The physicist Jon Wheeler is often credited for the ingenious reductionist framework known as “it from bit” philosphy. This idea reduces redundant terminology such as force, attraction, repulsion, and etc. to the more efficient framework of “interaction”. This idea is extremely powerful in that it can solve serious problems such as the infinite regression problem, as well as help simplify the complexity that accompanies trying to learn about living things. To explain the basic idea of “it from bit” philosophy I will use a common everyday example from physics: a baseball batter hitting a home run.

In freshmen level physics courses, we learn how to accurately calculate the trajectory of the baseball from the home plate to the outfield just by looking at a few variables such as initial velocity, the launch angle, and gravity. These equations do not just apply to baseball. Any object, given a set of initial conditions, can have its motion predicted using classical mechanics. This wont be a physics lessons so dont sweat the details too much, the point that I am making here and the idea that is central to understanding “it form bit” philosophy is that one can view the baseball being hit at home plate out of the park as merely the exchange of information.

Lets look at a mathematical equation that models this behavior ie a parabola:

Image proudly made with DESMOS. In this picture I have made some modifications to make the parabola more fit to scale, again dont worry about the details just notice the shape

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We can notice that the graph above, is merely a physical representation of abstract exchange of information: ie a function (a “black box” that takes information, performs an operation, and then generates an output).

It is not an accident that objects follow a parabolic path that can be modeled using basic algebra.

Regardless of whether we are talking about a baseball moving from home plate to the outfield or merely a graph of a parabola, the motion and description are inherently the same. Just substitute the variable terminology and they are in fact the exact same. I cannot tell the difference the between X and Y values of a parabola graphed out on paper from a baseball struck at home plate with its vertical and horizontal positions moving as it flies through the air.

Since there is no way to tell the difference between ‘X and Y’ on a graph and “Horizontal and Vertical position” on a baseball field these two operations are identical. Since there is no observable difference we cannot differentiate the two.

Therefore one can describe the baseball being struck into the outfield by the batter as merely the physical substrates of abstract information exchange.

This idea is not just limited to solving physics problems about baseball. When we study introductory physics, we are studying the physical substrates of abstract information exchange in the context of classical mechanics. When we study chemistry we are studying the physical substrates of abstract information exchange in the context of the various sub-fields of chemistry such as analytical, physical, organic and biochemistry.

Even in the less “quantitative” fields of chemistry such as organic chemistry (which IS actually very quantitative by its true nature, just not taught as such at the introduction level) or even biology: we see physical substrates of abstract information exchange take place.

This idea is incredibly useful in that is allows us to simplify things into a predictive framework that is not overwhelmed by an insurmountable information overload. When complicated things such as ribosomes, RNA, nucleoui, or chromatin become reduced down to “physical substrates” you end up being able to view biological systems as merely complex adaptive systems that are operating in non-equillibirating thermodynamics.

In fact one can view all of biology as merely the continuous replication and interaction of information within its environment.

In his Nobel Prize acceptance speech Sydney Brenner is quoted saying “*We are all conscious today that we are drowning in a sea of data and starving for knowledge. The biological sciences have exploded, largely through our unprecedented power to accumulate descriptive facts. How to understand genomes and how to use them is going to be a central task of our research for the future. We need to turn data into knowledge and we need a framework to do it. “*

It is my optimistic belief that the necessary framework that will turn “data into knowledge” lies in understanding “it from bit” philosophy.

*Afterthoughts*

If you want to learn more about why the world around is, in the most reductionist framework: a purely mathematical structure I would recommend the works of Max Tegmark, Brian Greene, and Don Zaiger.

To summarize their works quickly, the entropy of a black hole ( a 3 dimensional object) is related to the area ( 2 dimensional) NOT the volume (3 dimensional).

This points to the idea that the universe itself is a hologram.

Adrian Leatherland is quite famous for creating incredibly realistic *purely mathematical* images generated from prime numbers and a simple algorithm.

Don Zagier, explains this further in his incredible blog which you can find here.

If there is no way to tell the difference between images made out of mathematical operations and images taken in the “real” world, then the two images are equivalent. Since there is no observable difference we cannot differentiate the two.