ripple_logic

The Complete Decision Engine

A mathematical framework for ethical intelligence. Every decision generates ripples across stakeholders and dimensions. This engine measures impact, enforces rights floors, and learns from outcomes.

Loop

NCAR

Notice Choose Act Reflect

⊕

9 Unions

Stakeholder layers from Self to Universal. Every decision ripples through these.

\(U = \{u_1, u_2, \ldots, u_9\}\)

◈

7 Dimensions

Welfare axes: Material, Health, Social, Knowledge, Agency, Meaning, Environment.

\(D = \{d_1, d_2, \ldots, d_7\}\)

⊘

Rights Floor

Non-compensatory constraint. If violated, the option fails. No exceptions.

\(\min_{u,d} \text{Floor}(a) = 1\)

∿

Ripple Score

Weighted impact from −100 to +100. Direction matters. Measurement enables learning.

\(R(a) \in [-100, +100]\)

Notice

Perception before calculation. Define the decision, options, and stakeholder map.

Frame the Decision

\(\textbf{Q} = \text{compress}(\text{situation}) \rightarrow \text{1 sentence}\)

↓ messy reality ↑ decision Q

Compress Reality into a Question

Before any calculation, force clarity. What is the actual decision? Write it in one sentence that can be answered.

Input

Ambiguous situation

Output

Q: clear, testable

Generate Options

\(\mathcal{A} = \{a_1, a_2, \ldots, a_n, a_{\text{redesign}}\}\)

↓ decision Q ↑ option set 𝒜

List Candidate Actions

Create a small set of real options. Always include a redesign option \(a_{\text{redesign}}\) if all visible choices harm stakeholders. The option set is expandable.

Input

Decision Q

Output

𝒜 = {options}

Map Stakeholders

\(U = \{\text{Self}, \text{Household}, \text{Community}, \text{Org}, \text{Polity}, \text{Humanity}, \text{Bio}, \text{Cosmic}, \text{Universal}\}\)

\(D = \{\text{Mat}, \text{Health}, \text{Social}, \text{Know}, \text{Agency}, \text{Mean}, \text{Env}\}\)

↓ scope ↑ map (U, D)

Define the Ripple Field

Specify which unions and dimensions are in scope. The default is all 9 unions and all 7 dimensions. Quick mode: identify top 3 unions. Deep mode: map all with weighted importance.

Input

Scope of responsibility

Output

(U, D) stakeholder map

Choose

Governance in action. Score ripples, enforce floors, select the best feasible option.

Estimate Ripples

\(r_{u,d}(a) \in [-100, +100]\)

For each action × union × dimension

↓ action a, map ↑ ripple matrix r(a)

Score Impact Per Cell

For each action, estimate its ripple on each union–dimension pair. This is honest estimation, not prediction. Positive values help; negative values harm. Uncertainty is noted, not hidden.

Input

Action a, map (U, D)

Output

Matrix r(a) ∈ ℝ^|U|×|D|

Assign Weights

\(\sum_{u \in U} w_U(u) = 1, \quad w_U(u) \geq 0\)

\(\sum_{d \in D} w_D(d) = 1, \quad w_D(d) \geq 0\)

↓ priorities ↑ weights w_U, w_D

Make Priorities Explicit

Weights encode what matters without hiding it. Default to equal weights (simple mode) or use stakeholder input (transparent mode). Weights must be auditable and never used to bypass the floor.

Input

Values, stakeholder input

Output

w_U, w_D vectors

⊘

Step 5: The Rights Floor (NCRC)

The non-compensatory rights constraint. This is the hard boundary that cannot be crossed regardless of total score. If any union–dimension cell violates fundamental rights, the action is infeasible. No aggregation of benefits can justify crossing this floor.

\(\text{Floor}_{u,d}(a) \in \{0, 1\}\)

\(a \in \mathcal{F} \iff \min_{u,d} \text{Floor}_{u,d}(a) = 1\)

If any cell = 0, the action fails outright

⚠️ Floor violations include: coercion, manipulation at scale, non-consensual harm, dignity violations, life-support degradation, bodily integrity violations

Choose (continued)

Aggregate scores, apply constraints, select optimal feasible action.

Compute Union Scores

\(S_u(a) = \sum_{d \in D} w_D(d) \cdot r_{u,d}(a)\)

↓ r(a), w_D ↑ S_u(a) per union

Collapse Dimensions per Union

A single net score per union. This reveals who benefits, who pays, and where trade-offs appear. Negative union scores signal harm that may require redesign.

