Performance and prediction: Bayesian modelling of fallible choice in chess

Evaluating agents in decision-making applications requires assessing their skill and predicting their behaviour. Both are well developed in Poker-like situations, but less so in more complex game and model domains. This paper addresses both tasks by using Bayesian inference in a benchmark space of reference agents. The concepts are explained and demonstrated using the game of chess but the model applies generically to any domain with quantifiable options and fallible choice. Demonstration applications address questions frequently asked by the chess community regarding the stability of the rating scale, the comparison of players of different eras and/or leagues, and controversial incidents possibly involving fraud. The last include alleged under-performance, fabrication of tournament results, and clandestine use of computer advice during competition. Beyond the model world of games, the aim is to improve fallible human performance in complex, high-value tasks.

Calibrating flood inundation models: identifying and addressing uncertainty in satellite observed flood extents

Improvements in the resolution of satellite imagery have enabled extraction of water surface elevations at the margins of the flood. Comparison between modelled and observed water surface elevations provides a new means for calibrating and validating flood inundation models, however the uncertainty in this observed data has yet to be addressed. Here a flood inundation model is calibrated using a probabilistic treatment of the observed data. A LiDAR guided snake algorithm is used to determine an outline of a flood event in 2006 on the River Dee, North Wales, UK, using a 12.5m ERS-1 image. Points at approximately 100m intervals along this outline are selected, and the water surface elevation recorded as the LiDAR DEM elevation at each point. With a planar water surface from the gauged upstream to downstream water elevations as an approximation, the water surface elevations at points along this flooded extent are compared to their ‘expected’ value. The pattern of errors between the two show a roughly normal distribution, however when plotted against coordinates there is obvious spatial autocorrelation. The source of this spatial dependency is investigated by comparing errors to the slope gradient and aspect of the LiDAR DEM. A LISFLOOD-FP model of the flood event is set-up to investigate the effect of observed data uncertainty on the calibration of flood inundation models. Multiple simulations are run using different combinations of friction parameters, from which the optimum parameter set will be selected. For each simulation a T-test is used to quantify the fit between modelled and observed water surface elevations. The points chosen for use in this T-test are selected based on their error. The criteria for selection enables evaluation of the sensitivity of the choice of optimum parameter set to uncertainty in the observed data. This work explores the observed data in detail and highlights possible causes of error. The identification of significant error (RMSE = 0.8m) between approximate expected and actual observed elevations from the remotely sensed data emphasises the limitations of using this data in a deterministic manner within the calibration process. These limitations are addressed by developing a new probabilistic approach to using the observed data.

Quantifying uncertainty in the simulation of crop-climate processes

The impacts of climate change on crop productivity are often assessed using simulations from a numerical climate model as an input to a crop simulation model. The precision of these predictions reflects the uncertainty in both models. We examined how uncertainty in a climate (HadAM3) and crop General Large-Area Model (GLAM) for annual crops model affects the mean and standard deviation of crop yield simulations in present and doubled carbon dioxide (CO2) climates by perturbation of parameters in each model. The climate sensitivity parameter (λ, the equilibrium response of global mean surface temperature to doubled CO2) was used to define the control climate. Observed 1966–1989 mean yields of groundnut (Arachis hypogaea L.) in India were simulated well by the crop model using the control climate and climates with values of λ near the control value. The simulations were used to measure the contribution to uncertainty of key crop and climate model parameters. The standard deviation of yield was more affected by perturbation of climate parameters than crop model parameters in both the present-day and doubled CO2 climates. Climate uncertainty was higher in the doubled CO2 climate than in the present-day climate. Crop transpiration efficiency was key to crop model uncertainty in both present-day and doubled CO2 climates. The response of crop development to mean temperature contributed little uncertainty in the present-day simulations but was among the largest contributors under doubled CO2. The ensemble methods used here to quantify physical and biological uncertainty offer a method to improve model estimates of the impacts of climate change.

