Calling in the gap: competition or cooperation in littermates' begging behaviour?

Abstract: Offspring are frequently raised alongside their siblings and are provisioned early in life by adults. Adult provisioning is stimulated by offspring begging, but it is unclear how each offspring should beg, given the begging behaviour of their siblings. It has previously been suggested that siblings may compete directly through begging for a fixed level of provisioning, or that siblings may cooperate in their begging in order to jointly elevate the level of provisioning by adults. We studied the begging behaviour of meerkat Suricata suricatta pups, explored how it changed as the begging behaviour of their littermates altered, and asked how the adults responded to group-level changes in begging. We found conflicting evidence for classic models of competitive and cooperative begging. Pups reared in larger litters begged at higher rates, yet experimentally increasing begging levels within groups caused individual begging rates to decrease. Pups decreased begging rates when close to other begging pups, and pups spaced further apart were fed more. Adults increased their overall level of provisioning as group levels of begging increased, but per capita provisioning decreased. Adults preferred to provision speakers playing back recordings of two pups begging alternately to recordings of the same two pups begging simultaneously. Therefore, we suggest that meerkat pups avoid some of the costs of direct competition imposed by an escalation of begging as other pups beg, by begging in gaps between the bouts of others or avoiding littermates. Such behaviour is also preferred by provisioning adults, thus providing additional benefits to the pups.

Assessment of the benefits of Discrete Conditional Survival Models in modelling ambulance response times

Many of the challenges faced in health care delivery can be informed through building models. In particular, Discrete Conditional Survival (DCS) models, recently under development, can provide policymakers with a flexible tool to assess time-to-event data. The DCS model is capable of modelling the survival curve based on various underlying distribution types and is capable of clustering or grouping observations (based on other covariate information) external to the distribution fits. The flexibility of the model comes through the choice of data mining techniques that are available in ascertaining the different subsets and also in the choice of distribution types available in modelling these informed subsets. This paper presents an illustrated example of the Discrete Conditional Survival model being deployed to represent ambulance response-times by a fully parameterised model. This model is contrasted against use of a parametric accelerated failure-time model, illustrating the strength and usefulness of Discrete Conditional Survival models.

Discrete Conditional Phase-type models utilising classification trees: Application to modelling health service capacities

Discrete Conditional Phase-type (DC-Ph) models consist of a process component (survival distribution) preceded by a set of related conditional discrete variables. This paper introduces a DC-Ph model where the conditional component is a classification tree. The approach is utilised for modelling health service capacities by better predicting service times, as captured by Coxian Phase-type distributions, interfaced with results from a classification tree algorithm. To illustrate the approach, a case-study within the healthcare delivery domain is given, namely that of maternity services. The classification analysis is shown to give good predictors for complications during childbirth. Based on the classification tree predictions, the duration of childbirth on the labour ward is then modelled as either a two or three-phase Coxian distribution. The resulting DC-Ph model is used to calculate the number of patients and associated bed occupancies, patient turnover, and to model the consequences of changes to risk status.

Strategic behavior under partial cooperation

We investigate how a group of players might cooperate with each other within the setting of a non-cooperative game. We pursue two notions of partial cooperative equilibria that follow a modification of Nash's best response rationality rather than a core-like approach. Partial cooperative Nash equilibrium treats non-cooperative players and the coalition of cooperators symmetrically, while the notion of partial cooperative leadership equilibrium assumes that the group of cooperators has a first-mover advantage. We prove existence theorems for both types of equilibria. We look at three well-known applications under partial cooperation. In a game of voluntary provision of a public good we show that our two new equilibrium notions of partial cooperation coincide. In a modified Cournot oligopoly, we identify multiple equilibria of each type and show that a non-cooperator may have a higher payoff than a cooperator. In contrast, under partial cooperation in a symmetric Salop City game, a cooperator enjoys a higher return.

The role of proximity in university-business cooperation for innovation

The potential for universities to contribute positively to business innovation has received much attention in recent years. While the determinants of university-business cooperation have been examined extensively, less attention has been given to the mediating influence of proximity in this relationship. The analysis in this paper builds on theUKbusiness innovation survey (2002–2005) by incorporating measures of the university research environment for each of the 16,500 businesses surveyed. These measures allow us to look beyond business-level characteristics as determinants of the geography of university cooperation and account for the character of the local university environment. Measures include the distance from each business to its nearest university, the quality of local university research and the density of the university research environment. The findings suggest that significant differences exist between those businesses that cooperate with local universities and those that cooperate with non-local universities. These differences relate to business size, sales profile, location, absorptive capacity and innovation activity. In addition, we also find that if a business is located close to a research excellent university, cooperation tends to remain local, however, the distance between businesses and the nearest university is not a significant determinant of university-business cooperation and further, the higher the concentration of universities in the business locale, the more likely businesses are to cooperate with non-local universities.

Computational learning of the conditional phase-type (C-Ph) distribution: Learning C-Ph distributions

This paper presents a new algorithm for learning the structure of a special type of Bayesian network. The conditional phase-type (C-Ph) distribution is a Bayesian network that models the probabilistic causal relationships between a skewed continuous variable, modelled by the Coxian phase-type distribution, a special type of Markov model, and a set of interacting discrete variables. The algorithm takes a dataset as input and produces the structure, parameters and graphical representations of the fit of the C-Ph distribution as output.The algorithm, which uses a greedy-search technique and has been implemented in MATLAB, is evaluated using a simulated data set consisting of 20,000 cases. The results show that the original C-Ph distribution is recaptured and the fit of the network to the data is discussed.