Research
Evaluating how evidence structure affects decision quality
DATOM represents claims about how evidence should be represented and used in high-stakes environments. These claims are not self-evident; they must be tested against empirical results. This page documents our methodology, the questions we are currently testing, and the areas where our assumptions have failed or remain unproven.
Explicit Research Questions
Q1
Under what conditions does the aggregation of replication outcomes reduce decision error compared to narrative synthesis?
Q2
How should the presence of unresolved contradictory evidence materially affect quantified action thresholds?
Q3
At what point of structural density does additional evidence cease to meaningfully reduce operational risk?
Q4
Can a structured representation of "what was known when" successfully mitigate hindsight bias in institutional accountability audits?
Methodological Stance
We treat evidence as atomic. This means decomposing research into discrete units—Datoms—that capture population, measure, setting, and result. Rather than collapsing findings into a single summary statistic, we preserve the directional structure of evidence so that agreement and conflict remain visible.
Negative Results
Failures to replicate are treated as high-resolution data that narrow the boundaries of a claim, rather than as noise.
Disagreement
We preserve contradictory findings within the knowledge graph. Consensus is not a goal; legibility is.
Provisional Confidence
Confidence scores are time-dependent and update as the graph evolves. They reflect structural stability, not absolute truth.
Active Research Threads
Quantified Confidence as Structural Density
Impact: Determining if decision-makers can trust a score derived purely from evidence geometry.
Evidence to date: Initial trials using the Remote Work Datomer show that structural confidence scores correctly identified volatility in coordination-heavy tasks.
Falsification: This assessment would change if a claim with high structural density consistently resulted in unforeseen operational failure.
Next-Best Evidence (NBE) Optimization
Impact: Identifying which specific experiment would resolve the most uncertainty for the lowest cost.
Evidence to date: Proof-of-concept modeling indicates that targeting "structural gaps" in the graph provides higher information gain than repeating established measures.
Falsification: This would change if experimental results in NBE-targeted areas failed to update the global confidence score.
Uncertainty is unavoidable. Decisions under uncertainty are hard. DATOM exists to make that difficulty explicit rather than hide it.
Work with us.
Whether you are a lab evaluating your own claims or a fund assessing a portfolio company, DATOM is designed for the decisions that require defensibility.