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Argument Scores from Sub-Argument Scores

"A conclusion is the place where you got tired of thinking." — Steven Wright

It doesn't have to be. With the right infrastructure, analysis can continue while you sleep. The problem isn't that people refuse to think. It's that we keep starting over — relitigating the same arguments every election cycle because there's no shared structure connecting arguments to sub-arguments to evidence to conclusions. This page explains the core mechanic that makes a permanent, cumulative reasoning structure possible.

The Tree Structure

Arguments in the ISE are organized as a tree, not a list. At the root sits the conclusion being evaluated. Branching down from it are the arguments for and against. Each of those arguments has its own branches — the sub-arguments that support or undermine it — and those sub-arguments have branches too, all the way down to raw evidence.

Every node in this tree is itself a belief with its own page. "Roundabouts reduce fatal accidents" isn't just a bullet point under "We should build roundabouts." It's a full claim with its own supporting evidence, its own opposing arguments, and its own score. That score feeds upward into its parent. The rebuttal to any bad argument is permanently attached to the claim it refutes — always one link away, never buried in a comment thread nobody reads anymore.

Technically the structure is a Directed Acyclic Graph (DAG): directed because scores flow upward from evidence to conclusions, and acyclic because the system prevents circular reasoning. An argument cannot be its own ancestor. Navigation between pages is bidirectional, but scoring always flows one way: up.

The Three Factors That Determine an Argument's Influence

Each sub-argument influences its parent through three independent scores, and all three must be strong for a sub-argument to significantly move a conclusion. A claim can be true but irrelevant, relevant but weakly supported, or important but poorly linked to the conclusion it's supposed to prove. The scoring system captures these distinctions explicitly.

The Contribution Formula

Each sub-argument's contribution to its parent is:

Contribution = Truth Score × Linkage Score × Importance Weight × Independence Score

Add up all the positive contributions, subtract all the negative ones, and you have the parent argument's net score. That score becomes the Truth Score input for the argument above it, repeating all the way up to the conclusion. Weak arguments automatically fade. Strong arguments dominate. And changing evidence anywhere in the tree automatically updates every conclusion above it.

Algorithm summary:

1. Evidence determines Truth Scores at the leaf nodes.
2. Truth Scores combine with Linkage, Importance, and Independence to produce Argument Scores.
3. Argument Scores combine to produce Conclusion Scores.
4. Any change propagates upward through the network automatically.

Some arguments only activate when a prerequisite belief is sufficiently established. For example, "we must regulate AI autonomous weapons" only matters if AI can actually deploy autonomous weapons. These conditional dependencies add a fifth multiplier — the Truth Score of the prerequisite belief — ensuring that argument chains built on uncertain foundations are automatically discounted rather than treated as fully active.

A Worked Example: Should We Replace Stop Signs with Roundabouts?

The claim: "We should replace stop signs with roundabouts." Here's how the four-factor formula plays out across its main sub-arguments:

The construction delays row is 100% true — but because its linkage and importance are both near zero, it contributes almost nothing. This is the key insight: being technically correct about an irrelevant detail is not the same as making a good argument. The ISE system makes that distinction explicit rather than leaving it to whoever shouts loudest.

Now notice that "Reduces fatal accidents by 37%" has a Truth Score of 80%, not 100%. That 80% is the score of that claim's own belief page, calculated from the traffic studies supporting it and any methodological criticisms opposing it. If new research weakens the fatality reduction estimate and drops that Truth Score to 40%, the net score automatically recalculates to approximately +37.75%, and that change ripples upward through every higher-level conclusion that depends on roundabout safety. The analysis keeps running. The scores keep updating. The strongest arguments keep rising.

Why Traditional Debates Fail at This

Preventing Manipulation

Any scoring system can be gamed if it's not designed carefully. The ISE uses four interlocking defenses:

1. Semantic clustering groups similar arguments so the same claim can't be counted multiple times under different wordings. "Roundabouts reduce crashes" and "Roundabouts improve safety" get merged into one node, not two. You can't inflate a score by restating the same point fifty different ways.

2. Independence Scores catch a subtler version of the same problem. Four arguments built on the same underlying dataset aren't four independent reasons — they're one reason wearing four hats. Independence weighting scales their combined contribution down to reflect the actual evidentiary base, not the number of phrasings an organized group could generate.

3. Linkage Scores are evaluated independently from Truth Scores. A true claim still has to prove it's relevant. Someone can't boost a conclusion by piling on true-but-irrelevant sub-arguments — the low linkage score neutralizes the contribution regardless of how well-evidenced the claim is.

4. Community and expert review can challenge any score at any level of the tree. Flag a weak linkage, identify a missing counterargument, submit new evidence — the whole network recalculates from that node upward. Every revision is traceable.

Most importantly: no score in the Idea Stock Exchange is authoritative. Every score is the output of an argument that can itself be challenged. Clicking any number shows the sub-arguments and evidence behind it. There are no black boxes. If you think a score is wrong, the correct response is to add the counterargument and the evidence. The system doesn't suppress disagreement — it structures it. Every challenge strengthens the reasoning network instead of derailing it.

Frequently Asked Questions

Q: Isn't this too complicated for most people to use?

The math is elementary school arithmetic: count the pros, count the cons, subtract. The ISE automates the aggregation so users never have to juggle all the levels simultaneously. You engage at whatever depth you have time for — top-level conclusions, intermediate arguments, or raw evidence — and the system handles the rest. See the full treatment at Frequently Asked Questions.

Q: Who decides the scores?

Nobody and everybody. Scores emerge from the arguments beneath them, not from editorial authority. Community members can challenge any score by adding counterarguments or new evidence. Experts can flag methodological problems. The community collectively assesses linkage and importance. No single person controls the outcome, and every input is traceable.

Q: What stops motivated groups from flooding the system with weak arguments?

Three things simultaneously: semantic clustering prevents duplicate arguments from being counted separately; Independence Scores prevent correlated arguments from multiplying each other's weight; and Linkage Scores ensure that even a thousand true-but-irrelevant claims don't move the conclusion. Volume is not persuasion.

Q: How is this different from a poll or vote?

Votes aggregate preferences. This system aggregates reasons. A poll tells you that 60% of people favor roundabouts. This system tells you that the safety evidence is strong, the traffic flow evidence is moderate, and the elderly confusion concern is real but insufficient to outweigh the fatality reduction — and it shows you exactly where each of those judgments came from. The difference matters when you're trying to understand why a conclusion is warranted, not just that people believe it.

Related Pages

• ReasonRank: the full algorithm
• Linkage Scores: does this argument actually prove the point?
• Truth Scores: is the claim actually true?
• Evidence Scores: how strong is the underlying data?
• Importance Scores: does this factor actually matter?
• Independence Scores: preventing correlated arguments from inflating conclusions
• Conditional Linkage: arguments that only activate when prerequisites are true
• One Page Per Topic: why each argument lives in one place
• Linking Evidence to Conclusions Would Automatically Update Truth
• Media Truth Score
• Topic Overlap Scores
• Objective Criteria Scores
• Linkage Score Code
• Book Logical Validity Score