Natural Causality
Junior high. The gym’s decorated with streamers and a DJ’s in the corner. You’ve been frozen against the wall, telling yourself you can’t dance.
Five songs in, someone catches your eye and smiles. You glance down at your feet. Now they’re moving, shuffling side to side. You have no idea when it started.
You think, “the music made me dance.”
There it is. The mechanism that gets overlooked. From inside the blindfold, direct causation makes perfect sense. We see A followed by B and say A caused B. But music doesn’t make anybody dance. We dance in response to the music.
Traditional causality hasn’t formalized the distinction between causing and responding. Part III develops Natural Causality to model how influence propagates through receptive capacity.
Contents
7.1 Why vs. How
7.2 What’s Been Missing
7.2.1 Blindfolded Causal Inference
7.3 Foundations of Natural Causality
7.3.1 States and Transformation
7.3.2 Causal Spaces and Their Boundaries
7.3.3 Admittance, Cross-Impedance, and Transitions
7.3.4 Induction and Readiness
7.4 Natural Causality in Action
7.4.1 Cognition
7.4.2 Ecosystems
7.4.3 Technology
7.4.4 Economics
7.5 Closing Remarks
7.1 Why vs. How
We ask why something happened and accept almost any reason that sounds convincing. Why and Because create stories that feel complete. Match a convincing Why with a logical Because and we can all move on.
How works differently.
How lives under the surface. We can’t see influence propagate. We see outcomes and build stories to connect them.
When a bridge collapses, people ask Why. Someone offers Because. The crowd nods. But the bridge stays broken until someone asks How.
Why and Because organize meaning. How gets us closer to happening.
The door to Natural Causality opens with How.
7.2 What’s Been Missing
Traditional causality assumes direct propagation. Event A causes Event B, which causes Event C. Linear chains connect causes to effects. We follow them backward to locate root causes, forward to predict outcomes. This works for organizing much of what we observe.
But influence doesn’t propagate through direct contact. One process creates change, another responds based on its capacity to engage. Nothing pushes from cause to effect.
This is where the distinction between domains pays off. The Blue Space (Causation Domain) is where influence propagates. Red Spaces (Interpretative Domain) are where processes respond based on internal readiness. Traditional causality blends the two, treating propagation and response as if they were one and the same. They’re not.
A yawn doesn’t force the next yawn. Supply doesn’t dictate demand, and demand doesn’t dictate supply. Every interaction involves a receiving process responding from its own internal state.
Traditional causality hasn’t accounted for this.
You punch someone for no reason, they bruise. Legally and practically, you caused the injury. Natural Causality doesn’t dispute this. It shows the mechanism underneath: your fist created conditions, their tissue responded based on its properties. The bruise is their response, not something you gave them. Same outcome, but with an understanding of the happening underneath.
The distinction between causing and responding remains unformalized. So how have we been handling causation without it?
7.2.1 Blindfolded Causal Inference
The challenge facing every scientist is distinguishing true causal relationships from mere correlations. Just because two things happen together doesn’t mean one caused the other. Ice cream sales and drowning deaths both rise in summer, but ice cream doesn’t cause drowning. Warm weather affects both.
The gold standard for establishing causation is randomized controlled trials. Randomly assign people to treatment or control groups, eliminate confounding variables, measure outcomes. When groups differ only in the treatment they received, we can confidently say the treatment caused the difference.
When RCTs aren’t possible, we use observational methods: regression models controlling for confounders, propensity score matching, directed acyclic graphs mapping relationships, time-series analysis.
Yet all these methods share a fundamental limitation. They work from inside interpretation. We observe outcomes in our Red Spaces, control for variables we can measure, make assumptions about causal chains, then infer relationships.
Traditional causal inference doesn’t account for receptive capacity determining response. After observing A followed by B enough times, we build models where A causes B. The music made you dance becomes our working model, even though you danced when you were ready.
These methods organize what we observe without modeling how influence propagates through receptive capacity. Natural Causality formalizes that mechanism.
7.3 Foundations of Natural Causality
We do not observe causation. The blindfold prevents that. But we can model the Causation Domain by tracking patterns of interpretation. When processes respond consistently, we infer the mechanisms beneath.
When resistance appears, when influence spreads or stops, when transformations trigger further transformations, these responses show how causation works in the Blue Space.
Causation happens through transformations within defined environments. Each environment has rules. Each process has resistance. Rules govern what can happen. Resistance determines what does happen.
7.3.1 States and Transformation
Causation works through state transformation. A cause is a state or condition of a process. An effect is the transformed state that follows.
Consider water flowing downhill. At any moment, the water occupies a position with a certain velocity and volume. Gravity pulls it, terrain channels or blocks it, momentum carries it forward. The next moment brings a new state. The transformation flows continuously.
A conversation works the same way. Each person maintains an internal state: current thoughts, emotional tone, readiness to speak. One person’s statement alters what the listener faces. The listener’s state transforms based on what they heard, how they interpreted it, and their capacity to engage. Their response then changes what the first person faces.
We describe causation as “Event A caused Event B,” treating causes and effects as discrete events with clear boundaries. Events are interpretations we impose on continuous transformation.
The distinction between “before” and “after” helps us organize what we observe. The underlying transformation doesn’t operate in discrete steps. Causation is the continuous process by which states change in response to what affects them.
This changes how we model influence. Instead of tracing chains of discrete events, we track how states alter, how processes resist or accommodate those alterations, and how transformed states create further change.
7.3.2 Causal Spaces and Their Boundaries
A causal space is a conceptual environment where specific rules determine outcomes.
