M: Say that hypothetically, I managed to give you every piece of information you could possibly need for a question, and then ask you to analyze second, third, and fourth order likely affects. As you get higher in the number, what level of resource and or time use do things increase by? Is it linear, exponential, something even worse…?

G: Short answer: It’s not linear. The cost of analyzing second-, third-, and fourth-order effects tends to grow exponentially or worse, depending on:

Branching factor (how many effects each cause has),
Interdependencies (how effects feed back into or compound with each other),
Stability of the system (chaotic or self-correcting),
Scope of simulation or prediction.

Breakdown:

Order of Effect	Description	Resource Scaling Trend
1st Order	Direct consequence	Baseline
2nd Order	Consequences of the consequence	~3–10× more complex
3rd Order	Consequences of those consequences	~10–100× more complex
4th Order+	Deep network ripple effects	Explosive or intractable

Why It Gets Worse:

Combinatorial Explosion: If each effect spawns 5 new effects, then:
- 1st order: 5 outcomes
- 2nd order: 25
- 3rd order: 125
- 4th order: 625
- …and so on.
Feedback Loops: Some effects loop back into the original system, requiring recursive logic or simulation models.
Dimensional Drift: Higher-order effects often cross domain boundaries (e.g., economic → political → social → technological), needing new kinds of models or data.

Summary:

2nd-order is usually manageable.
3rd-order starts to require significant judgment or modeling assumptions.
4th-order and up is where human reasoning or even bounded AI starts bluffing or pruning aggressively just to cope.

So: scaling is exponential or worse, unless assumptions allow you to collapse branches.