Allot is ConfluxLab's data-collection study of how people allocate scarce aid when every choice has a cost. We are building an open, cross-cultural record of human moral judgement — the training signal for an allocation-support model the lab will build next, with omniaid.io as the deployment platform. The model is meant to learn from what people judge to be right, not from cold optimisation.
Why
Humanitarian allocation sits between two failure modes. Hand the decision to pure optimisation, stripped of moral judgement, and helping the most vulnerable can be ruled "non-viable" — lives become rounding errors. Leave it to unaided human instinct, and we tend to help whoever is closest, most legible, or most immediately moving, rather than whoever needs help most. Allot is built to study that tension directly, before any system is designed to act in it.
The domain is concrete. Cash-and-voucher assistance accounted for roughly a fifth of all international humanitarian assistance in 2023, and the agencies delivering it face these trade-offs daily: where cash helps and where in-kind works better; how much diversion erodes trust; and whether, in reaching the most visibly suffering, programmes quietly lose the people who do not present well.
Method
Allot decomposes every allocation choice along seven ethical axes. Participants never see these axes during play — they are revealed only in the debrief, to avoid priming the very judgement the study is trying to measure.
- Aggregate welfare — total good produced;
- Equality of outcomes — levelling disparities;
- Equity to need — reaching those in greatest hardship;
- Desert — what is earned;
- Unconditional rights — dignity regardless of return on investment;
- Efficiency — cost-effectiveness;
- Kinship — in-group and family ties.
Beneath the narrative, the study is calibrated rather than improvised. It uses a structured set of beneficiary archetypes, scenarios, and participant roles, with every numeric attribute traced to published field evidence and randomised controlled trials. Scenarios are deliberately free of win-win answers — resources are limited and every choice carries a cost — and each is written with arguments on more than one side, in clinical language without evaluative adjectives, to keep the instrument from steering the participant.
What we are collecting
A session is anonymous and takes roughly forty minutes: a participant works through twenty-seven core dilemmas, each a concrete allocation choice with no costless answer. For every decision we record not just the choice but how it was made — the option taken, the time taken to decide, the participant's stated confidence, and, once decomposed in the debrief, how the choice weighted the seven axes. A mid-session twist changes the conditions, so we also see how people revise under new information rather than only their first instinct.
Each participant is assigned a role that shapes the framing of the scenarios without altering the underlying numbers, which lets us separate the effect of narrative emphasis from the effect of the trade-off itself. The instrument runs in several languages so the record is genuinely cross-cultural, and the aim is a corpus of thousands of participants broad enough to study how moral intuition varies across people and contexts — not a single population's view treated as universal.
From data to a model
ConfluxLab is an AI-research laboratory: it builds models, and Allot is how it sources the training signal for one — responsibly, and in the open. Stage 1, the work described here, collects the cross-cultural moral data. Stage 2 is the model the lab will train on that curated signal: an allocation-support system that learns the trade-offs people actually make, rather than the trade-offs an optimiser would impose. Its purpose is to help humanitarian missions reduce nepotism and bias in distribution, and it will be developed with omniaid.io as the deployment platform and evaluated in the field rather than asserted.
Collecting the data before designing the model is deliberate. A system trained on what people judge to be right is accountable to that judgement; one designed first and justified later is accountable to nothing. That ordering also fixes the model's limits in advance: it is meant to support allocation — never to compute a "minimum necessary" survival level, never to ration knowledge or medicine for institutional convenience, and never to be optimised to reduce anyone's decline.
Openness and pre-registration
To protect the study's validity, the full design specification is withheld during data collection — but its integrity is fixed in advance. A cryptographic fingerprint of the specification was published on 2026-05-11, so the design cannot be quietly changed after results come in. When the first dataset is released, the specification is released with it.
The dataset will be published openly under CC‑BY‑4.0; the code is released under Apache 2.0. This follows the lab's default: methods and data in the open, so that others can check the work — and audit the model trained on it — rather than take either on trust.
Ethics
Participation is designed to be low-risk and self-determined. Identity questions (such as religion, ethnicity, or citizenship) are optional and isolated from the narrative a participant sees — they are used only to measure bias on the server side, never to change what is shown. Collected data carries a defined retention limit, and no "right answer" is ever asserted during play. These choices follow directly from the lab's ethics statement: the study is part of the research design, not a survey bolted onto it.