencies: Simulating Elias’ One-level Multi-person Models

2017 ◽  
Vol 22 (4) ◽  
pp. 105-131 ◽  
Author(s):  
Kenneth Hemmerechts ◽  
Nohemi Jocabeth Echeverria Vicente ◽  
Dimokritos Kavadias
Keyword(s):  
Social Life ◽  
Theoretical Models ◽  
Social Processes ◽  
Computational Framework ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Norbert Elias ◽  
Or Groups ◽  
Emergent Dynamics

Sociologist Norbert Elias made it his lifework to describe and explain long-term processes. According to Elias, these processes cannot be studied voluntaristically by only focusing on human intentions or motivations. This is because they are the unplanned result of a whole spectrum of interactions of different people over time. According to Elias, these interactions between individuals interweave to produce a development that is relatively autonomous from the actions of individuals. To illustrate how the actions of individuals interweave and produce emergent dynamics, Elias constructed several theoretical models that are simplified versions of social processes. Importantly, the different models state precise propositions and consequences of specific types of interweaving that can be formally tested. This article simulates the Eliasian approach to social life. We reproduce the theoretical models of Elias with a method that is highly suited to investigate their emergent dynamics: agent-based modelling. Agent-based models are computer models that simulate agents (i.e. individuals or groups of individuals) and their interaction with other agents. More specifically, we test whether the theorized consequences of the Eliasian models exist when we implement their propositions in a computational framework.

scholarly journals The impact of natural disasters on the spread of COVID-19: a geospatial, agent-based epidemiology model

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Maximillian Van Wyk de Vries ◽  
Lekaashree Rambabu
Keyword(s):  
Infectious Diseases ◽  
Natural Disasters ◽  
Human Health ◽  
Natural Disaster ◽  
Potential Effect ◽  
Agent Based ◽  
Median Increase ◽  
Global Pandemic ◽  
Disaster Scenario ◽  
The Impact

Abstract Background Natural disasters and infectious diseases result in widespread disruption to human health and livelihood. At the scale of a global pandemic, the co-occurrence of natural disasters is inevitable. However, the impact of natural disasters on the spread of COVID-19 has not been extensively evaluated through epidemiological modelling. Methods We create an agent-based epidemiology model based on COVID-19 clinical, epidemiological, and geographic data. We first model 35 scenarios with varying natural disaster timing and duration for a COVID-19 outbreak in a theoretical region. We then evaluate the potential effect of an eruption of Vesuvius volcano on the spread of COVID-19 in Campania, Italy. Results In a majority of cases, the occurrence of a natural disaster increases the number of disease related fatalities. For a natural disaster fifty days after infection onset, the median increase in fatalities is 2, 59, and 180% for a 2, 14, and 31-day long natural disaster respectively, when compared to the no natural disaster scenario. For the Campania case, the median increase in fatalities is 1.1 and 2.4 additional fatalities per 100,000 for eruptions on day 1 and 100 respectively, and 60.0 additional fatalities per 100,000 for an eruption close to the peak in infections (day 50). Conclusion Our results show that the occurrence of a natural disaster in most cases leads to an increase in infection related fatalities, with wide variance in possible outcomes depending on the timing of the natural disaster relative to the peak in infections and the duration of the natural disaster.

scholarly journals Identifying Synergistic Interventions to Address COVID-19 Using a Large Scale Agent-Based Model

2020 ◽  
Author(s):  
Junjiang Li ◽  
Philippe J. Giabbanelli
Keyword(s):  
Public Health ◽  
Large Scale ◽  
Contact Tracing ◽  
Remote Work ◽  
Agent Based Model ◽  
Agent Based ◽  
Global Pandemic ◽  
Small Set ◽  
Factorial Design Of Experiments ◽  
The Individual

AbstractThere is a range of public health tools and interventions to address the global pandemic of COVID-19. Although it is essential for public health efforts to comprehensively identify which interventions have the largest impact on preventing new cases, most of the modeling studies that support such decision-making efforts have only considered a very small set of interventions. In addition, previous studies predominantly considered interventions as independent or examined a single scenario in which every possible intervention was applied. Reality has been more nuanced, as a subset of all possible interventions may be in effect for a given time period, in a given place. In this paper, we use cloud-based simulations and a previously published Agent-Based Model of COVID-19 (Covasim) to measure the individual and interacting contribution of interventions on reducing new infections in the US over 6 months. Simulated interventions include face masks, working remotely, stay-at-home orders, testing, contact tracing, and quarantining. Through a factorial design of experiments, we find that mask wearing together with transitioning to remote work/schooling has the largest impact. Having sufficient capacity to immediately and effectively perform contact tracing has a smaller contribution, primarily via interacting effects.

scholarly journals EQUATION-FREE MULTISCALE COMPUTATIONS IN SOCIAL NETWORKS: FROM AGENT-BASED MODELING TO COARSE-GRAINED STABILITY AND BIFURCATION ANALYSIS

2010 ◽  
Vol 20 (11) ◽  
pp. 3673-3688 ◽  
Author(s):  
A. C. TSOUMANIS ◽  
C. I. SIETTOS ◽  
G. V. BAFAS ◽  
I. G. KEVREKIDIS
Keyword(s):  
Social Networks ◽  
Public Information ◽  
Stationary Solutions ◽  
Agent Based Modeling ◽  
Coarse Grained ◽  
Computational Framework ◽  
Agent Based ◽  
Private And Public ◽  
Multiscale Computations ◽  
Dynamical Information

