Sociality and external validity in experimental economics

It is sometimes argued that experimental economists do not have to worry about external validity so long as the design sticks closely to a theoretical model. This position mistakes the model for the theory. As a result, applied economics designs often study phenomena distinct from their stated objects of inquiry. Because the implemented models are abstract, they may provide improbable analogues to their stated subject matter. This problem is exacerbated by the relational character of the social world, which also sets epistemic limits for the social science laboratory more generally.

the implication of social cognition for experimental economics From the issue entitled "Special issue on Experimental economics and the social embedding of economic behavior and cognition"

Can human social cognitive processes and social motives be grasped by the methods of experimental economics? Experimental studies of strategic cognition and social preferences contribute to our understanding of the social aspects of economic decisions making. Yet, papers in this issue argue that the social aspects of decision-making introduce several difficulties for interpreting the results of economic experiments. In particular, the laboratory is itself a social context, and in many respects a rather distinctive one, which raises questions of external validity.

Genome expansion and gene loss in powdery mildew fungi reveal tradeoffs in extreme parasitism

Powdery mildews are phytopathogens whose growth and reproduction are entirely dependent on living plant cells. The molecular basis of this life-style, obligate biotrophy, remains unknown. We present the genome analysis of barley powdery mildew, Blumeria graminis f.sp. hordei (Blumeria), as well as a comparison with the analysis of two powdery mildews pathogenic on dicotyledonous plants. These genomes display massive retrotransposon proliferation, genome-size expansion, and gene losses. The missing genes encode enzymes of primary and secondary metabolism, carbohydrate-active enzymes, and transporters, probably reflecting their redundancy in an exclusively biotrophic life-style. Among the 248 candidate effectors of pathogenesis identified in the Blumeria genome, very few (less than 10) define a core set conserved in all three mildews, suggesting thatmost effectors represent species-specific adaptations.

Refinement of the Clinical Scenario Evaluation Framework for Assessment of Competing Development Strategies with an Application to Multiple Sclerosis

When competing strategies for development programs, clinical trial designs, or data analysis methods exist, the alternatives need to be evaluated in a systematic way to facilitate informed decision making. Here we describe a refinement of the recently proposed clinical scenario evaluation framework for the assessment of competing strategies. The refinement is achieved by subdividing key elements previously proposed into new categories, distinguishing between quantities that can be estimated from preexisting data and those that cannot and between aspects under the control of the decision maker from those that are determined by external constraints. The refined framework is illustrated by an application to a design project for an adaptive seamless design for a clinical trial in progressive multiple sclerosis.

Relating computer tasks to existing knowledge to improve accessibility for older adults

Routine computer tasks are often difficult for older adult computer users to learn and remember. People tend to learn new tasks by relating new concepts to existing knowledge. However, even for 'basic' computer tasks there is little, if any, existing knowledge on which older adults can base their learning. This paper investigates a custom file management interface that was designed to aid discovery and learnability by providing interface objects that are familiar to the user. A study was conducted which examined the differences between older and younger computer users when undertaking routine file management tasks using the standard Windows desktop as compared with the custom interface. Results showed that older adult computer users requested help more than ten times as often as younger users when using a standard windows/mouse configuration, made more mistakes and also required significantly more confirmations than younger users. The custom interface showed improvements over standard Windows/mouse, with fewer confirmations and less help being required. Hence, there is potential for an interface that closely mimics the real world to improve computer accessibility for older adults, aiding self-discovery and learnability.

The impact of finer-resolution air-sea coupling on the intraseasonal oscillation of the Indian monsoon

