According to the recognition heuristic (RH), for decision domains where recognition is a valid predictor of a choice criterion, recognition alone is used to make inferences whenever one object is recognized and the other is not, irrespective of further knowledge. Erdfelder, Küpper-Tetzel, and Mattern (2011) questioned whether the recognition judgment itself affects decisions or rather the memory strength underlying it. Specifically, they proposed to extend the RH to the memory state heuristic (MSH), which assumes a third memory state of uncertainty in addition to recognition certainty and rejection certainty. While the MSH already gathered significant support, one of its basic and more counterintuitive predictions has not been tested so far: In guessing pairs (none of the objects recognized), the object more slowly judged as unrecognized should be preferred, since it is more likely to be in a higher memory state. In this paper, we test this prediction along with other recognition latency predictions of the MSH, thereby adding to the body of research supporting the MSH.

Judgments and decisions under uncertainty are frequently linked to a prior sequential search for relevant information. In such cases, the subject has to decide when to stop the search for information. Evidence accumulation models from social and cognitive psychology assume an active and sequential information search until enough evidence has been accumulated to pass a decision threshold. In line with such theories, we conceptualize the evidence threshold as the “desired level of confidence” (DLC) of a person. This model is tested against a fixed stopping rule (one-reason decision making) and against the class of multi-attribute information integrating models. A series of experiments using an information board for horse race betting demonstrates an advantage of the proposed model by measuring the individual DLC of each subject and confirming its correctness in two separate stages. In addition to a better understanding of the stopping rule (within the narrow framework of simple heuristics), the results indicate that individual aspiration levels might be a relevant factor when modelling decision making by task analysis of statistical environments.

Assuming a society of conditional cooperators (or moody conditional cooperators), this computational study proposes a new perspective on the structural advantage of social network clustering. Previous work focused on how clustered structure might encourage initial outbreaks of cooperation or defend against invasion by a few defectors. Instead, we explore the ability of a societal structure to retain cooperative norms in the face of widespread disturbances. Such disturbances may abstractly describe hardships like famine and economic recession, or the random spatial placement of a substantial numbers of pure defectors (or round-1 defectors) among a spatially structured population of players in a laboratory game, etc.

As links in tightly clustered societies are reallocated to distant contacts, we observe that a society becomes increasingly susceptible to catastrophic cascades of defection: mutually-beneficial cooperative norms can be destroyed completely by modest shocks of defection. In contrast, networks with higher clustering coefficients can withstand larger shocks of defection before being forced to catastrophically low levels of cooperation. We observe a remarkably linear protective effect of clustering coefficient that becomes active above a critical level of clustering. Notably, both the critical level and the slope of this dependence is higher for decision-rule parameterizations that correspond to higher costs of cooperation. Our modeling framework provides a simple way to reinterpret the counter-intuitive and widely cited human experiments of Suri and Watts (2011) while also affirming the classical intuition that network clustering and higher levels of cooperation should be positively associated.

Probit-based models relating a proportional response variable to a temporal explanatory variable, assuming that the times to response are normally distributed within the population, have been used in seed biology for describing the rate of loss of viability during seed ageing and the progress of germination over time in response to environmental signals (e.g. water, temperature). These models may be expressed as generalized linear models (GLMs) with a probit (cumulative normal distribution) link function, and, using GLM fitting procedures in current statistical software, parameters of these models are efficiently estimated while taking into account the binomial error distribution of the dependent variable. The fitted parameters can then be used to calculate the ‘traditional’ model parameters, such as the hydro- or hydrothermal time constant, the mean or median response of the seeds (e.g. mean time to death, median base water potential), and the standard deviation of the normal distribution of that response. Furthermore, through consideration of the deviance and residuals, performing model evaluation and modification can lead to improved understanding of the underlying physiological/ecological processes. However, fitting a binomial GLM is not appropriate for the cumulative count data often collected from germination studies, as successive observations are not independent, and time-to-event/survival analysis should be considered instead. This review discusses well-known probit-based models, providing advice on how to collect appropriate data and fit the models to those data, and gives an overview of alternative analysis approaches to improve understanding of the underlying mechanisms of seed dormancy and germination behaviour.

