Product life trade-offs: what if products fail early?

Increasing product life allows the embodied emissions in products to be spread across a longer period but can mean that opportunities to improve use-phase efficiency are foregone. In this paper, a model that evaluates this trade-off is presented and used to estimate the optimal product life for a range of metal-intensive products. Two strategies that have potential to save emissions are explored: (1) adding extra embodied emissions to make products more sturdy, increasing product life, and (2) increasing frequency of use, causing early product failure to take advantage of improvements in use-phase efficiency. These strategies are evaluated for two specific case studies (long-life washing machines and more frequent use of vehicles through car clubs) and for a range of embodied and use-phase intensive products under different use-phase improvement rate assumptions. Particular emphasis is placed on the fact that products often fail neither at their design life nor at their optimal life. Policy recommendations are then made regarding the targeting of these strategies according to product characteristics and the timing of typical product failure relative to optimal product life.

Comparing crowding in human and ideal observers.

A visual target is more difficult to recognize when it is surrounded by other, similar objects. This breakdown in object recognition is known as crowding. Despite a long history of experimental work, computational models of crowding are still sparse. Specifically, few studies have examined crowding using an ideal-observer approach. Here, we compare crowding in ideal observers with crowding in humans. We derived an ideal-observer model for target identification under conditions of position and identity uncertainty. Simulations showed that this model reproduces the hallmark of crowding, namely a critical spacing that scales with viewing eccentricity. To examine how well the model fits quantitatively to human data, we performed three experiments. In Experiments 1 and 2, we measured observers' perceptual uncertainty about stimulus positions and identities, respectively, for a target in isolation. In Experiment 3, observers identified a target that was flanked by two distractors. We found that about half of the errors in Experiment 3 could be accounted for by the perceptual uncertainty measured in Experiments 1 and 2. The remainder of the errors could be accounted for by assuming that uncertainty (i.e., the width of internal noise distribution) about stimulus positions and identities depends on flanker proximity. Our results provide a mathematical restatement of the crowding problem and support the hypothesis that crowding behavior is a sign of optimality rather than a perceptual defect.

Rebuilding the limit order book: Sequential Bayesian inference on hidden states

The limit order book of an exchange represents an information store of market participants' future aims and for many traders the information held in this store is of interest. However, information loss occurs between orders being entered into the exchange and limit order book data being sent out. We present an online algorithm which carries out Bayesian inference to replace information lost at the level of the exchange server and apply our proof of concept algorithm to real historical data from some of the world's most liquid futures contracts as traded on CME GLOBEX, EUREX and NYSE Liffe exchanges. © 2013 © 2013 Taylor & Francis.