The new HadKPP atmosphere–ocean coupled model is described and then used to determine the effects of sub-daily air–sea coupling and fine near-surface ocean vertical resolution on the representation of the Northern Hemisphere summer intra-seasonal oscillation. HadKPP comprises the Hadley Centre atmospheric model coupled to the K Profile Parameterization ocean-boundary-layer model. Four 30-member ensembles were performed that varied in oceanic vertical resolution between 1 m and 10 m and in coupling frequency between 3 h and 24 h. The 10 m, 24 h ensemble exhibited roughly 60% of the observed 30–50 day variability in sea-surface temperatures and rainfall and very weak northward propagation. Enhancing either only the vertical resolution or only the coupling frequency produced modest improvements in variability and only a standing intra-seasonal oscillation. Only the 1 m, 3 h configuration generated organized, northward-propagating convection similar to observations. Sub-daily surface forcing produced stronger upper-ocean temperature anomalies in quadrature with anomalous convection, which likely affected lower-atmospheric stability ahead of the convection, causing propagation. Well-resolved air–sea coupling did not improve the eastward propagation of the boreal summer intra-seasonal oscillation in this model. Upper-ocean vertical mixing and diurnal variability in coupled models must be improved to accurately resolve and simulate tropical sub-seasonal variability. In HadKPP, the mere presence of air–sea coupling was not sufficient to generate an intra-seasonal oscillation resembling observations.

Interpretation of infant facial expression in the context of maternal postnatal depression

Postnatal maternal depression is associated with difficulties in maternal responsiveness. As most signals arising from the infant come from facial expressions one possible explanation for these difficulties is that mothers with postnatal depression are differentially affected by particular infant facial expressions. Thus, this study investigates the effects of postnatal depression on mothers’ perceptions of infant facial expressions. Participants (15 controls, 15 depressed and 15 anxious mothers) were asked to rate a number of infant facial expressions, ranging from very positive to very negative. Each face was shown twice, for a short and for a longer period of time in random order. Results revealed that mothers used more extreme ratings when shown the infant faces (i.e. more negative or more positive) for a longer period of time. Mothers suffering from postnatal depression were more likely to rate negative infant faces shown for a longer period more negatively than controls. The differences were specific to depression rather than an effect of general postnatal psychopathology—as no differences were observed between anxious mothers and controls. There were no other significant differences in maternal ratings of infant faces showed for short periods or for positive or neutral valence faces of either length. The findings that mothers with postnatal depression rate negative infant faces more negatively indicate that appraisal bias might underlie some of the difficulties that these mothers have in responding to their own infants signals.

Proof of concept of automated mapping of weeds in arable fields

The project investigated whether it would be possible to remove the main technical hindrance to precision application of herbicides to arable crops in the UK, namely creating geo-referenced weed maps for each field. The ultimate goal is an information system so that agronomists and farmers can plan precision weed control and create spraying maps. The project focussed on black-grass in wheat, but research was also carried out on barley and beans and on wild-oats, barren brome, rye-grass, cleavers and thistles which form stable patches in arable fields. Farmers may also make special efforts to control them. Using cameras mounted on farm machinery, the project explored the feasibility of automating the process of mapping black-grass in fields. Geo-referenced images were captured from June to December 2009, using sprayers, a tractor, combine harvesters and on foot. Cameras were mounted on the sprayer boom, on windows or on top of tractor and combine cabs and images were captured with a range of vibration levels and at speeds up to 20 km h-1. For acceptability to farmers, it was important that every image containing black-grass was classified as containing black-grass; false negatives are highly undesirable. The software algorithms recorded no false negatives in sample images analysed to date, although some black-grass heads were unclassified and there were also false positives. The density of black-grass heads per unit area estimated by machine vision increased as a linear function of the actual density with a mean detection rate of 47% of black-grass heads in sample images at T3 within a density range of 13 to 1230 heads m-2. A final part of the project was to create geo-referenced weed maps using software written in previous HGCA-funded projects and two examples show that geo-location by machine vision compares well with manually-mapped weed patches. The consortium therefore demonstrated for the first time the feasibility of using a GPS-linked computer-controlled camera system mounted on farm machinery (tractor, sprayer or combine) to geo-reference black-grass in winter wheat between black-grass head emergence and seed shedding.