Seed germination is a crucial event in a plant's life cycle. Because temperature and water availability are important regulators of seed germination, this process will likely be influenced by global warming. Insight into the germination process under global warming is thus crucial, and requires the study of a wide range of water availability and temperature conditions. As hydrothermal time (HTT) models evaluate seed germination for any combination of water potential and temperature, they can be suitable to predict global warming effects on seed germination. We studied the germination characteristics of the high Andean endemic tree species Polylepis besseri (Rosaceae), using HTT models. We were especially interested in the potential effects of global warming on seed germination. Assembly of HTT models for P. besseri was fairly straightforward due to the lack of a seed dormancy mechanism. The models allowed prediction of Polylepis germination under constant and alternating temperatures. Initially, a global warming induced increase in the field minimum and mean temperature will increase P. besseri germination, but as maximum temperatures rise above the optimum temperature for the species, seed germination will become jeopardized. Effects of global warming on seed germination are currently considerably underexplored. HTT models prove to be useful tools to study a plant species' general germination characteristics, and how they may become affected under global warming. For the endemic mountain tree species P. besseri, we predict an increase, followed by a decrease of seed germination under global warming.

This paper reviews the stochastic asset model described in Wilkie (1995) and previous work on refining this model. The paper then considers the application on non-linear modelling to investment series, considering both ARCH techniques and threshold modelling. The paper suggests a threshold autoregressive (TAR) system as a useful progression from the Wilkie (1995) model. The authors are making available (on compact disk) a collection of spreadsheets, which they have used to simulate the stochastic asset models which are considered in this paper.

Data from 2032 Uruguayan Aberdeen Angus cows under extensive management and recording practices were analysed with Bayesian threshold-liability sire models, to assess genetic variability in calving success (CS), defined as a different binary trait for each of the second (CS2), third (CS3) and fourth (CS4) calving opportunities. Sire (herd) variances ranged from 0.08 to 0.11 (0.10 to 0.20) and heritability from 0.27 to 0.35, with large credibility intervals. Correlations between herd effects on CS at different calving opportunities were positive. Genetic correlation between CS2 and CS4 was positive (0.68), whereas those involving adjacent calving opportunities (CS2–CS3 and CS3–CS4) were negative, at −0.39 and −0.54, respectively. The residual correlation CS2–CS3 was negative (−0.32). The extent of uncertainty associated with the posterior estimates of the parameters was further evaluated through simulation, assuming different true values (−0.4, −0.2, +0.2 and +0.4) for the genetic correlations and changes in the degree of belief parameters of the inverse Wishart priors for the sire covariance matrix. Although inferences were not sharp enough, CS appears to be moderately heritable. The quality of data recording should be improved, in order to effect genetic improvement in female fertility.

We test the expectations theory of the term structure of U.S. interest rates in nonlinear systems. These models allow the response of the change in short rates to past values of the spread to depend upon the level of the spread. The nonlinear system is tested against a linear system, and the results of testing the expectations theory in both models are contrasted. We find that the results of tests of the implications of the expectations theory depend on the size and sign of the spread. The long maturity spread predicts future changes of the short rate only when it is high.

Previous studies investigating threshold behavior in real-exchange-rate and price difference data have used rather ad hoc statistical methods and have focused on univariate threshold models for relative prices. We utilize a general multivariate threshold cointegration model and develop a systematic testing and estimation strategy for this model, building on the work of others. Using Monte Carlo experiments, we systematically compare the use of univariate and multivariate techniques for testing threshold cointegration, estimating various threshold models, and testing specifications. We apply our methodology to a large set of U.S. disaggregated CPI data. We find evidence of threshold cointegration mainly for tradable goods. However, the type of threshold nonlinearity that we find generally does not support the transaction-cost view of commodity arbitrage.

