Question 1

What is R64N research trying to establish?

Accepted Answer

R64N research asks whether collective human decision-making can be measured continuously and expressed in a form machines can read. Rather than modelling why people choose, it builds an empirical record of how populations actually resolve trade-offs over time, so that record can serve as a reference for evaluating automated systems.

Question 2

Why does an AI-readable record of human decisions matter now?

Accepted Answer

Autonomous and agentic systems increasingly make consequential choices without a stable empirical reference for how people resolve the same trade-offs. R64N provides that reference: a continuously updated, de-identified description of observed human decisions that AI outputs can be compared against, conditioned on time and country.

Question 3

Where can R64N research be applied beyond AI evaluation?

Accepted Answer

The same observational record supports computational social science, behavioural and experimental economics, game-theoretic and multi-agent research, public policy analysis, and longitudinal study of how decision patterns shift under changing economic, social, and environmental conditions.

Question 4

What is the Resonant Network?

Accepted Answer

Resonant is a continuously operating observatory of collective human decision-making. Participants voluntarily complete structured trade-off decisions over time, and each completed scenario produces a decision event that feeds a de-identified, population-level representation of how people resolve comparable trade-offs.

Question 5

What exactly is a decision event?

Accepted Answer

A decision event is the fundamental unit of observation, represented as e = (s, r, t, c): the presented scenario, the participant's selected response, the observation timestamp, and de-identified contextual metadata comprising country-level location only. Events carry no interpretation of intent or cognition.

Question 6

How is this different from a survey or poll?

Accepted Answer

Surveys periodically measure beliefs, attitudes, and opinions. Resonant continuously observes structured trade-off decisions across many domains, distributed through time rather than completed in a single session, so decisions can be observed under changing personal, social, economic, and environmental conditions.

Question 7

How are decisions represented mathematically?

Accepted Answer

Everyday trade-offs map onto a predefined six-dimensional coordinate system of mutually orthogonal decision dimensions, giving an auditable common basis. Complex domains such as game-theoretic experiments, behavioural ethics, and multi-agent interactions are not forced into that basis; they are analysed with multidimensional item response theory and topological data analysis, producing an evolving decision topology G = (V, E).

Question 8

What is Human Decision Intelligence?

Accepted Answer

HDI is the continuously evolving mathematical description of collective human decisions: centroid, covariance structure, density field, and evolving topology, all expressed as de-identified population-level representations. It is a measurement framework, not a predictive model or theory of human cognition.

Question 9

How does Resonant verify AI decisions?

Accepted Answer

Verification is a retrieval and comparison problem. Given a decision context, the framework retrieves contextually proximate scenarios and compares the AI-generated decision against the observed population distribution, conditioned on time and country. Assessment is probabilistic, not binary: divergence from observed norms is not an error but establishes a burden of explanation.

Question 10

Is participant identity protected?

Accepted Answer

Yes. Contextual metadata is deliberately minimal — country-level location and observation time only; no age, gender, or other demographic attributes are collected. All scientific outputs are de-identified aggregates, and identifiable participant trajectories are never presented as results.

Question 11

What do participants get in return?

Accepted Answer

Each participant builds a longitudinal personal decision profile from their own decision history. It stays under their control and can be used for personal reflection, compared against aggregate population structures, or integrated with external AI and decision-support systems.

Question 12

What are the applications?

Accepted Answer

The primary anticipated application is an empirical verification layer for artificial intelligence: a continuously updated reference describing how human populations resolve comparable trade-offs. Further applications span computational social science, behavioural economics, and public policy.

Question 13

How do I get access?

Accepted Answer

Request access via the link in the navigation. We onboard partners in waves based on use case fit and operational readiness.

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