Quantification of physical and biological uncertainty in the simulation of the yield of a tropical crop using present-day and doubled CO2 climates

The impacts of climate change on crop productivity are often assessed using simulations from a numerical climate model as an input to a crop simulation model. The precision of these predictions reflects the uncertainty in both models. We examined how uncertainty in a climate (HadAM3) and crop General Large-Area Model (GLAM) for annual crops model affects the mean and standard deviation of crop yield simulations in present and doubled carbon dioxide (CO2) climates by perturbation of parameters in each model. The climate sensitivity parameter (lambda, the equilibrium response of global mean surface temperature to doubled CO2) was used to define the control climate. Observed 1966-1989 mean yields of groundnut (Arachis hypogaea L.) in India were simulated well by the crop model using the control climate and climates with values of lambda near the control value. The simulations were used to measure the contribution to uncertainty of key crop and climate model parameters. The standard deviation of yield was more affected by perturbation of climate parameters than crop model parameters in both the present-day and doubled CO2 climates. Climate uncertainty was higher in the doubled CO2 climate than in the present-day climate. Crop transpiration efficiency was key to crop model uncertainty in both present-day and doubled CO2 climates. The response of crop development to mean temperature contributed little uncertainty in the present-day simulations but was among the largest contributors under doubled CO2. The ensemble methods used here to quantify physical and biological uncertainty offer a method to improve model estimates of the impacts of climate change.

Greenhouse whitefly (Homoptera : Aleyrodidae) dispersal under different UV-light environments

The greenhouse whitefly, Dialeurodes vaporariorum (Westwood) (Homoptera: Aleyrodidae), is known to respond to UV light (UV). Field studies were conducted to improve our understanding of the behavioral effects and practical implications of using UV-blocking plastic films for the control of whitefly. Adult whiteflies were released in outdoor-located choice-chamber experiments with compartments clad with a range of films that transmitted incident UV to different extents. In release-recapture experiments, a very small proportion of the whiteflies recovered had dispersed into compartments where the entire UV spectrum was blocked, whereas the major proportion preferred compartments with UV. Compartments clad with films that blocked LTV below 375 nm attracted significantly more whiteflies than films that blocked UV below 385 nm, whereas the absorption of LTV wavelengths above 385 nm did not show any further effect on whitefly numbers. A reduction in the side cladding of the compartments by > 20% significantly reduced the advantage of using LTV-blocking films. Adult whitefly did not discriminate between direct- and diffused-light environments, as long as the UV-absorbing properties of the films were equivalent. Whitefly dispersal was influenced by the time of the day when adult whitefly were released, with a higher proportion of whitelly avoiding compartments clad with LTV-blocking films, at times of the day when light intensities were higher. The future use of UV-blocking films as a potentially highly effective component of integrated pest management systems for the control of whitefly is discussed.

Occupational choice, socio-economic status and educational attainment: a study of the occupational choices and destinations of young people in the British household panel survey

The article considers young people's occupational choices at the age of 15 in relation to their educational attainment, the occupations of their parents and their actual occupations when they are in their early 20s. It uses data from the British Household Panel Survey over periods of between five and ten years. The young people in the survey are occupationally ambitious: many more aspire to professional, managerial and technical jobs than the likely availability of these occupations. In general ambitions and educational attainment and intentions are well aligned but there are also many instances of misalignment; either people wanting jobs which their educational attainments and intentions will not prepare them for, or people with less ambitious aspirations than their educational performance would justify. Children from more occupationally advantaged families are more ambitious, achieve better educationally and have better occupational outcomes than other children. However, where young people are both ambitious and educationally successful the occupational outcomes are as good for those from disadvantaged as advantaged families. In contrast, where young people are neither ambitious nor educationally successful, the outcomes for those from disadvantaged homes are very much poorer than for other young people. The article suggests that while choice is real it is also heavily constrained for many people. A possible educational implication of the study is that career interventions could be directed at under-ambitious but academically capable young people from disadvantaged backgrounds.

Implicit memory: a prime example for brand consideration and choice

Three experiments investigated the influence of implicit memory for familiar brand names on consumer choice. Priming was measured using modified preference judgment tasks that comprised both brand consideration and choice components. Experiment 1 used a 'complex choice task' where consideration and choice stages were characterized as acting in sequence. Experiment 2 explored a different formulation whereby consideration and choice were assumed to act in parallel, Both experiments demonstrated that priming had an influence on brand consideration but not on final or preferred choice. Finally, Experiment 3 replicated and extended these findings under more realistic conditions where participants actually received some of the products that they selected. Overall, the experiments suggested that for many decisions involving the consideration of familiar brands prior to choice, previous exposure to brand names can increase the likelihood that they will enter the consumers' consideration set. However, the advantage does not appear to extend to choice itself. Copyright (C) 2004 John Wiley Sons, Ltd.

