Testing preference formation in learning design contingent valuation (LDCV) using advanced information and repetitive treatments

Policymakers have largely replaced Single Bounded Discrete Choice (SBDC) valuation by the more statistically efficient repetitive methods; Double Bounded Discrete Choice (DBDC) and Discrete Choice Experiments (DCE) . Repetitive valuation permits classification into rational preferences: (i) a priori well-formed; (ii) consistent non-arbitrary values “discovered” through repetition and experience; (Plott, 1996; List 2003) and irrational preferences; (iii) consistent but arbitrary values as “shaped” by preceding bid level (Tufano, 2010; Ariely et al., 2003) and (iv) inconsistent and arbitrary values. Policy valuations should demonstrate behaviorally rational preferences. We outline novel methods for testing this in DBDC applied to renewable energy premiums in Chile.

A study to evaluate the introduction of simulation as a teaching strategy for the mental health and learning disability fields of nursing, in an undergraduate nursing curriculum within one higher education institution

High Fidelity Simulation or Human Patient Simulation is an educational strategy embedded within nursing curricula throughout many healthcare educational institutions. This paper reports on an evaluative study that investigated the views of a group of Year 2 undergraduate nursing students from the mental health and the learning disability fields of nursing (n = 75) in relation to simulation as a teaching pedagogy. The study took place in the simulation suite within a School of Nursing and Midwifery in the UK. Two patient scenarios were used for the session and participants completed a 22-item questionnaire consisting of three biographical information questions and a 19-item Likert scale. Descriptive statistics were employed to illustrate the data and non-parametric testing (Mann-Whitney U test) was employed to test a number of hypotheses. Overall students were positive about the introduction of patient scenarios using the human patient simulator into the undergraduate nursing curriculum. This study used a small, convenience sample in one institution and therefore the results obtained cannot be generalised to nursing education before further research can be conducted with larger samples and a mixed-method research approach. However these results provide encouraging evidence to support the use of simulation within the mental health and the learning disability fields of nursing, and the development and implementation of further simulations to complement the students’ practicum.

Belief Updating and Learning in Semi-Qualitative Probabilistic Networks

This paper explores semi-qualitative probabilistic networks (SQPNs) that combine numeric and qualitative information. We first show that exact inferences with SQPNs are NPPP-Complete. We then show that existing qualitative relations in SQPNs (plus probabilistic logic and imprecise assessments) can be dealt effectively through multilinear programming. We then discuss learning: we consider a maximum likelihood method that generates point estimates given a SQPN and empirical data, and we describe a Bayesian-minded method that employs the Imprecise Dirichlet Model to generate set-valued estimates.