One prominent model in the realm of memory-based judgments and decisions is the recognition heuristic. Under certain preconditions, it presumes that choices are based on recognition in a one-cue non-compensatory manner and that other information is ignored. This claim has been studied widely—and received, at best, mixed support—in probabilistic inferences. By contrast, only a small number of recent investigations have taken the RH to the realm of preferential decisions (i.e. consumer choice). So far, the conclusion has been that the RH cannot satisfactorily account for aggregate data patterns, but no fully specified alternative model has been demonstrated to provide a better account. Herein, the data from a recent consumer-choice study (Thoma & Williams, 2013) are re-analyzed with the outcome-based maximum-likelihood strategy classification method, thus testing several competing models on individual data. Results revealed that an alternative compensatory model (an equal weights strategy) accounted best for a larger number of datasets than the RH. Thereby, the findings further specify prior results and answer the call for comparative model testing on individual data that has been voiced repeatedly.

The advantage of models that do not use flexible parameters is that one can precisely show to what degree they predict behavior, and in what situations. In three issues of this journal, the recognition heuristic has been examined carefully from many points of view. We comment here on four themes, the use of optimization models to understand the rationality of heuristics, the generalization of the recognition input beyond a binary judgment, new conditions for less-is-more effects, and the importance of specifying boundary conditions for cognitive heuristics.

Often research in judgment and decision making requires comparison of multiple competing models. Researchers invoke global measures such as the rate of correct predictions or the sum of squared (or absolute) deviations of the various models as part of this evaluation process. Reliance on such measures hides the (often very high) level of agreement between the predictions of the various models and does not highlight properly the relative performance of the competing models in those critical cases where they make distinct predictions. To address this important problem we propose the use of pair-wise comparisons of models to produce more informative and targeted comparisons of their performance, and we illustrate this procedure with data from two recently published papers. We use Multidimensional Scaling of these comparisons to map the competing models. We also demonstrate how intransitive cycles of pair-wise model performance can signal that certain models perform better for a given subset of decision problems.

The determination of livestock growth patterns is important for meat or milk production systems, and nonlinear models are used to summarize and interpret the information. The aim of this study was to more accurately estimate growth curve parameters in buffalo cows by evaluating and selecting nonlinear mixed models that employ different types of residuals and include or not contemporary groups (CG) as a covariate. Weight records from 720 animals obtained over a period of 60 months were used. The growth curves were fit using nonlinear mixed-effects models. The Bertalanffy, Gompertz and Logistic models were evaluated. Modelling residuals using four structures (constant, combined, exponential and proportional) and the inclusion or not of CG in the models were also evaluated. The Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used to select the model. In addition to estimating the parameters of the nonlinear growth models and their correlations, the instantaneous growth rate and inflection point were obtained. The Bertalanffy model with a combined residual structure and CG exhibited the lowest AIC and BIC values. Asymptotic weight (A) estimates ranged from 621.8 to 742.1 kg, and the maturity rate (k) ranged from 0.068 to 0.115 kg/month. The correlation between A and k ranged from −0.32 to −0.82 among the models evaluated. The selection criteria indicated that the Bertalanffy model was the most suitable for growth curve analysis in buffaloes.

Vaccination is increasingly being recognised as a potential tool to supplement ‘stamping out’ for controlling foot-and-mouth disease (FMD) outbreaks in non-endemic countries. Infectious disease simulation models provide the opportunity to determine how vaccination might be used in the face of an FMD outbreak. Previously, consistent relative benefits of specific vaccination strategies across different FMD simulation modelling platforms have been demonstrated, using a UK FMD outbreak scenario. We extended this work to assess the relative effectiveness of selected vaccination strategies in five countries: Australia, New Zealand, the USA, the UK and Canada. A comparable, but not identical, FMD outbreak scenario was developed for each country with initial seeding of Pan Asia type O FMD virus into an area with a relatively high density of livestock farms. A series of vaccination strategies (in addition to stamping out (SO)) were selected to evaluate key areas of interest from a disease response perspective, including timing of vaccination, species considerations (e.g. vaccination of only those farms with cattle), risk area vaccination and resources available for vaccination. The study found that vaccination used with SO was effective in reducing epidemic size and duration in a severe outbreak situation. Early vaccination and unconstrained resources for vaccination consistently outperformed other strategies. Vaccination of only those farms with cattle produced comparable results, with some countries demonstrating that this could be as effective as all species vaccination. Restriction of vaccination to higher risk areas was less effective than other strategies. This study demonstrates consistency in the relative effectiveness of selected vaccination strategies under different outbreak start up conditions conditional on the assumption that each of the simulation models provide a realistic estimation of FMD virus spread. Preferred outbreak management approaches must however balance the principles identified in this study, working to clearly defined outbreak management objectives, while having a good understanding of logistic requirements and the socio-economic implications of different control measures.

