Intelligent kanban : evaluation of a supply chain MAS application using benchmarking

The real world challenges of incomplete information access and bounded computational resources in supply chain management motivate us to propose a bottom up approach to supply chain intelligence, built over a widely used reactive card-based replenishments system (kanban). The rationale is to use agent technology to improve the performance of the traditional kanban system while maintaining its recognized usability. Instead of optimizing a system utility function, we encode the system goal in desired behaviours of individual agents that reason about their own behaviours in the local context. This paper discusses a rigorous framework for evaluation of the proposal based on the concept of benchmarking. Preliminary results from these simulations show remarkable improvements over the traditional system. Furthermore, use of the benchmarking framework gives confidence that these results translate into real performance gains in practical implementations.

Optimal communication amongst team members

We present a formal framework based on the theory of game with incomplete information [5] for modelling the coordination and communication problem among team of collaborative agents, and define what it means by optimal communication in this setting. Although computing an optimal communication strategy for the team is hard in general, we illustrate with an example of collaborative negotiation and meeting scheduling that computation can be substantially reduced when domain dependent assumptions are introduced.

A framework for co-ordination and learning among teams of agents

We present a framework for team coordination under incomplete information based on the theory of incomplete information games. When the true distribution of the uncertainty involved is not known in advance, we consider a repeated interaction scenario and show that the agents can learn to estimate this distribution and share their estimations with one another. Over time, as the set of agents' estimations become more accurate, the utility they can achieve approaches the optimal utility when the true distribution is known, while the communication requirement for exchanging the estimations among the agents can be kept to a minimal level.

Multi-target tracking via space transformations using a single frequency continuous wave radar

This paper investigates the location and velocity estimation problem involving multiple targets using the phase difference and frequency shift of the returned Doppler modulated signal. The minimal receiver configuration that addresses the data association and missing information problem is presented for the case of linear arrays. Non-linearly modeled Doppler radar measurements are used to obtain an accurate estimate of the target dynamics progressively in a linear framework utilizing a recently developed robust state estimation approach.

Market failure, policy failure and other distortions in chronic disease markets

Background: The increasing prevalence of chronic disease represents a significant burden on most health systems. This paper explores the market failures and policy failures that exist in the management of chronic diseases.
Discussion: There are many sources of market failure in health care that undermine the efficiency of chronic disease management. These include incomplete information as well as information asymmetry between providers and consumers, the effect of externalities on consumer behaviour, and the divergence between social and private time preference rates. This has seen government and policy interventions to address both market failures and distributional issues resulting from the inability of private markets to reach an efficient and equitable distribution of resources. However, these have introduced a series of policy failures such as distorted re-imbursement arrangements across modalities and delivery settings.
Summary: The paper concludes that market failure resulting from a preference of individuals for 'immediate gratification' in the form of health care and disease management, rather than preventative services, where the benefits are delayed, has a major impact on achieving an efficient allocation of resources in markets for the management of chronic diseases. This distortion is compounded by government health policy that tends to favour medical and pharmaceutical interventions further contributing to distortions in the allocation of resources and inefficiencies in the management of chronic disease.

Study protocol: asking questions about alcohol in pregnancy (AQUA): a longitudinal cohort study of fetal effects of low to moderate alcohol exposure

Despite extensive research, a direct correlation between low to moderate prenatal alcohol exposure (PAE) and Fetal Alcohol Spectrum Disorders has been elusive. Conflicting results are attributed to a lack of accurate and detailed data on PAE and incomplete information on contributing factors. The public health effectiveness of policies recommending complete abstinence from alcohol during pregnancy is challenged by the high frequency of unplanned pregnancies, where many women consumed some alcohol prior to pregnancy recognition. There is a need for research evidence emphasizing timing and dosage of PAE and its effects on child development.

An analysis of nursing students' decision-making in teams during simulations of acute patient deterioration

AIM: The aim of this study was to examine the decision-making of nursing students during team based simulations on patient deterioration to determine the sources of information, the types of decisions made and the influences underpinning their decisions.

BACKGROUND: Missed, misinterpreted or mismanaged physiological signs of deterioration in hospitalized patients lead to costly serious adverse events. Not surprisingly, an increased focus on clinical education and graduate nurse work readiness has resulted.

DESIGN: A descriptive exploratory design.

METHODS: Clinical simulation laboratories in three Australian universities were used to run team based simulations with a patient actor. A convenience sample of 97 final-year nursing students completed simulations, with three students forming a team. Four teams from each university were randomly selected for detailed analysis. Cued recall during video review of team based simulation exercises to elicit descriptions of individual and team based decision-making and reflections on performance were audio-recorded post simulation (2012) and transcribed.

RESULTS: Students recalled 11 types of decisions, including: information seeking; patient assessment; diagnostic; intervention/treatment; evaluation; escalation; prediction; planning; collaboration; communication and reflective. Patient distress, uncertainty and a lack of knowledge were frequently recalled influences on decisions.

