In 1999 the movie The Matrix introduced (2) millions of people to the philosophical question “how do we know if we live in the real world or a simulation”. As an engineer, working for a company that makes the “Matrix Laboratory” I have been thought about this idea and it’s logical extension; can we trick a machine into thinking it is in the real world?

Is this the real life? (3)

Simulations of reality have existed for a long time, from wind tunes, to lumped mass model to complex finite element models; they have been the backbone of engineering design. As powerful as these models have been they have been limited as simulations of physical properties, or perhaps a system of physical properties. The next generation of simulations attempts to simulate the complex nature of the real world, that is to say people and their semi-predictable behavior.

Humans as lumpy-mass models

Ok, first I’m going to need to write a lot of text here to make it all the way down this comic strip…

The world is filled with humans, roughly 7.6 billion as of the writing of this post. We are out there driving cars, walking in the street, making phone calls, cooking dinner, talking about philosophy and sitcoms. We do a lot of different things. When moving in a mass we are largely predictable; that is to say if you asked me to calculate how long it would take for 100,000 people to exit University of Michigan’s football stadium, I could give you an reasonable accurate answer for the total. If, however you asked me how long for a given person, well then it becomes more difficult. It is the aggregate behavior that is predictable. The issue is that for controllers that interact with the real world the aggregate is not enough.

So how do you go about simulating a pod of people? There are several basic methods.

• Conway’s game of life: “humans” can be simulated by giving them a set of basic “rules”. Those rules (with weighted objective functions) determine how they operate. Note: your rules here can’t be too perfect, real humans make mistakes. (Note this is often done using cellular automation approach)
• Genetic algorithms: The humans can be derived using genetic algorithms(5). In this case a set of baseline behaviors are defined as well as “mutations” or permutations on those behaviors.
• Fluid dynamic analogies: Fluid dynamic models do a good job with modeling flow “restrictions” around doors and changing widths of the system.
• Real world data (human in the loop): The most difficult to set up, but done well, mining the real-world for data on how people act, and react, provides the most accurate models. The previous three suggestions can be considered reduced form version of the “RWD-HIL”

Why do we want a matrix for our machine?

It is oft repeated question for self-driving cars, what happens if a child darts out in front of it? Because it is repeated so often it is now tested for heavily. But what about all the other things kids (or adults) do that are foolish? Ever drop something in the street and stop to pick it up? Every order a 100 tee-shirts instead of 1? Pull up on the throttle when you should have pushed down? In the end people are semi-random creatures. Creating realistic models of people allows us to create better control algorithms.

Footnotes

1. The image of the “red pill” / “blue pill” should not be taken as an endorsement of A/B testing as the only validation methodology
2. Note, it introduced people to the question, it does not mean that many people put much thought into it beyond “dude, how can you know?”
3. Note, if we do it correctly we never have to sing “mamma, my controller just killed a man”
4. A pod of people is of course a reference to “pod-people“; e.g. close enough to fool some people
5. Real people are created from generic algorithms so this should wok right?

If you find benefit from this blog, consider subscribing