CAUSALITY
The following terms and their respective definitions describe causality as an emergent property of a sufficiently complex and orderly universe which enables an information processing agent inhabiting that universe to consistently achieve its goals (rather than bumble around more aimlessly) as a consequence of that information processing agent systematically aligning its memorized and theoretical knowledge about how its encompassing universe generally works with novel sensory input from that information processing agent’s encompassing environment (due to the fact that the information processing agent believes that some events are more likely to occur than are any other events at a given point in that information processing agent’s runtime (after that information processing agent attain a sufficiently accurate worldview)).
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CAUSALITY: (synonym: causation, cause-and-effect) the apparent emergence of relatively recent phenomena from relatively older phenomena according to some observing frame of reference which observes the chronological succession of such events (especially in conjunction with the the observing frame of reference being informed by abstract conceptualizations about what that frame of reference observes as being consistent with expectations about how physical processes generally transpire).
What makes the observation of causality distinct from the observation of mere randomness is that the observation of causality entails gathering a sufficiently large amount of empirical evidence that some types of events are sufficiently likely to occur while all other types of events are insufficiently likely to occur.
An example of past events having causal influence over future events is simulating probability without replacement. For example, one marble can be taken out of an opaque box containing exactly nine marbles in a randomized fashion such that the observer of that process does not know what color the marble will be initially while being informed that there are initially exactly three red marbles, exactly three blue marbles, and exactly three green marbles and that one marble per every thirty seconds will be randomly ejected from the box. Initially, and based on probability theory, the observer can logically assume that each color of marble has the same likelihood of being ejected from the box during the first marble ejection. If the observer sees that the first ejected marble was green, the second ejected marble was blue, and the third marble was green, the observer can calculate that there are more red marbles remaining inside the box than blue and green marbles and use that knowledge to calculate that the probability that the next ejected marble will be green is (1/6), that probability that the next ejected marble will be blue is (2/6), and that the probability that the next ejected marble will be red is (3/6). Based on those calculations (and expectation that physical reality will conform to probability theory), the observer logically assumes that the most likely color to be ejected next is red. The emergence of red would be based on the fact that prior events made red more abundant than the other colors to be selected.
EVENT: a pattern of phenomena occurring within a specific and finite time interval.
A single event is generally considered to be one of multiple events which occur in chronological succession (and such that each of those events occurs inside of its own unique time interval in temporal isolation from the other events in that succession). In other words, an event is generally conceptualized as taking place within the larger encompassing context of a causally-linked chain of events.
TRAJECTORY: a linear spacetime continuum in which non-overlapping events occur in immediate chronological succession (especially from the perspective of a frame of reference which travels along that spacetime continuum and observes those events).
The image displayed on this web page depicts a linear succession of events observed by some information processing agent. That linear succession of observed events is colored green and represents a particular decision-making trajectory which that information processing agent implemented from the point in time labeled time_0 to the point in time labeled time_3.
The particular trajectory of events which that information processing agent (actually) traversed can be represented by the following sequence of symbols:
e0 -> e1 -> e4 -> e9
Each of those four events occurred inside of its own finite time interval. Those time intervals happened in immediate succession of each other in exactly one chronological order as depicted by the following sequence of symbols:
time_0 -> time_1 -> time_2 -> time_3
A more comprehensive model of the aforementioned trajectory of events can be depicted by the following sequence of symbols:
time_0(e0) -> time_1(e1) -> time_2(e4) -> time_3(e9)
When the information processing agent was localized to time_0, the information processing agent (supposedly) could have traversed an alternative trajectory than the one depicted in the preformatted text box above.
At time_0, the information processing agent was in the midst of deciding whether to choose the action labeled e1 or else the action labeled e2 (while the information processing agent was in the midst of implementing the action labeled e0).
Through a process of elimination, the information processing agent chose to implement e1 instead of e2. That decision constrained the information processing agent’s next decision at time_1.
At time_1, the information processing agent was in the midst of deciding whether to choose the action labeled e3 or else the action labeled e4 (while the information processing agent was in the midst of implementing the action labeled e1).
(If the information processing agent had chosen e2 instead of e1 while that information was in the midst of making that decision during the finite time interval labeled time_0, the information processing agent would have then conceived that it only the options e5 and e6 to choose from at time_1 instead of the options e3 and e4).
Through a process of elimination, the information processing agent chose to implement e4 instead of e3. That decision constrained the information processing agent’s next decision at time_2.
At time_2, the information processing agent was in the midst of deciding whether to choose the action labeled e9 or else the action labeled ea (while the information processing agent was in the midst of implementing the action labeled e4).
(If the information processing agent had chosen e3 instead of e4 while that information was in the midst of making that decision during the finite time interval labeled time_1, the information processing agent would have then conceived that it only had the options e7 and e8 to choose from at time_2 instead of the options e9 and ea).
Through a process of elimination, the information processing agent chose to implement e9 instead of ea. That decision constrained the information processing agent’s next decision at time_3 (though the diagram does not show what the information processing agent does after time_3).
The following list depicts each trajectory which the information processing agent (supposedly) could have traversed starting from time_0:
time_0(e0) -> time_1(e1) -> time_2(e4) -> time_3(e9) // actual path time_0(e0) -> time_1(e1) -> time_2(e4) -> time_3(ea) // alternative path time_0(e0) -> time_1(e1) -> time_2(e3) -> time_3(e7) // alternative path time_0(e0) -> time_1(e1) -> time_2(e3) -> time_3(e8) // alternative path time_0(e0) -> time_1(e2) -> time_2(e5) -> time_3(eb) // alternative path time_0(e0) -> time_1(e2) -> time_2(e5) -> time_3(ec) // alternative path time_0(e0) -> time_1(e2) -> time_2(e6) -> time_3(ed) // alternative path time_0(e0) -> time_1(e2) -> time_2(e6) -> time_3(ee) // alternative path
The following list depicts some of the trajectories which the information processing agent (supposedly) could not have traversed starting from time_0 (due to the fact that such “impossible trajectories” violate causality as the diagram depicts):
time_0(e0) -> time_1(e1) -> time_2(e4) -> time_3(e0) // causal loop time_0(e0) -> time_1(e1) -> time_2(e4) -> time_3(e3) // temporal non-linearity time_0(e0) -> time_1(e1) -> time_2(e4) -> time_3(ee) // jumping to an alternative timeline time_0(e0) -> time_1(e1) -> time_2(e4) -> time_3(e1) // traveling backwards in time
This web page was last updated on 10_JULY_2025. The content displayed on this web page is licensed as PUBLIC_DOMAIN intellectual property.