We focus on the "trijunction" between multiscale computations, bifurcation theory and social networks. In particular, we address how the Equation-Free approach, a recently developed computational framework, can be exploited to systematically extract coarse-grained, emergent dynamical information by bridging detailed, agent-based models of social interactions on networks, with macroscopic, systems-level, continuum numerical analysis tools. For our illustrations, we use a simple dynamic agent-based model describing the propagation of information between individuals interacting under mimesis in a social network with private and public information. We describe the rules governing the evolution of the agents' emotional state dynamics and discover, through simulation, multiple stable stationary states as a function of the network topology. Using the Equation-Free approach we track the dependence of these stationary solutions on network parameters and quantify their stability in the form of coarse-grained bifurcation diagrams.

Ethnic Minority Rule and Civil War Onset How Identity Salience, Fiscal Policy, and Natural Resource Profiles Moderate Outcomes

2011 ◽  
Vol 28 (5) ◽  
pp. 438-458 ◽  
Author(s):  
Dan Miodownik ◽  
Ravi Bhavnani
Keyword(s):  
Civil War ◽  
Fiscal Policy ◽  
Ethnic Minority ◽  
Data Availability ◽  
Computational Framework ◽  
Agent Based ◽  
Moderating Variables ◽  
Lootable Resources ◽  
The Impact ◽  
Civil War Onset

Using an agent-based computational framework designed to explore the incidence of conflict between two nominally rival ethnic groups, we demonstrate that the impact of ethnic minority rule on civil war onset could be more nuanced than posited in the literature. By testing the effects of three key moderating variables on ethnic minority rule, our analysis demonstrates that: (i) when ethnicity is assumed to be salient for all individuals, conflict onset increases with size of the minority in power, although when salience is permitted to vary, onset decreases as minority and majority approach parity; (ii) fiscal policy—the spending and investment decisions of the minority EGIP—moderates conflict; conflict decreases when leaders make sound decisions, increases under corrupt regimes, and peaks under ethno-nationalist regimes that place a premium on territorial conquest; and lastly (iii) natural resources—their type and distribution—affect the level of conflict which is lowest in agrarian economies, higher in the presence of lootable resources, and still higher when lootable resource are “diffuse”. Our analysis generates a set of propositions to be tested empirically, subject to data availability.

scholarly journals Agent-Based Learning Model for the Obesity Paradox in RCC

2021 ◽  
Vol 9 ◽  
Author(s):  
Matteo Belenchia ◽  
Giacomo Rocchetti ◽  
Stefano Maestri ◽  
Alessia Cimadamore ◽  
Rodolfo Montironi ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Environmental Changes ◽  
Interaction Network ◽  
Obesity Paradox ◽  
Cell Interaction ◽  
Computational Framework ◽  
Agent Based ◽  
Medical Needs ◽  
Starting Model ◽  
Cell Cell

A recent study on the immunotherapy treatment of renal cell carcinoma reveals better outcomes in obese patients compared to lean subjects. This enigmatic contradiction has been explained, in the context of the debated obesity paradox, as the effect produced by the cell-cell interaction network on the tumor microenvironment during the immune response. To better understand this hypothesis, we provide a computational framework for the in silico study of the tumor behavior. The starting model of the tumor, based on the cell-cell interaction network, has been described as a multiagent system, whose simulation generates the hypothesized effects on the tumor microenvironment. The medical needs in the immunotherapy design meet the capabilities of a multiagent simulator to reproduce the dynamics of the cell-cell interaction network, meaning a reaction to environmental changes introduced through the experimental data.

scholarly journals Investigating the growth of an engineered strain of Cyanobacteria with an Agent-Based Model and a Recurrent Neural Network

2021 ◽  
Author(s):  
Jonathan Sakkos ◽  
Joe Weaver ◽  
Connor Robertson ◽  
Bowen Li ◽  
Denis Taniguchi ◽  
...  
Keyword(s):  
Neural Network ◽  
Microbial Communities ◽  
Recurrent Neural Network ◽  
Computational Cost ◽  
Solution Space ◽  
Synechococcus Elongatus ◽  
Computational Framework ◽  
Agent Based ◽  
Synthetic Images ◽  
Pcc 7942

AbstractA computational framework combining Agent-Based Models (ABMs) and Deep Learning techniques was developed to help design microbial communities that convert light and CO2 into useful bioproducts. An ABM that accounts for CO2, light, sucrose export rate and cell-to-cell mechanical interactions was used to investigate the growth of an engineered sucrose-exporting strain of Synechococcus elongatus PCC 7942. The ABM simulations produced population curves and synthetic images of colony growth. The curves and the images were analyzed, and growth was correlated to nutrients availability and colonies’ initial spatial distribution. To speed up the ABM simulations, a metamodel based on a Recurrent Neural Network, RNN, was trained on the synthetic images of growth. This metamodel successfully reproduced the population curves and the images of growth at a lower computational cost. The computational framework presented here paves the road towards designing microbial communities containing sucrose-exporting Synechococcus elongatus PCC 7942 by exploring the solution space in silico first.

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