scholarly journals Design of an Agent-Based Learning Environment for High-Risk Doorstep Scam Victims

2018 ◽  
pp. 335-347 ◽  
Author(s):  
Laura M. van der Lubbe ◽  
Tibor Bosse ◽  
Charlotte Gerritsen
Keyword(s):  
High Risk ◽  
Learning Environment ◽  
Agent Based

scholarly journals Is the Green Wave Really Green? The Risks of Rebound Effects When Implementing “Green” Policies

2021 ◽  
Vol 13 (10) ◽  
pp. 5411
Author(s):  
Elisabeth Bloder ◽  
Georg Jäger
Keyword(s):  
Climate Change ◽  
High Risk ◽  
Urban Areas ◽  
The Other ◽  
Agent Based ◽  
Traffic Lights ◽  
Traffic System ◽  
Rebound Effects ◽  
Car Use ◽  
Reduce Emissions

Traffic and transportation are main contributors to the global CO2 emissions and resulting climate change. Especially in urban areas, traffic flow is not optimal and thus offers possibilities to reduce emissions. The concept of a Green Wave, i.e., the coordinated switching of traffic lights in order to favor a single direction and reduce congestion, is often discussed as a simple mechanism to avoid breaking and accelerating, thereby reducing fuel consumption. On the other hand, making car use more attractive might also increase emissions. In this study, we use an agent-based model to investigate the benefit of a Green Wave in order to find out whether it can outweigh the effects of increased car use. We find that although the Green Wave has the potential to reduce emissions, there is also a high risk of heaving a net increase in emissions, depending on the specifics of the traffic system.

scholarly journals Evolution of social learning when high expected payoffs are associated with high risk of failure

2011 ◽  
Vol 8 (64) ◽  
pp. 1604-1615 ◽  
Author(s):  
Michal Arbilly ◽  
Uzi Motro ◽  
Marcus W. Feldman ◽  
Arnon Lotem
Keyword(s):  
High Risk ◽  
Social Learning ◽  
Risk Aversion ◽  
Individual Risk ◽  
Individual Experience ◽  
Agent Based ◽  
Risk Of Failure ◽  
Expected Payoffs ◽  
Maladaptive Behaviours ◽  
Optimal Behaviour

In an environment where the availability of resources sought by a forager varies greatly, individual foraging is likely to be associated with a high risk of failure. Foragers that learn where the best sources of food are located are likely to develop risk aversion, causing them to avoid the patches that are in fact the best; the result is sub-optimal behaviour. Yet, foragers living in a group may not only learn by themselves, but also by observing others. Using evolutionary agent-based computer simulations of a social foraging game, we show that in an environment where the most productive resources occur with the lowest probability, socially acquired information is strongly favoured over individual experience. While social learning is usually regarded as beneficial because it filters out maladaptive behaviours, the advantage of social learning in a risky environment stems from the fact that it allows risk aversion to be circumvented and the best food source to be revisited despite repeated failures. Our results demonstrate that the consequences of individual risk aversion may be better understood within a social context and suggest one possible explanation for the strong preference for social information over individual experience often observed in both humans and animals.

Simulation of HIV Infection Propagation Networks

2013 ◽  
Vol 5 (1) ◽  
pp. 53-63 ◽  
Author(s):  
Alfredo Tirado-Ramos ◽  
Chris Kelley
Keyword(s):  
High Risk ◽  
Drug Users ◽  
Hiv Transmission ◽  
State Of The Art ◽  
Complex Nature ◽  
Model Framework ◽  
Agent Based ◽  
Current State ◽  
Future Work ◽  
Risk Infection

Simulating the transmission of HIV requires a model framework that can account for the complex nature of HIV transmission. In this paper the authors present the current state of the art for simulating HIV with agent-based models and highlight some of the significant contributions of current research. The authors then propose opportunities for future work including their plan that involves identifying and monitoring high-risk drug users that can potentially initiate high-risk infection propagation networks.

scholarly journals An agent based architecture for high-risk neonate management at neonatal intensive care unit

10.19082/6193 ◽  
2018 ◽  
Vol 10 (1) ◽  
pp. 6193-6200
Author(s):  
Jaleh Shoshtarian Malak ◽  
Reza Safdari ◽  
Hojjat Zeraati ◽  
Fatemeh Sadat Nayeri ◽  
Niloofar Mohammadzadeh ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
High Risk ◽  
Neonatal Intensive Care Unit ◽  
Neonatal Intensive Care ◽  
Agent Based ◽  
High Risk Neonate

scholarly journals Modelling aerosol-based exposure to SARS-CoV-2 by an agent based Monte Carlo method: Risk estimates in a shop and bar

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0260237
Author(s):  
Henri Salmenjoki ◽  
Marko Korhonen ◽  
Antti Puisto ◽  
Ville Vuorinen ◽  
Mikko J. Alava
Keyword(s):  
High Risk ◽  
Quantitative Difference ◽  
Equilibrium Concentration ◽  
Infection Risk ◽  
Space Time ◽  
Aerosol Concentration ◽  
Model Parameters ◽  
Single Individual ◽  
Agent Based ◽  
Aerosol Inhalation

Present day risk assessment on the spreading of airborne viruses is often based on the classical Wells-Riley model assuming immediate mixing of the aerosol into the studied environment. Here, we improve on this approach and the underlying assumptions by modeling the space-time dependency of the aerosol concentration via a transport equation with a dynamic source term introduced by the infected individual(s). In the present agent-based methodology, we study the viral aerosol inhalation exposure risk in two scenarios including a low/high risk scenario of a “supermarket”/“bar”. The model takes into account typical behavioral patterns for determining the rules of motion for the agents. We solve a diffusion model for aerosol concentration in the prescribed environments in order to account for local exposure to aerosol inhalation. We assess the infection risk using the Wells-Riley model formula using a space-time dependent aerosol concentration. The results are compared against the classical Wells-Riley model. The results indicate features that explain individual cases of high risk with repeated sampling of a heterogeneous environment occupied by non-equilibrium concentration clouds. An example is the relative frequency of cases that might be called superspreading events depending on the model parameters. A simple interpretation is that averages of infection risk are often misleading. They also point out and explain the qualitative and quantitative difference between the two cases—shopping is typically safer for a single individual person.

Sign in / Sign up

Export Citation Format

Share Document