Written By: Katie Zelvin (guest writer)
”Today, even school children are taught that the machine is a tool for creating new worlds, not simply something for probing and/or modifying the existing one.”
While reading John L. Casti’s Would-Be Worlds, this line stood out. It spoke to me directly, challenging an idea that I previously believed was common knowledge.
I live in a world full of computer fanatics. Both my mom and my dad were computer science majors, and my aunt is an accountant. I go to an engineering-focused high school, where half of my grade majors in electrical and computer engineering, and the other half uses computer software for CAD modeling. The people around me process and create things using machines. In my eyes, and I assume many of yours, creation is the primary purpose of a machine.
However, computers do more than simply process data to provide results. They can be used to recreate real-world problems in a controlled environment to help us make decisions about future events. They are able to render simulations.
The first ideas that come to mind when discussing simulation are video games. These are advanced computer programs that allow users to make decisions following a set of rules that affect what will happen next. This same concept is used in scientific simulations to predict the future.
Let’s take a look at a simple example. Biomorphs are an early simulation idea described by Richard Dawkins in his book, The Blind Watchmaker. These computer-generated structures, inspired by real animals, are surrounded by slightly mutated shapes. The user has the option of choosing one of the surrounding structures to change it, and the process is repeated as they continue to choose the mutated shapes. In other words, the person is controlling how the structure evolves, including its width and length, shape, and details. The concept revolves around the idea of the evolution of organisms. Here is an example of what I mean:
In this simulation by Emergent Mind, the central structure is the original “animal”, and the surrounding structures are the slightly mutated animals. Being able to analyze these computer-generated models is useful if humans know which natural factors result in each of the specific mutations in the real world. It allows scientists to understand what different organisms evolved from and what they may evolve into in the future.
In comparison to its more modern counterparts, this biomorph simulation is fairly simple. It was described in a book published in 1997 when technology was not nearly as advanced as it is today. Simulation has evolved over time to tackle a variety of problems that more directly impact our everyday lives.
For example, simulations are currently being used to predict the spread of COVID-19. Only using available data to understand past trends is not enough—it’s important to use simulations in order to predict the spread of the disease.
The Washington Post is using mathematical data to simulate how the virus would spread over time. They modeled the small town of Whittier, Alaska using a set of rules to figure out how quickly people recovered and how many people were healthy and sick. Here is a snapshot of the simulation:
A forced quarantine was implemented in the simulation to show the impact of isolation. Here is what they came up with:
What about social distancing? This is more difficult to show, because the simulation shows most of the dots staying still, while some are in motion. When a dot that has the disease comes into contact with one that does not, they transmit it, and the spread grows exponentially. The model began to look like this:
And finally, a model that shows the comparison of my results after playing each simulation is rendered at the end.
It is important to remember that the results generated from these simulations are random. After the simulation is completed, I can replay it, but I will have slightly different results. With repeated testing, however, it becomes clear that the basic trends will emerge, considering the conditions are the same, and the common results can be interpreted as probable future outcomes.
If we simply made calculations from data, it would result in a single number or a range prediction, which, depending on the complexity of the situation, can often be inaccurate. Simulation allows us to take conditions, rather than results, and see how things play out in those conditions.
Katie Zelvin is an incoming high school junior and writes about engineering on her blog: https://engineeringthefutureblog.weebly.com/.