Taxonomy Based Models for Reasoning: Making Inferences from Electronic Road Sign Information

Taxonomy Based modeling was applied to describe drivers’ mental models of variable message signs (VMS’s) displayed on expressways. Progress in road telematics has made it possible to introduce variable message signs (VMS’s). Sensors embedded in the carriageway every 500m record certain variables (speed, flow rate, etc.) that are transformed in real time into “driving times” to a given destination if road conditions do not change. VMS systems are auto-regulative Man-Machine (AMMI) systems which incorporate a model of the user: if the traffic flow is too high, then drivers should choose alternative routes. In so doing, the traffic flow should decrease. The model of the user is based on suppositions such as: people do not like to waste time, they fully understand the displayed messages, they trust the displayed values, they know of alternative routes. However, people also have a model of the way the system functions. And if they do not believe the contents of the message, they will not act as expected. We collected data through interviews with drivers using the critical incidents technique (Flanagan, 1985). Results show that the mental models that drivers have of the way the VMS system works are various but not numerous and that most of them differ from the“ideal expert” mental model. It is clear that users don’t have an adequate model of how the VMS system works and that VMS planners have a model of user behaviour that does not correspond to the behaviour of the drivers we interviewed. Finally, Taxonomy Based Modeling is discussed as a tool for mental model remediation.     

El Niño/Southern Oscillation and tropical Pacific climate during the last millennium

Any assessment of future climate change requires knowledge of the full range of natural variability in the El Ni?o/Southern Oscillation (ENSO) phenomenon. Here we splice together fossil-coral oxygen isotopic records from Palmyra Island in the tropical Pacific Ocean to provide 30-150-year windows of tropical Pacific climate variability within the last 1,100 years. The records indicate mean climate conditions in the central tropical Pacific ranging from relatively cool and dry during the tenth century to increasingly warmer and wetter climate in the twentieth century. But the corals also document a broad range of ENSO behaviour that correlates poorly with these estimates of mean climate. The most intense ENSO activity within the reconstruction occurred during the mid-seventeenth century. Taken together, the coral data imply that the majority of ENSO variability over the last millennium may have arisen from dynamics internal to the ENSO system itself.     

The forecasting attributes of trend- and difference-stationary representations for macroeconomic time series

We analyse the forecasting attributes of trenc and diffence-stationary representations of the U.S. macroeconomic time series sudied by Nelson and Plosser (1982). Predictive densities based on models estimated for these series (which terminate in 1970) are compared with subsequent realizations compiled by Schotman and van Dijk (1991) which terminate in (1988). Predictive densities obtained using the, extended series are also derived to assess the impact of the subsequent realization on long-range forecasts. Of particular interest are comparisons of the average intervals of predictive densities corresponding to the competing specifications In general, we find that coverage intervals based on diference-stationary specifications are far wider than those based or. trend-stationary specifications for the real series, and slightly wider for the nominal series. This additional width is often a virtue in forecasting nuninal series over the 1971-1988 period, as the inflation experienced durnig this time was unprecedented in the 1900s. However, the evolution of the real series has been relatively stable in the 1900s, hence the uncertainty associated with difference-stationary specifications generally seems excessive for these data.