Final report on research project PS2010, proof of concept of automated mapping of weeds in arable fields

Many weeds occur in patches but farmers frequently spray whole fields to control the weeds in these patches. Given a geo-referenced weed map, technology exists to confine spraying to these patches. Adoption of patch spraying by arable farmers has, however, been negligible partly due to the difficulty of constructing weed maps. Building on previous DEFRA and HGCA projects, this proposal aims to develop and evaluate a machine vision system to automate the weed mapping process. The project thereby addresses the principal technical stumbling block to widespread adoption of site specific weed management (SSWM). The accuracy of weed identification by machine vision based on a single field survey may be inadequate to create herbicide application maps. We therefore propose to test the hypothesis that sufficiently accurate weed maps can be constructed by integrating information from geo-referenced images captured automatically at different times of the year during normal field activities. Accuracy of identification will also be increased by utilising a priori knowledge of weeds present in fields. To prove this concept, images will be captured from arable fields on two farms and processed offline to identify and map the weeds, focussing especially on black-grass, wild oats, barren brome, couch grass and cleavers. As advocated by Lutman et al. (2002), the approach uncouples the weed mapping and treatment processes and builds on the observation that patches of these weeds are quite stable in arable fields. There are three main aspects to the project. 1) Machine vision hardware. Hardware component parts of the system are one or more cameras connected to a single board computer (Concurrent Solutions LLC) and interfaced with an accurate Global Positioning System (GPS) supplied by Patchwork Technology. The camera(s) will take separate measurements for each of the three primary colours of visible light (red, green and blue) in each pixel. The basic proof of concept can be achieved in principle using a single camera system, but in practice systems with more than one camera may need to be installed so that larger fractions of each field can be photographed. Hardware will be reviewed regularly during the project in response to feedback from other work packages and updated as required. 2) Image capture and weed identification software. The machine vision system will be attached to toolbars of farm machinery so that images can be collected during different field operations. Images will be captured at different ground speeds, in different directions and at different crop growth stages as well as in different crop backgrounds. Having captured geo-referenced images in the field, image analysis software will be developed to identify weed species by Murray State and Reading Universities with advice from The Arable Group. A wide range of pattern recognition and in particular Bayesian Networks will be used to advance the state of the art in machine vision-based weed identification and mapping. Weed identification algorithms used by others are inadequate for this project as we intend to collect and correlate images collected at different growth stages. Plants grown for this purpose by Herbiseed will be used in the first instance. In addition, our image capture and analysis system will include plant characteristics such as leaf shape, size, vein structure, colour and textural pattern, some of which are not detectable by other machine vision systems or are omitted by their algorithms. Using such a list of features observable using our machine vision system, we will determine those that can be used to distinguish weed species of interest. 3) Weed mapping. Geo-referenced maps of weeds in arable fields (Reading University and Syngenta) will be produced with advice from The Arable Group and Patchwork Technology. Natural infestations will be mapped in the fields but we will also introduce specimen plants in pots to facilitate more rigorous system evaluation and testing. Manual weed maps of the same fields will be generated by Reading University, Syngenta and Peter Lutman so that the accuracy of automated mapping can be assessed. The principal hypothesis and concept to be tested is that by combining maps from several surveys, a weed map with acceptable accuracy for endusers can be produced. If the concept is proved and can be commercialised, systems could be retrofitted at low cost onto existing farm machinery. The outputs of the weed mapping software would then link with the precision farming options already built into many commercial sprayers, allowing their use for targeted, site-specific herbicide applications. Immediate economic benefits would, therefore, arise directly from reducing herbicide costs. SSWM will also reduce the overall pesticide load on the crop and so may reduce pesticide residues in food and drinking water, and reduce adverse impacts of pesticides on non-target species and beneficials. Farmers may even choose to leave unsprayed some non-injurious, environmentally-beneficial, low density weed infestations. These benefits fit very well with the anticipated legislation emerging in the new EU Thematic Strategy for Pesticides which will encourage more targeted use of pesticides and greater uptake of Integrated Crop (Pest) Management approaches, and also with the requirements of the Water Framework Directive to reduce levels of pesticides in water bodies. The greater precision of weed management offered by SSWM is therefore a key element in preparing arable farming systems for the future, where policy makers and consumers want to minimise pesticide use and the carbon footprint of farming while maintaining food production and security. The mapping technology could also be used on organic farms to identify areas of fields needing mechanical weed control thereby reducing both carbon footprints and also damage to crops by, for example, spring tines. Objective i. To develop a prototype machine vision system for automated image capture during agricultural field operations; ii. To prove the concept that images captured by the machine vision system over a series of field operations can be processed to identify and geo-reference specific weeds in the field; iii. To generate weed maps from the geo-referenced, weed plants/patches identified in objective (ii).