Simulated and agent-based modeling: a world to explore
Julián Alberto Uribe Gómez
When you hear about the word "agent" what do you think? What does it evoke? Sometimes with the renowned Oscar-winning 4-movie “The Matrix” and his trilogy, very famous for his antagonist agent “Smith”. It was there that they began to talk about this concept in a fairly common way, however, quite apart from the fictional concept that is handled there is an academic concept that has been exploited for decades by researchers and scientists.
It can be said that the concept of “agent” was born in the 60s, under the influence of the LOGO programming language. This language and its platform had as its main objective the education and teaching of programming (Pea, 2007), apparently in a very didactic way for the time. At that time "agents" were not programmed but "turtles" were programmed, and with simple commands or controls the "turtle" executed a series of orders and movements.
This resulted in much more powerful applications, one of them is currently the NETLOGO platform. In this platform the concept of initial “turtle” is still being implemented, however, being an academically accepted tool for programming events of individual entities, the “turtle” changes to the name of “agent”.
NETLOGO as an educational and procedural platform, was created with the same educational principle as the LOGO, where children and adults can learn equally and no previous programming bases are required, in addition the program is an open-source platform and is available on the link https://ccl.northwestern.edu/netlogo/.
Platforms such as NETLOGO over the years have incorporated various tools that enhance their usefulness in different areas of knowledge, platforms like this are known as multiparadigma tools, because they allow to explore phenomena that include biology, physics, chemistry, psychology, networks, computer science, economics and others, under aspects such as educational, procedural and simulation, and enter what is currently known as studies of emerging phenomena or behaviors.
Under this perspective, studies of complex phenomena, modeling and simulation of them have begun to gain great importance. Where the latter presents an option to improve decision making and reduce response times to situations of conflict and uncertainty (Viveros & Chew, 2013), likewise, it has been shown that traditional and analytical mathematics finds difficulties in establishing relationships and Solutions in the short term. Some examples where simulation and “agents” have been used are models of innovation systems, diffusion and adoption of technology, social networks, epidemics and viruses, behaviors of insect colonies and land traffic, among others.
With this in mind, then what is an "agent"? An "agent" is a heterogeneous object or entity, with a set of states or rules, that exhibits pre-programmed behavior to perform specific tasks in a given environment. However, an agent is programmed to be autonomous, reliable and learn (Foner, n.d.) when in interaction with other agents in the system. As examples of agents we have: ants, people, cars, companies, computers, birds.
A characteristic example to represent agents and study emergent behavior can be represented in the following situation of medical application: Consider a tissue that is being affected by a virus. It reproduces very quickly and spreads through the tissue without allowing recovery. In this case the agents represented in the system are: the tissue and the number of viruses found, with the following behaviors:
Figure 1 represents the initial phase of the preparation of the simulation of the tissue-virus system, the points are shown as the viral agents which are on the representation of the tissue.
To start the simulation there are two important moments: the first one is the preparation of the simulation environment, the second moment involves entering the simulation phase, in which the orders to the tissue and virus agents are executed, resulting in the following dynamics represented in figures 2 and 3.
In Figure 4, after 500 simulation runs, the behavior of the system can be seen. It is appreciated that at the beginning of the graph there is a bacterial and inner tissue growth due to the consumption of the outer tissue. This favors the reproduction of the virus, while the outer tissue decreases. The system reaches a moment of equilibrium between the agents.
If this system in equilibrium is injected with the effect of an enzyme that functions as an antibody, one can study how the system changes and its behavior. Therefore, another agent called enzyme will be defined with the rule: vaccinate-tissue, so we get the result represented in Figure 5.
With the injection of the enzyme into the system the viral agent is attacked by decreasing it and recovering the affected tissue.
The simulation presents benefits to all areas of science, since it allows developing experiments with minimal risks and anticipating positive or negative behaviors, all this combined with the paradigm of agent-based models allows to explore and understand global phenomena arising from individual behaviors.
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