Our Approach
Resonant Research approaches analysis from the standpoint of decisions rather than datasets.
Rather than beginning with available data and asking what can be modeled, we begin by identifying the decision to be made, the constraints under which it operates, and the consequences of being wrong. Only then do we consider what forms of data—real or synthetic—are appropriate to explore that space.
This orientation reflects a belief that analytical rigor is measured not by model performance in isolation, but by how well decisions hold up when conditions deviate from expectations.
Decisions Before Models
In many analytical workflows, models are treated as the end product. Accuracy metrics, validation scores, and historical fit become proxies for confidence.
Our work treats models as instruments, not answers.
We are primarily interested in:
- How sensitive a decision is to underlying assumptions
- Where performance degrades under stress or uncertainty
- Which variables meaningfully change outcomes, and which do not
- How decisions behave at the margins, not just at the mean
This requires moving beyond retrospective optimization toward deliberate exploration of possibility space.
The Role of Synthetic Data
Synthetic data is used selectively as a means of making decision structure visible.
When historical data is sparse, biased, or dominated by routine cases, synthetic data can be constructed to:
- Explore counterfactual conditions
- Amplify rare but consequential scenarios
- Test decision logic under controlled variation
- Examine boundary conditions and failure modes
In this context, synthetic data is not intended to increase sample size or improve model training. Its value lies in enabling systematic questioning of how decisions respond when reality does not conform to precedent.
Relationship to Existing Practices
Our approach complements established practices such as scenario planning, red teaming, and simulation-based analysis.
Where these methods rely heavily on narrative construction or expert judgment, we seek to add analytical structure without introducing false precision. Where quantitative models dominate, we seek to reintroduce judgment, uncertainty, and consequence.
The objective is not to replace existing methods, but to integrate them into a more disciplined examination of decision behavior.
What We Deliberately Avoid
Resonant Research does not publish step-by-step methodologies, toolchains, or prescriptive frameworks.
The purpose of this work is not replication or deployment, but conceptual clarity. We aim to clarify how decisions should be interrogated, not to provide turnkey solutions.