DTM.3:4.2 - Convert the state into a transmission contribution

Determine the quantity delivered to the next model. Examples include demonstrations per period that meet a performance criterion, an uptake probability conditional on readiness, the composition of reconstructed variants, or the cost of exchange with an adopter.

Preserve the event selected in DTM.2. If each encounter is eligible for a repeatable success, the expected success count is the sum of its event probabilities. With n comparable encounters and common probability p, that count is np; this expectation does not require independence.

First uptake by a recipient is different: after it occurs, later encounters cannot create that recipient’s first uptake again. For n independent comparable opportunities before such uptake, its probability is 1−(1−p)^n. More generally, let p_k be the chance of first uptake at opportunity k conditional on no earlier uptake and the stated history. Compose those conditional chances along that history; if histories vary, also account for their probabilities. Alternatively, update the eligible recipient set as events occur.

Aggregate first-uptake probabilities over distinct eligible recipients, not encounters. For a continuous-time model, sum their hazards to obtain the population flow as in DTM.2. This preserves the quantity passed to DTM.4.

Use DTM.2’s conditional factors and resulting-variant rule where needed. Do not multiply a capability score by a contact count without explaining how that score changes the selected event probability or rate.

Then name the observation that could establish or challenge the proposed state-to-event relation. If two mechanisms predict different outcomes for the same state, retain both until the receiving decision warrants discrimination by MMP.16.