In one sentence
Decision Intelligence evaluates the decision process behind a trade rather than attempting to predict the market.
In practice
- 1.Evaluate the decision behind a trade
- 2.Examine relevant decision factors
- 3.Provide analytical context
- 4.Support structured review
- 5.Record the resulting decision history
What it is
Decision science meets self-directed trading.
Decision intelligence is the applied edge of behavioural economics and decision science — used across finance, aviation and medicine to improve high-stakes calls.
EI ALGOS applies that same lens to self-directed trading. Instead of asking “what is the market going to do?” — a question no one can answer honestly — we ask “how well-aligned is this decision, given what we can measure right now?”
The answer is a score, a reasoning trail, and a coach that lives inside your workflow. You still make every call. We just make sure the mirror is clean.
Three principles
Read the context
Every trade lives inside a regime, a session, a book. Decision intelligence surfaces the context first — then the decision.
Score, don't signal
A score describes the *quality* of a decision without pretending to know the outcome. Signals promise. Scores calibrate.
Coach the review
The best time to improve your trading is *after* the trade — while the details are fresh. Decision intelligence structures that review.
In practice
Where decision intelligence lives across EI ALGOS.
Decision science reading list
Continue the thread across the cluster.
Better trading decisions →
Decision Score + LIANA coaching, applied per trade.
Trading decision framework →
The six-factor Decision Alignment framework.
Decision quality →
Why quality is the leading signal and outcome is the trailing one.
Trade evaluation →
The evaluation framework across every trade you take.
Decision science for traders →
The academic thread — bridged to product surfaces.
Related concepts
Behavioral Decision Intelligence →
A decision-support framework that combines behavioral finance and decision intelligence to make trading decision quality measurable — before, during, and after a trade.
Behavioral Intelligence →
Analysis of observable trading behavior derived from a trader’s own trading history — cadence, plan deviation, position-size drift, and post-loss patterns.
Adaptive Intelligence →
Machine-learning personalization that adapts analytical context to a trader’s historical behavior and learns from subsequent trade outcomes.
Further reading
Articles from the EI ALGOS research library
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