Living systems assign relevant meaning to Data Stream, not physical objects. Why? Our relationship with our environment is through Data Streams, not actual bodies.
However due to its utility, we objectify the informational content of Data Streams. This shortcut enables us to handle a greater amount of information more efficiently and rapidly. For instance, organizing multiple data streams around a single object, say a sex partner, is a simplification that speeds both storage and retrieval.
However, this categorization reduces dimensionality. While useful in daily affairs, categories distort reality as they truncate potentially significant information. Objectification is essential when demands are urgent. However, when attempting to formulate a more complete picture of reality in a more leisurely setting, better return to the data that was objectified. In this light, we find it very fruitful to understand our relationship to the environment via data streams, rather than the simplification of objects.
You might wonder: What’s the difference? As living systems have survived for billions of years, we have obviously constructed a fairly accurate representation of external and internal reality through our relationship to data streams. Why quibble over data vs. what the data represents? The relationship between the two is close enough for survival.
It’s akin to mistaking the map for the territory. In this case the physical representation is a simplification of the content and trajectories of the data stream. Therein lies the utility of the map – its ability to simplify by reducing dimensionality. The purpose of dimension reduction lies in eliminating random noise and extraneous information to better focus upon the more salient features of reality.
Indeed, the objectification of data streams has proved to be an incredibly useful evolutionary tool. Witness the profusion of animals that have the ability to move towards food or sexual partners and away from toxic environments or dangerous predators. Due to their inactivity, plants don’t need to objectify their environment, e.g. bees.
So why does it matter? The map, the metaphor, the abstraction, idea and/or mental construct are all simplifications of a far more complex reality. Any simplification, any category, enfolds part of the complexity into the fabric – hiding it from view. While sometimes useful, sometimes simplifying can become a distortion that obscures significant information.
Let’s provide a concrete example of the power of data stream analysis. Humans, from time immemorial, have anthropomorphized their environment – seeing people-like gods and demons everywhere and in everything, for instance sky gods or earth goddesses. Philosophers have posed many reasons for this unusual but pervasive trait.
Let us analyze the same phenomenon from the perspective of data streams and metaphoric logic. Many talk of their car or computers as if they are alive. Why? Their relationship with these inanimate objects is closer to their relationship with other life forms than it is with less animate, less interactive, and more reliable, hence predictable objects – say a toaster.
The data streams (DS) associated with cars and humans share more in common than they do with the DS of toasters. Toasters generally work the same in all instances – on, off or broken. Cars on the other hand have myriad states that are multiplied by the vicissitudes of time, perhaps a scratched body, torn fabric in the interior, and/or inoperable electronics. We anthropomorphize our cars because the logic of their data streams is closer to the logic of humans than to the logic of toasters.
Despite our intellectual understanding that cars are not alive, we, especially those of us with old vehicles, attempt to appease our aging companions with pleading, respect and consideration. Despite our knowledge that inanimate objects are not alive, we continue to project human attributes upon them. Why? The logic of their data streams is similar to the logic of our data streams.
It is difficult, if not impossible, to overestimate the importance of data streams to the Attention of all living creatures. This mode of analysis ultimately provides a plausible explanation for a question that has long haunted evolutionary theorists, including Darwin. Do groups evolve as a dis-embodied unit or as individual units, e.g. organisms or genes?
Before tackling the thorny question of group evolution, we must first explore the dynamics of data streams. The Living Algorithm (LA) is the mathematical realization of Attention’s image overlay process. The LA generates a mathematical system deemed Data Stream Dynamics (DSD). Modeled after the constructs of traditional Newtonian dynamics, DSD is a key player in the mystery. The system reveals the inner dynamical workings of our relationship with data streams. An understanding of DSD is crucial to the understanding of group dynamics, which is at the heart of group evolution.
Let us begin this discussion with DS Time and the significance of the Iteration Rate.