Examining reality as a system of discrete information rather than fundamental matter.
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From “things” to “structure”
For most of human history, reality seemed simple: the world is made of things. Solid things. Tiny things. Invisible things, perhaps (waves?) — but still things.
Then physics started to quietly dismantle that picture.
We already know that atoms are not the smallest things reality is made of. Physics moved past the idea of tiny solid building blocks a long time ago.
But something even stranger happened more recently.
A group of physicists working on the most fundamental interactions in nature discovered that they could predict the outcomes of particle interactions — which results are possible, and how likely they are — without talking about particles at all. No little balls. No trajectories. Not even space and time in the usual sense.
Instead, they used a purely mathematical object — a kind of abstract geometric structure from whose properties they could extract the relationships between possible interaction events. They called it the amplituhedron.
The important point is not the math. The important point is the implication:
The behavior of the physical world could be derived from structure, not substance. From relationships, not things.
Which makes a very uncomfortable question suddenly reasonable:
if physics itself can work without particles…what is reality actually made of?
Interestingly, some of the most advanced work in theoretical physics today approaches its hardest problems by treating reality as if it were a kind of digital simulation: not in the science-fiction sense, but as a formal, abstract system used to model physical behavior.
This does not mean physicists believe we live inside a video game. It means that modeling reality as a computational process often turns out to be the most powerful way to understand it.
Question 1
But… what does “digital” mean?
A) “Digital” means reality is a fixed recording that we simply replay.
Correct: in this model, reality is not “played back.”
Not quite: “digital” here is not “pre-recorded.”
B) “Digital” means discrete information processed step by step.
Correct: “digital” implies discrete representation and step-based processing.
Not quite: “digital” implies discreteness and processing, not smooth continuity.
C) “Digital” means reality is made of tiny electronic devices.
Correct: “digital” does not require electronics — it’s a description of information processing.
Not quite: “digital” is about information structure, not microchips.
D) “Digital” implies rule-like constraints that let the system update in consistent steps.
Correct: step-based updating typically requires well-defined constraints.
Not quite: discreteness usually comes with consistent update constraints.
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From “digital” to “information”
If reality is treated as a digital process, then something has to be represented — some description of “what is the case” right now.
That brings us to a deceptively simple question: what do we mean by information in this framework?
Question 2
So… what is information in MBT?
A) Information is only what humans know and record.
Correct: information is not limited to human knowledge.
Not quite: in this model, information is more fundamental than human records.
B) Information is the set of distinctions that describe the current state and constrain what can follow.
Correct: information functions as a state-description with constraints.
Not quite: think “state description and allowed transitions,” not “facts in a notebook.”
C) Information is the physical substance the universe is made of.
Correct: this view treats matter as derived, not fundamental.
Not quite: information is not physical “stuff” in this model.
D) Information works like a constraint-map: it helps define which next states are allowed from here.
Correct: information is not just “data” — it shapes allowed transitions.
Not quite: in this framing, information helps constrain what can happen next.
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From information to representation
If the information that sustains reality is processed step by step (digitally), then it must be represented in some clear, discrete way.
In everyday digital language, the word we use for that kind of representation is bits. Let’s unpack that carefully.
Question 3
What is a bit?
A) A minimal unit of information — a basic distinction (like yes/no or 0/1).
Correct: a bit is a minimal informational distinction.
Not quite: here, “bit” means a minimal distinction.
B) A tiny physical particle that carries information through space.
Correct: a bit is not a particle.
Not quite: a bit is an informational distinction, not a particle.
C) The smallest “difference that makes a difference” in a digital description.
Correct: a bit marks a minimal distinction in the description.
Not quite: here a bit is a minimal distinction, not a “thing.”
D) The total amount of information in the universe.
Correct: a bit is a unit, not the total.
Not quite: a bit is a single unit, not the whole inventory.
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But here’s the problem
But reality seems far too complex to be built from minimal units of information.
Question 4
How do bits produce complex information?
A) By forming structured patterns and relationships that can be interpreted as richer descriptions.
Correct: complexity can arise from structured relationships.
Not quite: think “patterns and relationships,” not isolated units.
B) By becoming meaningful only when a human assigns a label to them.
Correct: patterns can exist at the system level before labels.
Not quite: labels come later — patterns can be objective descriptions.
C) By randomness alone, without any structure or constraint.
Correct: stable complexity requires structure, not pure randomness.
Not quite: randomness alone doesn’t reliably produce stable structure.
D) By enabling higher-level “compressed” descriptions: repeated bit-patterns can stand for richer structures.
Correct: repeating structure lets the system build richer descriptions from simple units.
Not quite: complexity can come from structured repetition and relationships.
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From representation to change
But if different patterns of bits represent the information present in the system… then something has to account for change.
If reality is not a static picture but an evolving process, how does that information actually get updated from one moment to the next?
