Anytime I see a male urinal installed in a train station, I run a simulation in my head.

Women may not know this, but there are usually three male urinals installed in stations.

And every time I look at these three urinals, I think.

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- Which urinal is the best strategy to use when all three of these are available?

- Which urinal is the best strategy to use, left or right, when the middle is in use?

- Should you avoid urinals while the water is flowing?

Then, taking into account the average time of use of urinals, the dispersion of urinals by age, and the congestion rate at the time of use

Which of these is the best reasoning engine to build into these, urinal-using male agent programs ?

- Rule-based reasoning to describe behavior verbally

- Alternatively, reinforcement learning to derive behavior from observations

- Or, virtual agents to model behavioral economics

It is fun for me to think about those things.

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As you can imagine, we are not allowed to set up a camera in the toilet. However, if we had data on the time of the human sensors installed at the urinal, I think I could hypothesize and test it there.

So here's a request to all toilet bowl manufacturers.

Would you mind releasing that time-series human sensor response time data to me?

Huh? You're not measuring that kind of data?

No, no, I'm sure you are measuring it.

The use rates of male urinals are important data for your company.

This is because you need to apply queuing theory to calculate the urinals needed at each station.

"There's no reason to disclose it to Ebata because it's important data"

Oh, that's certainly reasonable.

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Then I guess I'll just have to keep observing, independently, in the restroom.

But for sure, I'm sure they'll call the police.

Even I think, "If I find a suspicious person like that, I'm definitely going to call the police".