Demon: Decision Mapping Via Optimum Go-No Networks—A Model for Marketing New Products

TLDR: Although it is related to previous work in chance constrained programming, DEMON evidently also effects a further development and extension of these ideas by reference to the fact that here the statistical distributions are only partially known and the chance constraints are also expressed in terms of conditional distributions which in turn may be altered by the choices that are made.

Abstract: In this paper a dynamic, adaptive model, called DEMON,4 is interpreted in terms of a network. The latter is here employed to reduce the problem of selecting optimal decision procedures so that these can be interpreted in terms of a conditional sequential designation of links from such a network. More is involved, however, than is immediately apparent from the network possibilities only. Thus, in the DEMON applications to new product marketing—as discussed in the present paper—it is necessary to comprehend additional chance and deterministic constraints such as (1) payback, or breakeven, conditions that may be specified for fulfillment over a given time horizon, (2) minimum expected level of profits required and (3) study budget limits which should not be exceeded. By means of preemptions or over-rides, which also form a part of DEMON, however, it is possible to relate these additional constraints to the network as we also show by means of a development via ideas associated with the right inverse of an inc...