Relative advantage maximisation as a model of context dependence for binary choice data

Abstract

In recent years we have seen a burgeoning literature, within a discrete choice modelling setting, focussing on capturing increased behavioural realism in decision making through the use of alternative specifications of the utility function. One notable example is the increasing popularity of the random regret minimisation (RRM) model, in which respondents are assumed to choose the alternative that minimises the negative emotions associated with decision making.In this paper, we discuss a ‘relative advantage maximisation’ (RAM) model, which shares some similar characteristics to the RRM model in terms of how contextual effects in the choice set are accounted for. The key feature of the RAM model is the relative advantage component which frames the comparison of an attribute against its counterparts in all other alternatives in the choice set as either an advantage or a disadvantage. The relative advantage is so named because of the assumption that it is the advantage of an alternative relative to the sum of its advantage and disadvantage that matters in the utility function.Although the RRM model has been successfully applied to choice sets with three or more alternatives, with binary choice data, one significant limitation to its scope is that the RRM model collapses to the standard RUM model. This is not the case with the RAM model, and so the notion of context dependency is still meaningfully preserved under RAM. Using the MNL model, we find that the RAM model performs no worse than the RUM model in all six binary choice datasets analysed. In some cases, a small but significant improvement in model fit is observed. However, more advanced models such as the error components and random parameters logit present a more empirically mixed picture for the RAM model. Marginal willingness to pay (WTP) measures derived from the RAM model are also discussed. Differences may be negligible in the MNL case, but not necessarily so for the random parameter logit.