Logical model of the physical world

In summary, the conversation discusses the possibility of creating a machine-readable definition of common rules and actions in the physical world, similar to how semantic technologies and ontologies define classes and properties. The idea is to have a logical model that can simulate the behavior of physical objects based on their properties and actions. However, the scope and usefulness of such a model are questioned, as it may be too large and complex to be practical. The conversation also refers to the work of Prof Rucker in using symbols to describe reality and the potential applications of such a concept in topological and algebraic constructs. Ultimately, while there are existing knowledge base projects attempting to model actions, their randomness may hinder their usability for simulations.
  • #1
miksu
2
0
Hi,
are there any attempts to make a machine readable definition of most common rules and actions (or a causal model) of physical world? I'm looking more a logical level of modelling, at the same level that human common sense works.

I have some experience on semantic tecnologies and ontologies, and they usually define classes and individuals with properties, for example machine readable version of Wikipedia. What I'm looking for, is a set of descriptions of what physical properties these instances can have and how to manipulate them.

Let's make an artificial example of cake. So we have a class https://en.wikipedia.org/wiki/Cake. We would then add some physical properties to is, such as size and weight (range of) and a the fact that it permanently deforms on impact (dropping it will mash it, unlike dropping a ball).
Then we can have other data, independent of the cake, such as defined action "drop object" which causes an object to change location in free fall until it hits some other object and deforms, or bounces, depending of the defined properties. With this we can then run a simulation what happens to an instance of cake if it is dropped.

So with this model it would be possible to infer what happens to objects on physical events on some logical, common sense level.
 
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  • #2
It isn't clear to me what the scale/scope and use would be for what you are asking. But...

Simulations tend to be highly specific and limited in scope due to the computing power needed to run them. So they tend to be tailor-made or copied and modified from previous simulations.
 
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  • #3
I understand the appeal. I was once teased by the chance to make an object model of the whole electric power industry. Technical, finance, admin, social, political, including every job description. It was tremendously appealing to an analyst But it was shot down because:
  1. There is no way to limit the scope. It could be a forever project. And the bigger the model, the more cumbersome it becomes.
  2. In your example, the method "drop" might apply to many objects, but other programmers might choose "shock loading", or "shear" as their preference. Wrong choice of method could poison the whole project. We have no standards to clearly separate right from wrong.
  3. There is no objective evidence that such a model would be useful compared to existing software models and even common sense.
I believe those are the biggest reasons that most simulations tend to be highly specific as @russ_watters said.
 
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  • #4
Your "cake" model reminds me of a series of lectures and books by my combinatorics / computer science teacher Rudolf von Bitter Rucker. The wikipedia entry is incomplete and since this is a science thread we should only source his mathematics texts, but Prof Rucker develops fine semantic and CS arguments based on terms such as 'cake', 'rake' and, with a nod to Charles Dodgson, 'teapot' as reality descriptors; i.e., symbols.

'Teapot symbology' helped me understand topological constructs like Klein bottles and 'cake rake membranes' algebraic constructs in n-dimensions such as Calabi-Yau manifolds.
 
  • #5
Thank you for the replies. I was afraid this would impossible task, or huge atleast. There are few bigger knowledge base projects such as Cyc and ConceptNet which try to model actions too, for example http://conceptnet.io/c/en/cake. But they are too random to be usable for any simulation.
 

FAQ: Logical model of the physical world

What is a logical model of the physical world?

A logical model of the physical world is a simplified representation of the natural world that uses logical principles and mathematical equations to describe and predict the behavior of physical systems. It is often used in scientific fields such as physics and chemistry to study complex phenomena and make predictions.

How is a logical model different from a physical model?

A logical model is different from a physical model in that it focuses on the underlying principles and relationships that govern the behavior of a system, rather than its physical appearance. While a physical model may be a physical representation of a system, a logical model is an abstract representation that uses symbols and equations to describe the system's behavior.

What are the benefits of using a logical model?

One of the main benefits of using a logical model is that it allows scientists to study complex systems and make predictions without the need for physical experiments, which can be time-consuming and costly. Logical models also allow for easier manipulation and testing of different variables, providing a deeper understanding of the system's behavior.

Can a logical model accurately represent the physical world?

While a logical model is a simplified representation of the physical world, it can still accurately represent and predict the behavior of physical systems under certain conditions. However, it is important to note that logical models are based on assumptions and simplifications, so there may be limitations to their accuracy.

How are logical models used in real-world applications?

Logical models are used in a variety of real-world applications, such as weather forecasting, economic modeling, and engineering design. They are also used in the development of new technologies and products, as well as in understanding and predicting natural phenomena, such as climate change and epidemics.

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