Decision theory and real estate development: a note on uncertainty

Real estate development appraisal is a quantification of future expectations. The appraisal model relies upon the valuer/developer having an understanding of the future in terms of the future marketability of the completed development and the future cost of development. In some cases the developer has some degree of control over the possible variation in the variables, as with the cost of construction through the choice of specification. However, other variables, such as the sale price of the final product, are totally dependent upon the vagaries of the market at the completion date. To try to address the risk of a different outcome to the one expected (modelled) the developer will often carry out a sensitivity analysis on the development. However, traditional sensitivity analysis has generally only looked at the best and worst scenarios and has focused on the anticipated or expected outcomes. This does not take into account uncertainty and the range of outcomes that can happen. A fuller analysis should include examination of the uncertainties in each of the components of the appraisal and account for the appropriate distributions of the variables. Similarly, as many of the variables in the model are not independent, the variables need to be correlated. This requires a standardised approach and we suggest that the use of a generic forecasting software package, in this case Crystal Ball, allows the analyst to work with an existing development appraisal model set up in Excel (or other spreadsheet) and to work with a predetermined set of probability distributions. Without a full knowledge of risk, developers are unable to determine the anticipated level of return that should be sought to compensate for the risk. This model allows the user a better understanding of the possible outcomes for the development. Ultimately the final decision will be made relative to current expectations and current business constraints, but by assessing the upside and downside risks more appropriately, the decision maker should be better placed to make a more informed and “better”.

Precisely wrong or roughly right? An evaluation of development viability appraisal modelling

Purpose – The purpose of this paper is to investigate the effect of choices of model structure and scale in development viability appraisal. The paper addresses two questions concerning the application of development appraisal techniques to viability modelling within the UK planning system. The first relates to the extent to which, given intrinsic input uncertainty, the choice of model structure significantly affects model outputs. The second concerns the extent to which, given intrinsic input uncertainty, the level of model complexity significantly affects model outputs. Design/methodology/approach – Monte Carlo simulation procedures are applied to a hypothetical development scheme in order to measure the effects of model aggregation and structure on model output variance. Findings – It is concluded that, given the particular scheme modelled and unavoidably subjective assumptions of input variance, that simple and simplistic models may produce similar outputs to more robust and disaggregated models. Evidence is found of equifinality in the outputs of a simple, aggregated model of development viability relative to more complex, disaggregated models. Originality/value – Development viability appraisal has become increasingly important in the planning system. Consequently, the theory, application and outputs from development appraisal are under intense scrutiny from a wide range of users. However, there has been very little published evaluation of viability models. This paper contributes to the limited literature in this area.

Reconciling model and information uncertainty in development appraisal

This paper investigates the effect of choices of model structure and scale in development viability appraisal. The paper addresses two questions concerning the application of development appraisal techniques to viability modelling within the UK planning system. The first relates to the extent to which, given intrinsic input uncertainty, the choice of model structure significantly affects model outputs. The second concerns the extent to which, given intrinsic input uncertainty, the level of model complexity significantly affects model outputs. Monte Carlo simulation procedures are applied to a hypothetical development scheme in order to measure the effects of model aggregation and structure on model output variance. It is concluded that, given the particular scheme modelled and unavoidably subjective assumptions of input variance, simple and simplistic models may produce similar outputs to more robust and disaggregated models.

Stratospheric water vapor and climate: sensitivity to the representation in radiation codes

There has been considerable interest in the climate impact of trends in stratospheric water vapor (SWV). However, the representation of the radiative properties of water vapor under stratospheric conditions remains poorly constrained across different radiation codes. This study examines the sensitivity of a detailed line-by-line (LBL) code, a Malkmus narrow-band model and two broadband GCM radiation codes to a uniform perturbation in SWV in the longwave spectral region. The choice of sampling rate in wave number space (Δν) in the LBL code is shown to be important for calculations of the instantaneous change in heating rate (ΔQ) and the instantaneous longwave radiative forcing (ΔFtrop). ΔQ varies by up to 50% for values of Δν spanning 5 orders of magnitude, and ΔFtrop varies by up to 10%. In the three less detailed codes, ΔQ differs by up to 45% at 100 hPa and 50% at 1 hPa compared to a LBL calculation. This causes differences of up to 70% in the equilibrium fixed dynamical heating temperature change due to the SWV perturbation. The stratosphere-adjusted radiative forcing differs by up to 96% across the less detailed codes. The results highlight an important source of uncertainty in quantifying and modeling the links between SWV trends and climate.

