The Thanksgiving holiday we celebrate here in the USA has some unfortunate overtones regarding its colonial origin. Still, the idea of a festival of thanks is an ancient one — thanks for a good harvest or a good hunt. Or, in our case, thanks for helping us not die last winter.
As with Christmas or the Copenhagen interpretation, we tend to take a “shut up and calculate!” approach to the holidays. “Shut up and shop!” in the case of the Winter Solstice, and “Shut up and give thanks!” today.
One thing we can be very thankful about is patterns…
For the last two weeks I’ve written a number of posts contrasting physical systems with numeric systems.
(The latter are, of course, also physical, but see many previous posts for details on significant differences. Essentially, the latter involve largely arbitrary maps between real world magnitude values and internal numeric representations of those values.)
I’ve focused on the nature of causality in those two kinds of systems, but part of the program is about clearly distinguishing the two in response to views that conflate them.
I’ve written before about Drake’s Equation and the Fermi Paradox. The former suggests the possibility of lots of alien life. The latter asks okay, so where the heck are they? Given that the universe just started, it’s possible we’re simply the first. Maybe the crowd comes later. (Maybe we create the crowd!)
Recently, one of my favorite YouTube channels, PBS Space Time, began a series of videos about this. The first one (see below) talks about the Rare Earth Hypothesis, a topic that has long fascinated me.
The synchronicity in this is that I’d just had a thought about basic probability and how it applies to our being here…
There is something about the articles that Ethan Siegel writes for Forbes that don’t grab me. It might be that I’m not in the target demographic — he often writes about stuff I explored long ago. I keep an eye on him, though, because sometimes he comes up with a taste treat for me.
Such as his article today, No, Thermodynamics Does Not Explain Our Perceived Arrow Of Time. I jumped on it because the title declares something I think many have backwards: the idea that time arises from entropy or change. Quite to the contrary, I think entropy and change are consequences of time (plus physics).
Siegel makes an interesting argument I hadn’t considered before.
Last time I left off with a virtual ball moving towards a virtual wall after touching on the basics of how we determine if and when the mathematical ball virtually hits the mathematical wall. It amounts to detecting when one geometric shape overlaps another geometric shape.
In the physical world, objects simply can’t overlap due to physics — electromagnetic forces prevent it. An object’s solidity is “baked in” to its basic nature. In contrast, in the virtual world, the very idea of overlap has no meaning… unless we define one.
This time I want to drill down on exactly how we do that.
Last time we saw that, while we can describe a maze abstractly in terms of its network of paths, we can implement a more causal (that is: physical) approach by simulating its walls. In particular, this allows us to preserve its basic physical shape, which can be of value in game or art contexts.
This time I want to talk more about virtual walls as causal objects in a maze (or any) simulation. Walls are a basic physical object (as well as a basic metaphysical concept), so naturally they are equally foundational in the abstract and virtual worlds.
And ironically, “Something there is that doesn’t love a wall.”
First I discussed five physical causal systems. Next I considered numeric representations of those systems. Then I began to explore the idea of virtual causality, and now I’ll continue that in the context of virtual mazes (such as we might find in a computer game).
I think mazes make a simple enough example that I should be able to get very specific about how a virtual system implements causality.
With mazes, it’s about walls and paths, but mostly about paths.
This is the third of a series of posts about causal systems. In the first post I introduced five physical systems (personal communication, sound recording, light circuit, car engine, digital computer). In the second post I considered numerical representations of those systems — that is, implementing them as computer programs.
Now I’d like to explore further how we represent causality in numeric systems. I’ll return to the five numeric systems and end with a much simpler system I’ll examine in detail next time.
Simply put: How is physical causality implemented in virtual systems?
Put this under someone’s tree…
“You take one out and drink it down,… 98 cans of beer in the case…”
Last time I explored five physical systems. This time I want to implement those five systems as information systems, by which I mean numeric versions of those five systems. The requirement is that everything has to be done with numbers and simple manipulations of numbers.
Of course, to be useful, some parts of the system need to interact with the physical world, so, in terms of their primary information, these systems convert physical inputs into numbers and convert numbers into physical outputs.
Our goal is for the numeric systems to fully replace the physical systems.