A Bayesian threshold-liability model with Markov chain Monte Carlo techniques was used to infer genetic parameters for clinical mastitis records collected on Holstein-Friesian cows by one of the United Kingdom’s national recording schemes. Four data sets were created to investigate the effect of data sampling methods on genetic parameter estimates for first and multi-lactation cows, separately. The data sets were: (1) cows with complete first lactations only (8671 cows); (2) all cows, with first lactations whether complete or incomplete (10 967 cows); (3) cows with complete multi-lactations (32 948 records); and (4) all cows with multiple lactations whether complete or incomplete (44 268 records). A Gaussian mixed linear model with sire effects was adopted for liability. Explanatory variables included in the model varied for each data set. Analyses were conducted using Gibbs sampling and estimates were on the liability scale. Posterior means of heritability for clinical mastitis were higher for first lactations (0·11 and 0·10 for data sets 1 and 2, respectively) than for multiple lactations (0·09 and 0·07, for data sets 3 and 4, respectively). For multiple lactations, estimates of permanent environmental variance were higher for complete than incomplete lactations. Repeatability was 0·21 and 0·17 for data sets 3 and 4, respectively. This suggests the existence of effects, other than additive genetic effects, on susceptibility to mastitis that are common to all lactations. In first or multi-lactation data sets, heritability was proportionately 0·10 to 0·19 lower for data sets with all records (in which case the models had days in milk as a covariate) than for data with only complete lactation records (models without days in milk as a covariate). This suggests an effect of data sampling on genetic parameter estimates. The regression of liability on days in milk differed from zero, indicating that the probability of mastitis is higher for longer lactations, as expected. Results also indicated that a regression on days in milk should be included in a model for genetic evaluation of sires for mastitis resistance based on records in progress.

This study provides estimates of genetic parameters for various diseases, fertility and 305-day milk production traits in dairy cattle using data from a UK national milk recording scheme. The data set consisted of 63891 multiple lactation records on diseases (mastitis, lameness, milk fever, ketosis and tetany), fertility traits (calving interval, conception to first service, number of services for a conception, and number of days to first service), dystocia and 305-day milk, fat and protein yield. All traits were analysed by multi-trait repeatability linear animal models (LM). Binary diseases and fertility traits were further analysed by threshold sire models (TM). Both LM and TM analyses were based on the generalized linear mixed model framework. The LM included herd-year-season of calving (HYS), age at calving and parity as fixed effects and genetic, permanent environmental and residual effects as random. The TM analyses included the same effects as for LM, but HYS effects were treated as random to avoid convergence problems when HYS sub-classes had 0 or 100% incidence. Because HYS effects were treated as random, herd effects were fitted as fixed effects to account for effect of herds in the data. The LM estimates of heritability ranged from 0•389 to 0•399 for 305-day milk production traits, 0•010 to 0•029 for fertility traits and 0•004 to 0•038 for diseases. The LM estimates of repeatability ranged from 0•556 to 0•586 for 305-day milk production traits, 0•029 to 0•086 for fertility traits and 0•004 to 0•100 for diseases. The TM estimates of heritabilities and repeatabilities were greater than LM estimates for binary traits and were in the range 0•012 to 0•126 and 0•013 to 0•168, respectively. Genetic correlations between milk production traits and fertility and diseases were all unfavorable: they ranged from 0•07 to 0•37 for milk production and diseases, 0•31 to 0•54 for milk production and poor fertility and 0•06 to 0•41 for diseases and poor fertility. These results show that future selection programmes should include disease and fertility for genetic improvement of health and reproduction and for sustained economic growth in the dairy cattle industry.

Data on lamb survival from birth to weaning of Rambouillet and Finnsheep were analysed with sire-maternal grandsire linear (LM) and threshold (TM) models. Models for the Rambouillet included the effects of year, type of birth, age of dam and sex as fixed effects and sire, maternal grandsire and residual as random effects. For the Finnsheep, fixed effects were year-age of dam combination, type of birth-rearing and sex, and random effects were sire, maternal grandsire and residual. Restricted maximum likelihood (REML) and marginal maximum likelihood (MML) estimates of variance and covariance components were obtained under LM and TM, respectively. The performance of LM and TM was assessed in terms of goodness of fit and predictive ability. Within the Rambouillet breed, heritabilities of additive direct (0·06), additive maternal (0·04) and total effects (0·13) obtained with TM were 2, 1·3 and 1·9 times greater than those obtained with LM, respectively. For the Finnsheep data, estimated heritabilities of direct (0·17), maternal (0·26) and total (0·34) effects using TM were 1·9, 1·4 and 1·6 times greater than the estimates using LM, respectively. Estimated genetic correlation between direct and maternal effects was 0·14 for the Finnsheep with both the LM and TM. Higher genetic correlations between direct and maternal effects (0·44 and 0·62 with LM and TM, respectively) were obtained for the Rambouillet data. Goodness of fit and predictive ability of the models used with the Finnsheep data were better than for the models used with the Rambouillet data, but within breed, no sizable or significant differences were detected between LM and TM. Results indicate that maternal effects were important in lamb survival to weaning; especially in the more prolific Finnsheep breed.

