Most analytical tools are built around what can be easily measured: transactions, clicks, stated preferences, demographic proxies. Mana begins from a different premise. The most consequential patterns in human behaviour are often implicit — shaped by context, social influence, and cognitive tendencies that do not surface cleanly in structured data. Surfacing those patterns requires a different kind of analysis.
Mana's analysis framework draws on Hxly's Library of behavioural datasets and persona models to interpret observed behaviour in light of what is known about how people actually make decisions. Rather than treating behaviour as the output of rational preference, it treats behaviour as the product of a complex interaction between individual disposition, situational context, and social environment. This makes it possible to identify not just what is happening, but why — and what is likely to happen next.
The framework is designed to operate across multiple scales simultaneously. At the individual level, it identifies the decision-making patterns and contextual sensitivities that shape a person's behaviour within a system. At the group level, it surfaces the shared structures and divergences that define how different populations navigate the same environment. At the system level, it maps the feedback loops and tipping points that determine how behaviour in aggregate produces outcomes that no individual intended.
The goal is not to predict what any individual will do. It is to understand the structure of the decision space well enough to anticipate how the system will move.
Analysis outputs are structured to be directly actionable. Each analysis surfaces a ranked set of behavioural drivers — the factors that, if changed, would most significantly shift outcomes — along with confidence intervals that reflect the genuine uncertainty in the model. Decision-makers receive not a single interpretation of the data but a map of the interpretive space, with the most robust conclusions clearly distinguished from those that remain sensitive to assumptions.
Current analysis deployments span consumer financial behaviour, public health service uptake, and workforce decision-making. In each context, the common finding is that the most important drivers of behaviour are not the ones that conventional analysis identifies first — and that understanding them changes not just what interventions are chosen, but where and how they are applied.