Input

r_u,d(a), w_D

Output

S_u(a) for each union

Compute Total Ripple Score

\(R(a) = \sum_{u \in U} w_U(u) \cdot S_u(a)\)

↓ S_u(a), w_U ↑ R(a) total score

One Number to Compare

The total ripple score combines all unions and dimensions transparently. This is your comparison metric, not your moral justification. Higher scores indicate better net ripple, but only among floor-safe options.

Input

S_u(a), w_U

Output

R(a) ∈ [-100, +100]

Win-Win Constraint (Optional)

\(\forall u \in U: S_u(a) \geq 0\)

\(\mathcal{F}_{WW} = \{a \in \mathcal{F} : \text{all unions non-negative}\}\)

optional strong mode ↑ 𝒻_WW

Require No Losers

Optional "strong mode": if feasible, require non-negative net score for every union. If no win-win option exists, use redesign to expand the option set rather than accepting forced sacrifice.

Input

S_u(a) for all unions

Output

𝒻_WW ⊆ 𝒻

Select Best Feasible Action

\(a^* = \arg\max_{a \in \mathcal{F}} R(a)\)

↓ 𝒻, R(a) ↑ chosen action a*

Choose the Highest-Scoring Floor-Safe Option

Among all rights-safe options (or win-win options in strong mode), select the one with the highest total ripple score. This is governance, not gambling.

Input

𝒻 (or 𝒻_WW), R(a)

Output

a* = optimal action

Act

Execution is part of alignment. The smallest clean action moves reality.

Execute

\(\textbf{Execute}(a^*) \rightarrow O\)

O = real-world outcome

↓ a* ↑ outcome O

Smallest Clean Action

Thinking is not doing. Only action updates reality. Choose the smallest action that reliably advances the best ripple direction. External actions (messages, changes) and internal actions (boundary-setting, regulation) both count.

Input

Chosen action a*

Output

Real-world outcome O

Reflect

Learn without shame. Update the model. Feed signal back into the system.

Observe Outcome

\(O = \text{Observe}(\text{world after } a^*)\)

↓ post-action state ↑ observations O

Reality Feedback

Reflection begins with observation. What actually happened across unions and dimensions? Where did predictions match reality? Where did they diverge?

Input

Executed action a*

Output

Observed outcomes O

Update Estimates

\(r'_{u,d}(a) = \text{Update}(r_{u,d}(a) \mid O)\)

↓ prior r, outcome O ↑ improved r'

Learn and Recalibrate

Revise your future scoring based on evidence. This is how the system becomes smarter over time without changing its ethics. The floor remains fixed; estimation improves.

Input

Prior r(a), outcome O

Output

Improved estimates r'(a)

∞

Loop Back

\(\text{NCAR}: \text{Notice} \rightarrow \text{Choose} \rightarrow \text{Act} \rightarrow \text{Reflect} \rightarrow \text{Notice}\)

↓ new state ↑ next cycle

The Loop Never Ends

This is not a one-shot decision tool. It is a living practice loop that improves outcomes through consistent correction. Run it daily. Consistency beats intensity.

Input

New reality state

Output

Next decision cycle

The Complete Flow

Frame Q → Options 𝒜 → Map (U,D) → Score r(a) → Weights w → Floor ⊘ → S_u, R(a) → Select a* → Execute → Observe → Update ↺

Core Equation

\(a^* = \arg\max_{a \in \mathcal{F}} \sum_{u} w_U(u) \sum_{d} w_D(d) \cdot r_{u,d}(a)\)

Floor Constraint

\(\mathcal{F} = \{a : \min_{u,d} \text{Floor}_{u,d}(a) = 1\}\)

Learning Rule

\(r'(a) = \text{Update}(r(a) \mid O)\) — Ethics fixed, estimation improves

Win-Win Target

\(\forall u: S_u(a^*) \geq 0\) — Redesign until no union loses

The 2-Minute Daily Run

Minimum viable practice. Run this loop once per day. Consistency beats intensity.

Notice

One meaningful decision from the last 24 hours. What was your state?

Unions

Name the top 3 unions affected. Who benefited? Who paid?

Dimensions

Name the top 2 dimensions affected. Health? Agency? Social?

Floor Check

Any rights-floor risk? Coercion, manipulation, dignity violation?

Ripple Direction

Net positive, neutral, or negative? Where did the ripples land?

Redesign

One upstream change that would have improved the outcome?

Clean Action

One small action today that improves alignment. Do it.

Reflect

One lesson learned. What would you adjust next time?