Cumulative germination curves were recorded for carrot (Daucus carota L.) seeds at a range of constant temperatures (T) and water potentials (Ψ) in the laboratory and under variable soil conditions in 15 seed-bed environments in the field. A single base temperature (Tb), a distribution of base water potentials (Ψb(G)) for percentiles (G) of the population and the hydrothermal time constant (Hg) were determined from laboratory data. Although less effective at low Ψ, it was possible, using these germination parameters, to satisfactorily describe the effect of T and Ψ on germination rates under constant conditions according to the threshold models of thermal and hydrothermal time. These models were applied to field data with the condition that the germination process ceased if T<Tb for thermal time and additionally Ψ<Ψb(G) for hydrothermal time.

Neither model accurately predicted germination patterns in the field. However, the pattern of germination was adequately described in most situations by a modified threshold model in which the predicted progress towards germination was unaffected by soil Ψ, provided it remained above Ψb(G), and was therefore more rapid under variable seed bed conditions than hydrothermal time. In this modified threshold model, the condition Ψ<Ψb(G) had to be fulfilled at the initiation of radicle extension before germination occurred. This result implies that the initiation of radicle growth operates as a moisture-sensitive step that can determine germination and seedling emergence timing under variable soil-moisture conditions.

Seedling emergence was also recorded in the field and used to determine, separately, the impact of germination and post-germination growth on the variation in seedling emergence patterns. The analysis suggests that delays in seedling emergence occur largely in the germination phase, but that seedling losses and variation in the spread of seedling emergence times within the population occur largely during the post-germination growth phase.

Predictions of two models were compared. The models relate ovulation rate (OR) and prenatal survival (PS) to litter size (LS): the uterine capacity model (UCM), where maximum LS is limited by uterine capacity (UC), and the threshold model (TM) whereby PS is modelled as a binary threshold trait. Records were simulated according to both models using statistics from Trench Large White gilts. Both models were able to reproduce closely the observed curvilinear relationship between OR and LS, with LS reaching a plateau at high OR. Several genetic correlations (ρg) fulfilling the conditions h20R = 0·34 and h2LS = 0·12 (the residual maximum likelihood estimates in the population) were studied by means of stochastic computer simulation. The genetic correlation between OR and LS was very sensitive to changes in h2uc, whereas ρgLS,PS was always positive, and ρgOR,PS was always negative. The correlation between PS and UC was larger than 0·90, except for very small h2UC This suggests that genes affecting PS have a strong influence on UC and that PS can be a good indirect criterion to select for UC. Both models predicted that the advantage of an index combining OR and LS with respect to direct selection on LS diminishes in successive generations of selection and that the size of the experiment needed to detect significant differences is very large. Records were also simulated by halving the mean and variance of UC, so as to mimic unilateral hysterectomy-ovariectomy (UHO). If the UHO treatment results in halving UC, LS of UHO females should behave very much as half the UC of intact females.

The paper investigates stochastic processes directed by a randomized time process. A new family of directing processes called Hougaard processes is introduced. Monotonicity properties preserved under subordination, and dependence among processes directed by a common randomized time are studied. Results for processes subordinated to Poisson and stable processes are presented. Potential applications to shock models and threshold models are also discussed. Only Markov processes are considered.

Genetic parameters for dystocia in the Main-Anjou breed were estimated. Data consisted of 28 178 birth records collected between 1978 and 1989 in 995 herds, with 161, 71 and 12 415 sires, maternal grandsires and dams, respectively, represented. Original scores (1 through 5) were collapsed in order to set two dystocia definitions: dystocia 1 (scores 1+2 v. 3+4+5) and dystocia 2 (scores 1 v. 2+3+4+5). Four models were proposed for genetic parameter estimation: (1) fixed effects plus sire effects; (2) model 1 plus maternal grandsire effect; (3) model 2 plus dam within maternal grandsire effects; (4) same as model 3 but a random effect ‘herds’ replaced a fixed effect ‘regions’. Two methods of fitting models were applied: marginal maximum likelihood and the ‘tilde-hat’ approach. Estimates of genetic parameters by the two methods were similar. Models ignoring maternal effects overestimated the heritability of direct effects especially in the case of dystocia 2. Dystocia definition was responsible for the greatest difference among estimated genetic parameters. Possible reasons for this are discussed. When analysing large data sets, it is recommended judiciously to collapse dystocia categories and to apply approximate statistical procedures to complete models including maternal effects.