Additive and Multiplicative Uncertainty Revisited: What Explains the Contrasting Biases?

above the certainty level while for additive uncertainty the price should be lower than the certainty level. This note gives an intuitive explanation for the result after first presenting a parsimonious review of the two models. We also discuss which, if either, of the two models is more realistic. Received December 14, 2001; revised version received July 16, 2002 Published online: April 30, 2003 We thank referees for helpful comments. Ciaran Driver would like to acknowledge the research facilities from ANU, Canberra for their help in writing this paper.     

iTV: A major opportunity for financial services,or not?

Interactive television has arrived, thanks to substantial technology investments that have been made by Sky and others. It is growing in importance as innovative interactive applications are developed for broadcast programmes (eg the BBCi service and Big Brother), and for advertising — and will develop further when broadband deployment gets to critical mass. Readers should not expect a television-variant of the world wide web, however. This was tried during 2000–2002 in the form of web look-alike, interactive ‘walled garden’ sites, and failed to attract critical mass footfall. The model has evolved and now revolves around interactive content synchronised with broadcast, which offers financial services companies an interesting new way to increase awareness and to differentiate the service presentation. Furthermore, over the next ten years, broadband is expected to usher in a new era of personalised television — in which specialised broadcasts (such as personal pensions advice programmes) can be made available on demand. The observations in this paper are mainly drawn from the UK, where digital television penetration is high; the lessons can be applied more broadly, however, and are relevant to any organisation thinking of promoting its service through entertainment channels.     

Another view on optimal design for estimating the point of extremum in quadratic regression

In this paper we illustrate how certain design problems can be simplified by reparametrization of the response function. This alternative viewpoint provides further insights than the more traditional approaches, like minimax, Bayesian or sequential techniques. It will also improve a practitioner’s understanding of more general situations and their “classical” treatment.