5 What Ifs
Forward-chain hypothesising from observations in topic journals — mundane or extraordinary. For each observation, build a chain of 5 “what if” steps toward an implication, then check whether independent chains converge or diverge.
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- Signals/
Signals
Signals doesn’t gather anything new — it re-reads Topics’ material through analytical lenses that Topics’ own topic-by-topic framing can’t see. Internally this is “Column B”: the deliberate corrective to Column A’s acknowledged confirmation bias.
Three approaches run here. Symptom Catalogue collects concrete instances of disruption or surprise without explaining them, then periodically asks what structural hypothesis connects them. Five What-Ifs takes an observation — mundane or extraordinary — and hypothesises forward five steps, checking whether the resulting chains converge or diverge. Causal Chains looks for direct cause-and-effect relationships across different topic journals — where a development tracked in one is the structural cause of an effect tracked in another.
Where Topics is bound by its own topic framing, Signals isn’t — its purpose is to surface patterns that only become visible once you stop looking at one topic at a time.
Causal Chains
Cross-journal causal relationships — where a development tracked in one topic journal is the direct or structural cause of an observation in another. Symptom-catalogue collects observations; five-what-ifs hypothesises forward; causal-chains looks backward to identify what is causing what we observe across the topic system.
Symptom Catalogue
Concrete instances of disruption, surprise, or anomaly extracted from topic journals — collected without explanation. Periodic synthesis passes ask: what structural hypothesis would connect these?