Do ‘big losses’ in judgmental adjustments to statistical forecasts affect experts’ behaviour?

The behaviour of poker players and sports gamblers has been shown to change after winning or losing a significant amount of money on a single hand. In this paper, we explore whether there are changes in experts’ behaviour when performing judgmental adjustments to statistical forecasts and, in particular, examine the impact of ‘big losses’. We define a big loss as a judgmental adjustment that significantly decreases the forecasting accuracy compared to the baseline statistical forecast. In essence, big losses are directly linked with wrong direction or highly overshooting judgmental overrides. Using relevant behavioural theories, we empirically examine the effect of such big losses on subsequent judgmental adjustments exploiting a large multinational data set containing statistical forecasts of demand for pharmaceutical products, expert adjustments and actual sales. We then discuss the implications of our findings for the effective design of forecasting support systems, focusing on the aspects of guidance and restrictiveness

Deriving content selection rules from a corpus of non-naturally occurring documents for a novel NLG application

We describe a methodology for deriving content selection rules for NLG applications that aim to replace oral communications from human experts by written communications that are generated automatically. We argue for greater involvement of users and for a strategy for handling sparse data

Towards Collaborative Conceptual Exploration

In domains with high knowledge distribution a natural objective is to create principle foundations for collaborative interactive learning environments. We present a first mathematical characterization of a collaborative learning group, a consortium, based on closure systems of attribute sets and the well-known attribute exploration algorithm from formal concept analysis. To this end, we introduce (weak) local experts for subdomains of a given knowledge domain. These entities are able to refute and potentially accept a given (implicational) query for some closure system that is a restriction of the whole domain. On this we build up a consortial expert and show first insights about the ability of such an expert to answer queries. Furthermore, we depict techniques on how to cope with falsely accepted implications and on combining counterexamples. Using notions from combinatorial design theory we further expand those insights as far as providing first results on the decidability problem if a given consortium is able to explore some target domain. Applications in conceptual knowledge acquisition as well as in collaborative interactive ontology learning are at hand.Comment: 15 pages, 2 figure

Patient safety in dentistry: development of a candidate 'never event' list for primary care

Introduction The 'never event' concept is often used in secondary care and refers to an agreed list of patient safety incidents that 'should not happen if the necessary preventative measures are in place'. Such an intervention may raise awareness of patient safety issues and inform team learning and system improvements in primary care dentistry. Objective To identify and develop a candidate never event list for primary care dentistry. Methods A literature review, eight workshops with dental practitioners and a modified Delphi with 'expert' groups were used to identify and agree candidate never events. Results Two-hundred and fifty dental practitioners suggested 507 never events, reduced to 27 distinct possibilities grouped across seven themes. Most frequently occurring themes were: 'checking medical history and prescribing' (119, 23.5%) and 'infection control and decontamination' (71, 14%). 'Experts' endorsed nine candidate never event statements with one graded as 'extreme risk' (failure to check past medical history) and four as 'high risk' (for example, extracting wrong tooth). Conclusion Consensus on a preliminary list of never events was developed. This is the first known attempt to develop this approach and an important step in determining its value to patient safety. Further work is necessary to develop the utility of this method

Navigating the information landscape

Preprint of column segment to be published in Serials Librarian 61(3), 2011.This article explores the tension between the structures by which the library organises and presents information, and the ways in which students and researchers access, use and conceptualise knowledge. I suggest that while knowledge structures are vital to learning and research, an overemphasis on structurality is mistaken, and can lead to an inappropriately positivist approach which impedes the research mission. The article examines various metaphoric ways of negotiating meaning and navigating information structures, and of crossing the threshold of structuralit