When I wrote my first book, Qualitative Choice Analysis, in the mid1980s, the field had reached a critical juncture. The breakthrough concepts that defined the field had been made. The basic models – mainly logit and nested logit – had been introduced, and the statistical and economic properties of these models had been derived. Applications had proven successful in many different areas, including transportation, energy, housing, and marketing – to name only a few. The field is at a similar juncture today for a new generation of procedures. The first-generation models contained important limitations that inhibited their applicability and realism | P1 GEM IKJ P2 GEM IKJ QC GEM ABE T1 GEM September 18 2002 11 2 Char Count 0 CB495-01Drv CB495 Train KEY BOARDED 1 Introduction Motivation When I wrote my first book Qualitative Choice Analysis in the mid-1980s the field had reached a critical juncture. The breakthrough concepts that defined the field had been made. The basic models - mainly logit and nested logit - had been introduced and the statistical and economic properties of these models had been derived. Applications had proven successful in many different areas including transportation energy housing and marketing - to name only a few. The field is at a similar juncture today for a new generation of procedures. The first-generation models contained important limitations that inhibited their applicability and realism. These limitations were well recognized at the time but ways to overcome them had not yet been discovered. Over the past twenty years tremendous progress has been made leading to what can only be called a sea change in the approach and methods of choice analysis. The early models have now been supplemented by a variety of more powerful and more flexible methods. The new concepts have arisen gradually with researchers building on the work of others. However in a sense the change has been more like a quantum leap than a gradual progression. The way that researchers think about specify and estimate their models has changed. Importantly a kind of consensus or understanding seems to have emerged about the new methodology. Among researchers working in the field a definite sense of purpose and progress prevails. My purpose in writing this new book is to bring these ideas together in a form that exemplifies the unity of approach that I feel has emerged and in a format that makes the methods accessible to a wide audience. The advances have mostly centered on simulation. Essentially simulation is the researcher s response to the inability of computers to perform integration. Stated more precisely .