Question 5
How does the system update what comes next?
A) By applying constraints to the current state to determine the allowed next state(s).
Correct: updating implies current state + constraints → next state(s).
Not quite: updating implies constraints on how state can change.
B) By replaying a pre-recorded sequence of future states.
Correct: “updating” is not the same as “replaying.”
Not quite: this model is about generation of next states, not playback.
C) By generating one of several possible next states consistent with the current constraints.
Correct: constraints can allow multiple possible next states.
Not quite: updating can allow multiple possible next states, not only one.
D) By letting the next state be completely unconstrained and arbitrary.
Correct: unconstrained changes would destroy consistency.
Not quite: if everything were arbitrary, you wouldn’t get a stable reality.
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Why constraints show up so quickly
If a system updates, then not every change can be allowed — otherwise nothing would hold together long enough to be a world.
So the next question becomes practical: what determines the “size” of what can change per update?
Question 6
What determines the size of those discrete steps?
A) Constraints built into the system that limit how much change can occur per update.
Correct: step-size is tied to constraints on change.
Not quite: think “limits on change,” not personal expectations.
B) The observer’s expectations and beliefs determine the step size of the system.
Correct: expectations are not the system’s step-size parameter.
Not quite: step size belongs to system constraints, not beliefs.
C) It can be understood like a system “resolution”: the rule-set bounds how fine-grained updates can be.
Correct: step size is linked to the limits/resolution defined by constraints.
Not quite: the idea is system-defined resolution, not personal expectation.
D) Step size is chosen randomly each update with no constraints.
Correct: randomness without constraints would break consistency.
Not quite: even with uncertainty, constraints still apply.
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Change implies time
If reality changes step by step (that is, discretely), then that pattern of change is what we might call “time.”
And in that case, time would not be continuous — it would be discrete.
Question 7
What does it mean that time is discrete in a digital universe?
A) That time is something created by human perception, not a property of the system.
Correct: discreteness is treated as a system feature, not just psychology.
Not quite: the question is about time as a system property.
B) That the system does not change continuously, but only when an update occurs.
Correct: discreteness points to change being organized in updates.
Not quite: the key idea is “updates,” not continuous flow.
C) That the future is already fully fixed before it happens.
Correct: “discrete” does not automatically mean “pre-written.”
Not quite: discreteness is about update structure, not fixed outcomes.
D) That “time” can be thought of as the system’s tick-rate: a sequence of state updates.
Correct: in a digital framing, time aligns with a sequence of updates.
Not quite: the point is time as update sequence, not perception-only.
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If time is organized… what about space?
If we’re thinking in terms of discrete updates, it’s reasonable to ask whether discreteness applies only to time — or whether space is also represented in a discrete way.
Question 8
What does it mean that space and time are discrete in MBT?
A) That the system represents change and location in finite increments rather than infinite precision.
Correct: discreteness is about finite representation, not infinite precision.
Not quite: the key is finite representation (no infinite precision).
B) That space must look “pixelated” to human eyes at normal scales.
Correct: discreteness can be system-level without visible pixelation.
Not quite: system discreteness doesn’t imply visible pixelation.
C) That “where” and “when” are represented with limited resolution (finite detail), even if it looks smooth.
Correct: finite resolution can still produce smooth experience.
Not quite: discreteness can be hidden under an emergent smooth appearance.
D) That discreteness eliminates uncertainty from the system.
Correct: discreteness does not automatically remove uncertainty.
Not quite: finite representation can still allow uncertainty and probability.
Question 9
What advantages does discretization of space and time offer?
A) It makes stable, consistent updating possible (finite steps, finite representation).
Not quite: computability doesn’t require strict determinism.
Question 16
Why does causality depend on discrete limits?
A) Because limits make randomness impossible.
Correct: constraints can exist even if uncertainty exists.
Not quite: limits don’t automatically eliminate uncertainty.
B) Without limits, the ordering of changes becomes incoherent, and “cause → effect” can’t stay meaningful.
Correct: causality relies on coherent ordering of changes.
Not quite: causality requires coherent ordering of changes over updates.
C) Because causality requires continuous time, not discrete time.
Correct: causality can still be meaningful in step-based updates.
Not quite: the model explores causality within discreteness.
D) Because limits make “before/after” transitions well-defined instead of allowing arbitrary leaps.
Correct: bounded transitions help preserve a meaningful sequence of changes.
Not quite: causality depends on consistent sequencing of state changes.
Question 17
What kind of limit is the speed of light an example of in MBT?
A) A maximum rate at which information about change can propagate within the system constraints.
Correct: it functions like a maximum propagation/change constraint.
Not quite: it’s treated as a system constraint on propagation/change.
B) A limitation caused only by our measurement instruments.
Correct: it’s treated as a rule-set constraint, not merely instruments.
