Completion of IM-15119 (which added patches to IM-14058). Unconscious affective dynamics Josh Epstein's Agent Zero Book webpage  Part II p.89 with 2 agent types, spatial patches and location aware, mobile occupying (blue) agents

Hybrid conceptual mapping of relationships involving system causal loop diagram linked with ABM. Output of the problem conceptualization phase of the modelling process prior to developing a computational hybrid model in AnyLogic. Includes Nate Osgood's O PARTIES extension of Ross Hammond's PARTE. See also earlier Canadian version Insight

WIP Ideas for a hybrid budding SD plus ABM depression dynamics model

WIP Combining SD and ABM Representations

Attempt to learn ABM models

Modelo Baseado em Agente para a dispersão espacial de doenças, considerando o modelo SIR com perda da imunidade ao vírus, conforme [Bellinger G.]

The story board runs through the premise of the project with the approach I took

Physician agents interacting with delegate agents for emergency department assessment diagnosis and treatment. From BMC paper May 2013, combining figs 1 and 2

A new archetype, The Tyranny of Small Steps (TYST) has been observed. Explained through a system dynamics perspective, the archetypical behaviour TYST is an unwanted change to a system through a series of small activities that may be independent from one another. These activities are small enough not to be detected by the ‘surveillance’ within the system, but significant enough to encroach upon the “tolerance” zone of the system and compromise the integrity of the system. TYST is an unintentional process that is experienced within the system and made possible by the lack of transparency between an overarching level and a local level where the encroachment is taking place.

Reference:

Haraldsson, H. V., Sverdrup, H. U., Belyazid, S., Holmqvist, J. and Gramstad, R. C. J. (2008), The Tyranny of Small Steps: a reoccurring behaviour in management. Syst. Res., 25: 25–43. doi: 10.1002/sres.859 

A simple agent based foraging model. Consumer agents will move between fertile patches consuming them.

This insight is an element of the Agent Based Modeling learning module in Systems KeLE.

Modélisation spatiale et multi-agents d'une épidémie. Avec trois classes d'individus: susceptibles (sains), infectés (malades et contagieux), et remis (sains et temporairement immunisés).

Traduit de 

https://insightmaker.com/insight/2846/Agent-Based-Disease-Simulation  

.

From IM-3533 Grimm's ODD and Nate Osgood's ABM Modeling Process and Courses based on Volker Grimm and Steven F. Railsback's 2012 paper and Muller et al 2013 paper Describing Human Decisions in Agent-based Models – ODD + D, An Extension of the ODD Protocol', Environmental Modelling and Software, 48: 37-48.

Modélisation spatiale et multi-agents d'une épidémie. Avec trois classes d'individus: susceptibles (sains), infectés (malades et contagieux), et remis (sains et temporairement immunisés).

Traduit et adapté de 

https://insightmaker.com/insight/2846/Agent-Based-Disease-Simulation  

A spatially aware, agent based model of disease spread. There are three classes of people: susceptible (healthy), infected (sick and infectious), and recovered (health and temporarily immune).

This model is cloned thru an Agent-Based Modeling Simulation of "Covid-19 (ABM)_VHK" Model by Venkata Habiram Koppaka last April 2020 for presenting the Pandemic COVID-19 Disease. This ABM Simulation aims to represent the trend of COVID-19 infection and death rate (dynamics) at Puerto Princesa City, PALAWAN using the June 3, 2021 data of the CESU-PPC.

A random walk demonstration using an ABM. As individuals drink more they become more intoxicated and their walk becomes more random. And when they drink to much it finally kills them.

ABM Model Test

WIP ideas about auto-configuring cross-cutting concerns and resolving where to place role relationship properties and methods

The system in which it was possible to vary the number and size of street trees in a city, and to see the concomitant increases and decreases in the amount of pollution in that city.