The aim of the present study was to evaluate the prediction ability of models that cope with longevity phenotypic expression as uncensored and censored in Nellore cattle. Longevity was defined as the difference between the dates of last weaned calf and cow birth. There were information of 77 353 females, being 61 097 cows with uncensored phenotypic information and 16 256 cows with censored records. These data were analyzed considering three different models: (1) Gaussian linear model (LM), in which only uncensored records were considered; and two models that consider both uncensored and censored records: (2) Censored Gaussian linear model (CLM); and (3) Weibull frailty hazard model (WM). For the model prediction ability comparisons, the data set was randomly divided into training and validation sets, containing 80% and 20% of the records, respectively. There were considered 10 repetitions applying the following restrictions: (a) at least three animals per contemporary group in the training set; and (b) sires with more than 10 progenies with uncensored records (352 sires) should have daughters in the training and validation sets. The variance components estimated using the whole data set in each model were used as true values in the prediction of breeding values of the animals in the training set. The WM model showed the best prediction ability, providing the lowest χ2 average and the highest number of sets in which a model had the smallest value of χ2 statistics. The CLM and LM models showed prediction abilities 2.6% and 3.7% less efficient than WM, respectively. In addition, the accuracies of sire breeding values for LM and CLM were lower than those obtained for WM. The percentages of bulls in common, considering only 10% of sires with the highest breeding values, were around 75% and 54%, respectively, between LM–CLM and LM–WM models, considering all sires, and 75% between LM–CLM and LM–WM, when only sires with more than 10 progenies with uncensored records were taken into account. These results are indicative of reranking of animals in terms of genetic merit between LM, CLM and WM. The model in which censored records of longevity were excluded from the analysis showed the lowest prediction ability. The WM provides the best predictive performance, therefore this model would be recommended to perform genetic evaluation of longevity in this population.

Four models for in vitro embryo production traits in Guzerá cattle were compared: Gaussian (untransformed variable – LIN and transformed in logarithmic scale – LOG), Poisson (POI) and zero-inflated Poisson (ZIP). Data consisted of 5716 ovum pick-up and in vitro fertilization records performed in 1205 cows from distinct regions of Brazil. Analyzed count traits were the number of viable oocytes (NOV), number of grade I oocytes (NGI), number of degenerated oocytes (NDG), number of cleaved embryos (NCLV) and number of viable produced embryos (NEMB). Heritability varied from 0.17 (LIN) to 0.25 (POI) for NOV; 0.08 (LOG) to 0.18 (ZIP) for NGI; 0.12 (LIN) to 0.20 (POI) for NDG; 0.13 (LIN) to 0.19 (POI) for NCLV; 0.10 (LIN) to 0.20 (POI) for NEMB depending on the considered model. The estimated repeatability varied from 0.53 (LOG) to 0.63 (POI) for NOV; 0.22 (LOG) to 0.39 (ZIP) for NGI; 0.29 (LIN) to 0.42 (ZIP) for NDG; 0.42 (LIN) to 0.59 (POI) for NCLV; 0.36 (LIN) to 0.51 (POI) for NEMB. The goodness of fit, measured by deviance information criterion and mean squared residuals, suggested superiority of POI and ZIP over Gaussian models. Estimated breeding values (EBV) obtained by different models were highly correlated, varying from 0.92 for NOV (between LIN-POI) and 0.99 for NGI (between POI-ZIP). The number of coincident animals on the 10% top EBV showed lower similarities. We recommend POI and ZIP models as the most adequate for genetic analysis of in vitro embryo production traits in Guzerá cattle.

Simulation models can offer valuable insights into the effectiveness of different control strategies and act as important decision support tools when comparing and evaluating outbreak scenarios and control strategies. An international modelling study was performed to compare a range of vaccination strategies in the control of foot-and-mouth disease (FMD). Modelling groups from five countries (Australia, New Zealand, USA, UK, The Netherlands) participated in the study. Vaccination is increasingly being recognized as a potentially important tool in the control of FMD, although there is considerable uncertainty as to how and when it should be used. We sought to compare model outputs and assess the effectiveness of different vaccination strategies in the control of FMD. Using a standardized outbreak scenario based on data from an FMD exercise in the UK in 2010, the study showed general agreement between respective models in terms of the effectiveness of vaccination. Under the scenario assumptions, all models demonstrated that vaccination with ‘stamping-out’ of infected premises led to a significant reduction in predicted epidemic size and duration compared to the ‘stamping-out’ strategy alone. For all models there were advantages in vaccinating cattle-only rather than all species, using 3-km vaccination rings immediately around infected premises, and starting vaccination earlier in the control programme. This study has shown that certain vaccination strategies are robust even to substantial differences in model configurations. This result should increase end-user confidence in conclusions drawn from model outputs. These results can be used to support and develop effective policies for FMD control.

The Hierarchical Personality Inventory for Children (HiPIC) is a developmentally appropriate parent-report measure of the Five Factor Model (FFM) that has been validated in several European languages but only recently in English. The English translation of the HiPIC was evaluated in an Australian context. Parent-rated HiPIC scores were obtained for 202 children (aged 5–14 years) via an online survey. Exploratory factor analysis indicated five factors that appeared reasonably congruent with the original Flemish HiPIC structure, though with some apparent differences particularly in regard to the Extraversion and Benevolence facets. A Procrustes targeted rotation was used to evaluate the congruence with the original Flemish structure. This indicated encouragingly high congruences for the overall model (.97), as well as high facet and factor congruence (.92–.99). These findings can be taken to reflect the robust nature of the HiPIC model, validating the instrument and more specifically confirming its applicability for use in practice and research investigating children's development and wellbeing in Australia.