Not quite: it’s framed as system-level constraint, not just measurement limitation.
C) An example of a “maximum change rate” constraint that helps keep causality consistent.
Correct: a maximum rate limit supports consistent ordering and propagation rules.
Not quite: it’s used here as a limit on propagation/change.
D) Proof that the system must be continuous rather than discrete.
Correct: a maximum rate constraint can exist in discrete models too.
Not quite: maximum rate constraints are compatible with discrete updating.
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From simple units to complex behavior
We have seen that rich patterns and behaviors can emerge from very simple, discrete elements interacting over time.
So what do we mean, precisely, when we call something a “complex system” in this model?
Question 18
What is a complex system in MBT?
A) A system that is complex only because humans can’t understand it.
Correct: complexity is not defined by human confusion.
Not quite: complexity is a system property, not a human limitation.
B) A system where many interacting parts can generate rich, large-scale behavior over time.
Correct: complexity emerges from interaction under constraints.
Not quite: complexity is about interaction and emergence.
C) A system with no structure where everything is random.
Correct: randomness alone is not structured complexity.
Not quite: complex systems have structure and interaction patterns.
D) A system that can have structured behavior even when you can’t easily predict every detail.
Correct: complexity can be structured without being perfectly predictable.
Not quite: unpredictability is not “no structure.”
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Order without a designer?
If a complex system is made of many interacting parts, then an obvious question arises:
does order always need to be imposed from the outside?
Question 19
What does “self-organization” mean in a complex system?
A) Order and structure can arise from the interactions within the system, without a central arranger.
Correct: self-organization is internal emergence of order.
Not quite: “self” means structure arises from internal interactions.
B) A system is self-organized only if an external controller constantly corrects it.
Correct: external control is the opposite of self-organization.
Not quite: self-organization does not require constant external control.
C) It can produce repeating patterns without requiring a “boss” part telling everything what to do.
Correct: order can emerge from local interactions rather than central command.
Not quite: self-organization emphasizes internal interaction over top-down control.
D) Self-organization means the system follows a fixed script from the start.
Correct: self-organization is dynamic emergence, not a prewritten script.
Not quite: emergence is not the same as a fixed script.
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Patterns that repeat
When order emerges from interaction, it often does not appear just once.
Sometimes similar structures show up again and again, across different scales.
Question 20
What is a fractal in the context of MBT and complex systems?
A) A pattern with no structure — just chaos.
Correct: fractals can be highly structured.
Not quite: fractals are not “structureless chaos.”
B) A pattern that can repeat similar structure across different scales.
Correct: fractals are known for self-similarity across scales.
Not quite: the key is repeating structure across scales.
C) A pattern that exists only in nature, not in mathematical or simulated systems.
Correct: fractals can arise in mathematics and simulation too.
Not quite: fractals can be generated by iterative rules.
D) Something that can arise from iteration — repeating the same transformation again and again.
Correct: repeated rule-application (iteration) can generate fractal structure.
Not quite: fractals are often linked to iteration in rule-based systems.
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Fractals (step-by-step)
Two classic fractals built by repeatedly removing the central part of a simple shape.
First you see the process (steps 0–2) and, beside it, step 3 enlarged.
Sierpiński triangle
Step 0
Step 1
Step 2
(Step 3 →)
Step 3 (enlarged)
At this step the same rule is applied to all sub-parts, generating 27 new copies.
Start with an equilateral triangle, remove the central sub-triangle, and repeat the same rule on the remaining ones.
Sierpiński carpet (square)
Step 0
Step 1
Step 2
(Step 3 →)
Step 3 (enlarged)
At this step the same rule is applied to all sub-parts, generating 64 new copies.
Start with a square, remove the center of a 3×3 grid, and repeat the same rule on each remaining square.
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Patterns don’t appear by accident
If a pattern keeps showing up, the system must be doing something consistently to produce it — applying what we call iteration.
Question 21
What does “iteration” mean in this context?
A) Repeating the same process again and again, applying the same constraints each time.
Correct: iteration is repeated application over updates.
Not quite: iteration means repetition of the same process.
B) Randomly changing the rules at each step.
Correct: iteration is repetition, not rule-randomization.
Not quite: iteration assumes stable constraints across repeats.
C) The engine behind many emergent patterns: repeat + constraints + feedback over many steps.
Correct: iteration is the repeated “do it again” mechanism that can amplify structure over time.
Not quite: iteration is central to how patterns can build up across updates.
D) Stopping the system once it reaches a stable pattern.
Correct: stabilization can be a result, not the definition of iteration.
Not quite: iteration is the repeated process itself, whether or not patterns stabilize.
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Section 1 wrap-up
We’ve set up the “engine-room” view: reality as information, represented discretely, updated through constraints, and capable of generating stable complexity through iteration.
Next, we shift focus: how observation and measurement relate to the reality we experience.