Variation in the global-scale impacts of climate change on crop productivity due to climate model uncertainty and adaptation

Crop production is inherently sensitive to fluctuations in weather and climate and is expected to be impacted by climate change. To understand how this impact may vary across the globe many studies have been conducted to determine the change in yield of several crops to expected changes in climate. Changes in climate are typically derived from a single to no more than a few General Circulation Models (GCMs). This study examines the uncertainty introduced to a crop impact assessment when 14 GCMs are used to determine future climate. The General Large Area Model for annual crops (GLAM) was applied over a global domain to simulate the productivity of soybean and spring wheat under baseline climate conditions and under climate conditions consistent with the 2050s under the A1B SRES emissions scenario as simulated by 14 GCMs. Baseline yield simulations were evaluated against global country-level yield statistics to determine the model's ability to capture observed variability in production. The impact of climate change varied between crops, regions, and by GCM. The spread in yield projections due to GCM varied between no change and a reduction of 50%. Without adaptation yield response was linearly related to the magnitude of local temperature change. Therefore, impacts were greatest for countries at northernmost latitudes where warming is predicted to be greatest. However, these countries also exhibited the greatest potential for adaptation to offset yield losses by shifting the crop growing season to a cooler part of the year and/or switching crop variety to take advantage of an extended growing season. The relative magnitude of impacts as simulated by each GCM was not consistent across countries and between crops. It is important, therefore, for crop impact assessments to fully account for GCM uncertainty in estimating future climates and to be explicit about assumptions regarding adaptation.

Aerosol contribution to the rapid warming of near-term climate under RCP 2.6

The importance of aerosol emissions for near term climate projections is investigated by analysing simulations with the HadGEM2-ES model under two different emissions scenarios: RCP2.6 and RCP4.5. It is shown that the near term warming projected under RCP2.6 is greater than under RCP4.5, even though the greenhouse gas forcing is lower. Rapid and substantial reductions in sulphate aerosol emissions due to a reduction of coal burning in RCP2.6 lead to a reduction in the negative shortwave forcing due to aerosol direct and indirect effects. Indirect effects play an important role over the northern hemisphere oceans, especially the subtropical northeastern Pacific where an anomaly of 5-10\,Wm$^{-2}$ develops. The pattern of surface temperature change is consistent with the expected response to this surface radiation anomaly, whilst also exhibiting features that reflect redistribution of energy, and feedbacks, within the climate system. These results demonstrate the importance of aerosol emissions as a key source of uncertainty in near term projections of global and regional climate.

Reassessing the interaction between investment and tenure uncertainty

A diverse body of empirical literature recognizes that investment can influence tenure security, yet this phenomenon has rarely been examined analytically. This paper develops a theoretical model that demonstrates explicitly conditions under which the probability of eviction is endogenous to investment undertaken on illegally encroached land. By accommodating explicitly the government's objective function and its ability to commit credibly to an eviction policy, the model reveals why both those farmers who under-invest, and those who raise their investment levels to improve tenure security, may be behaving rationally. Indeed, both types of behaviour are accommodated within a single model.

A method for under-sampled ecological network data analysis: plant-pollination as case study

In this paper, we develop a method, termed the Interaction Distribution (ID) method, for analysis of quantitative ecological network data. In many cases, quantitative network data sets are under-sampled, i.e. many interactions are poorly sampled or remain unobserved. Hence, the output of statistical analyses may fail to differentiate between patterns that are statistical artefacts and those which are real characteristics of ecological networks. The ID method can support assessment and inference of under-sampled ecological network data. In the current paper, we illustrate and discuss the ID method based on the properties of plant-animal pollination data sets of flower visitation frequencies. However, the ID method may be applied to other types of ecological networks. The method can supplement existing network analyses based on two definitions of the underlying probabilities for each combination of pollinator and plant species: (1), pi,j: the probability for a visit made by the i’th pollinator species to take place on the j’th plant species; (2), qi,j: the probability for a visit received by the j’th plant species to be made by the i’th pollinator. The method applies the Dirichlet distribution to estimate these two probabilities, based on a given empirical data set. The estimated mean values for pi,j and qi,j reflect the relative differences between recorded numbers of visits for different pollinator and plant species, and the estimated uncertainty of pi,j and qi,j decreases with higher numbers of recorded visits.