A Brief History of DCEs and Several Important Challenge
Seminar Room 1, Newton Institute
A confrontation with reality led to integration of conjoint measurement, discrete multivariate analysis of contingency tables, random utility theory and discrete choice models and design of statistical experiments. Few seem to realise that discrete choice experiments (DCEs) are in fact sparse, incomplete contingency tables. Thus, much of that literature informs and assists design and analysis of DCEs, such that often complex statistical models are largely unnecessary. Many lack this perspective, and hence much of the literature is dominated by model-driven views of the design and analysis of DCEs.
The transition from the first DCEs to the present was very incremental and haphazard, with many advances being driven by market confrontations. For example "availability" designs arose from being asked to solve problems with out-of-stock conditions, infrastructure interruptions (eg, road or bridge closures), etc. Progress became more rapid and systematic from the late 1990s onwards, particularly with researchers skilled in optimal design theory getting involved in the field. Thus, there have been major strides in the optimal design of DCEs, but there now seems to be growing awareness that experiments on humans pose interesting issues for "optimal" design, particularly designs that seek to optimise statistical efficiency.
Along the way we stumbled onto individuals, error variance differences, cognitive process differences and we're still stumbling.
This talk is about a journey that starts in 1927 with paired comparisons, travels along an ad hoc path until it runs into an airline in 1978, emerges five years later as a systematic way to design and implement multiple comparisons, and slowly wanders back and forth until it begins to pick up speed and follow a "more optimal" path. Where is it going? Well, one researcher's optimum, may well be one human's suboptimum. Where should it be going? The road ahead is littered with overconfidence and assumptions. A better path is to invest in insurance against ignorance and assumptions.
If it doesn't, something may have gone wrong with our embedded player.
We'll get it fixed as soon as possible.