Each space has a boundary, marking where its rule applies. Some boundaries are strict, others allow influence to pass through in different ways. Causal impedance measures this:
- Low impedance: A process moves easily within the space and fully engages with its rules.
- High impedance: A process interacts weakly within the space and needs additional support before it engages.
- Infinite impedance: A process is entirely outside the space and unaffected by its rules.
Language comprehension can be modeled as a causal space. A familiar word has low impedance and gets understood immediately. A word in an unfamiliar language has high impedance and doesn’t trigger meaning without further engagement. A random sequence of sounds has infinite impedance and falls completely outside the space of comprehension.
7.3.3 Admittance, Cross-Impedance, and Transitions
Admittance is the counterpart to impedance. It describes how easily something moves through a causal space. A well-integrated notion, a practiced skill, or a widely accepted idea has high admittance and spreads easily. A new, unfamiliar, or disruptive idea has low admittance and requires more effort before gaining traction.
When something moves between spaces with different rules, it encounters cross-impedance. A business model that works well in one industry may face cross-impedance in another, requiring adaptation. A subscription-based pricing strategy that succeeds in software may not transfer directly to consumer goods, where buying habits follow different patterns.
7.3.4 Induction and Readiness
Every transformation is an induced response. Even what looks like force only works when receptive capacity permits it.
A yawn propagates influence. Another person responds when their internal state aligns. A change in supply influences purchasing behavior. Light provides the trigger and internal processes respond, producing vision.
High impedance prevents response. High admittance enables flow. Cross-impedance requires transformation when moving between different rule systems.
Causality operates as a web of readiness and response.
7.4 Natural Causality in Action
Impedance, admittance, and cross-impedance work the same way across cognition, ecosystems, technology, and economies.
7.4.1 Cognition
A student who understands arithmetic has low impedance to basic algebra. Variables feel like natural extensions of numbers, and learning flows easily.
Another student struggles with fractions. When algebra introduces rational expressions, impedance spikes and progress stalls because the foundation is missing or unstable.
Admittance works differently. A student fascinated by patterns sees algebra in music intervals, game mechanics, recipe scaling. High admittance means concepts spread easily across contexts.
Cross-impedance occurs when moving between domains. A physics student understands vectors mathematically but struggles to apply them to forces. The math flows easily until it needs translation into physical modeling. That translation carries its own impedance.
Understanding derivatives prepares the ground for learning integration. One concept reduces impedance for related concepts, making further concepts more receptive through induction.
7.4.2 Ecosystems
Kudzu arrived in the American South with low impedance. The climate matched its native range. No specialized predators offered resistance. The plant found high admittance and spread rapidly.
A wildfire clears dense undergrowth, allowing sunlight to reach the forest floor. Dormant seeds that couldn’t grow in shade now germinate. The fire reduced impedance for light-dependent species. The seeds responded based on their internal readiness built through evolutionary adaptation to post-fire environments.
Mangroves occupy the space between terrestrial and marine environments. Saltwater creates impedance for terrestrial plants. Wave action creates impedance for land animals. Mangroves succeed through specialized structures that reduce cross-impedance at the boundary.
Predator and prey populations cycle. When prey grows abundant, predators thrive. As predator numbers grow, prey populations decline. Fewer prey means predators face higher impedance to survival, causing their population to drop. Lower predator numbers reduce impedance for prey, which rebounds. Each event creates the conditions that induce the next.
7.4.3 Technology
The iPhone launched in 2007 with a touchscreen keyboard. Blackberry users resisted it. Years of muscle memory for physical keys made the touchscreen difficult to adopt. The interface faced high impedance with existing phone users.
Early smartphones had no app ecosystem, network speeds were slow, and people had no habits around touchscreens. Adoption was gradual.
As networks improved and apps developed, impedance dropped. Each new user reduced impedance for the next, social pressure built, and within years smartphones were everywhere.
Cross-impedance occurs when technology moves between contexts. Touchscreen interfaces work well for phones. In cars, drivers need tactile feedback to avoid distraction. The interface that succeeded in phones met resistance in vehicles because driving operates under different safety constraints.
7.4.4 Economics
Central banks raise interest rates. The policy change propagates differently through different sectors.
Mortgage rates jump from 3% to 7%. Monthly payments on a $400,000 home increase by $1,000. Homebuyers face immediate impedance. Many can no longer afford homes that were within reach months earlier. Housing demand drops within weeks.
Large corporations have different exposure. Many locked in low-rate debt years ago. Rising rates create minimal impedance for their existing operations. They continue investing and expanding.
Small businesses rely on variable-rate credit lines. Their borrowing costs spike immediately. Expansion plans stall. Hiring slows. The rate increase meets low impedance in some sectors and high impedance in others.
The Federal Reserve raises rates. The dollar strengthens. Developing market countries that borrowed in dollars face higher repayment costs. A policy designed for one economic space induces responses in markets operating under different rules.
Each sector responds based on its own structure and constraints. The rate increase changes the environment. Some processes adapt easily. Others face resistance. Higher rates don’t force specific outcomes. They alter what businesses, consumers, and investors respond to based on their receptive capacity.
7.5 Closing Remarks
The music’s loud, bass shaking the gym floor. Half the room’s already dancing. Another group stays frozen against the wall. A few slip out to the hallway. Same music, completely different responses.
Traditional causality organizes outcomes from inside interpretation without accounting for the orthogonal relationship between domains. Natural Causality models Blue Space influence meeting Red Space receptive capacity.
The same impedance relationships apply whether modeling minds, ecosystems, the cosmos, or businesses.
Chapter 8 formalizes causal spaces and how different environments enforce different rules. Chapter 9 develops the mathematics. Chapter 10 applies the framework to complexity across contexts.
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