CONCLUSIONS: Incomplete information, premature diagnosis and a failure to consider alternatives when caring for patients is likely to lead to poor quality decisions. All health professionals have a responsibility in recognizing and responding to clinical deterioration within their scope of practice. A typology of nursing students' decision-making in teams, in this context, highlights the importance of individual knowledge, leadership and communication.

Wine prices and quality eatings: a meta-regression analysis

This article examines the empirical support for the hypothesized hedonic theoretical relation between the price of wine and its quality. The examination considers over 180 hedonic wine price models developed over 20 years, covering many countries. The research identifies that the relation between the price of wine and its sensory quality rating is a moderate partial correlation of +0.30. This correlation exists despite the lack of information held by consumers about a wine’s quality and the inconsistency of expert tasters when evaluating wines. The results identify a moderate price-quality correlation, which suggests the existence of strategic buying opportunities for better informed consumers. Strategic price setting possibilities may also exist for wine producers given the incomplete quality information held by consumers. The results from the meta-regression analysis point to the absence of any publication bias, and attribute the observed asymmetry in estimates to study heterogeneity. The analysis suggests the observed heterogeneity is explained by the importance of a wine’s reputation, the use of the 100-point quality rating scale, the analysis of a single wine variety/style, and the employed functional form. The most important implication from the analysis is the relative importance of a wine’s reputation over its sensory quality, inferring that producers need to sustain the sensory quality of a wine over time to extract appropriate returns. The reputation of the wine producer is found not to influence the strength of the price quality relationship. This finding does not contradict the importance of wine producer reputation in directly influencing prices.

The language of design resolution in a digital building information model

Information in construction industry is delivered and interpreted in a language specific to the industry in which large complex objects are only partially described and with much information being implicit in the language used. Successful communication therefore relies on participants in the industry leaming how to interpret the language through many years of education, training and experience. With the introduction of computer technology, and in particular the detailed digital building information model (DB 1M), the accepted language currently in use is no longer a valid method of describing the building. At all stages in the paper based design and documentation process it is generally readily apparent which parts of the design require further completion and which are fully resolved. This is able to be achieved through the complex graphical language currently in use. In the DBIM, all information appears at the same level of resolution making difficult the interpretation of implicit information embedded in the model. This compromises the collaborative design environment which is being described as a fundamental characteristic of the future construction industry. This paper focuses on two areas. The first analyses design resolution and the role uncertain information plays in the design process. It then discusses the manner in which designers and the industry in general deal with incomplete or unresolved information. The second describes a theoretical model in which a design resolution (DR) environment incorporates the level of design resolution as an operable element in a collaborative DBIM. The development and implementation of this model will allow designers to better share, understand and interpret design knowledge from the shared information during the various stages of digital design and before full resolution is achieved.

A note on the property rights theory and the ex ante value of information

This paper examines the ex ante value of information in the property rights model where the possibility exists that an investing agent can be provided with relevant information before investments are undertaken. When contracts are incomplete, from an ex ante perspective, informing the investing agent does not necessarily increase the expected surplus resulting from a relationship between two economic agents. The paper highlights the fact that the second-best nature of the problem that arises from contractual incompleteness can ensure this.

Classifying human actions using an incomplete real-time pose skeleton

Currently, most human action recognition systems are trained with feature sets that have no missing data. Unfortunately, the use of human pose estimation models to provide more descriptive features also entails an increased sensitivity to occlusions, meaning that incomplete feature information will be unavoidable for realistic scenarios. To address this, our approach is to shift the responsibility for dealing with occluded pose data away from the pose estimator and onto the action classifier. This allows the use of a simple, real-time pose estimation (stick-figure) that does not estimate the positions of limbs it cannot find quickly. The system tracks people via background subtraction and extracts the (possibly incomplete) pose skeleton from their silhouette. Hidden Markov Models modified to handle missing data are then used to successfully classify several human actions using the incomplete pose features.

Exploiting side information in distance dependent Chinese restaurant processes for data clustering

Multimedia contents often possess weakly annotated data such as tags, links and interactions. The weakly annotated data is called side information. It is the auxiliary information of data and provides hints for exploring the link structure of data. Most clustering algorithms utilize pure data for clustering. A model that combines pure data and side information, such as images and tags, documents and keywords, can perform better at understanding the underlying structure of data. We demonstrate how to incorporate different types of side information into a recently proposed Bayesian nonparametric model, the distance dependent Chinese restaurant process (DD-CRP). Our algorithm embeds the affinity of this information into the decay function of the DD-CRP when side information is in the form of subsets of discrete labels. It is flexible to measure distance based on arbitrary side information instead of only the spatial layout or time stamp of observations. At the same time, for noisy and incomplete side information, we set the decay function so that the DD-CRP reduces to the traditional Chinese restaurant process, thus not inducing side effects of noisy and incomplete side information. Experimental evaluations on two real-world datasets NUS WIDE and 20 Newsgroups show exploiting side information in DD-CRP significantly improves the clustering performance.

A new interval-based method for handling non-monotonic information

The focus of this paper is on handling non-monotone information in the modelling process of a single-input target monotone system. On one hand, the monotonicity property is a piece of useful prior (or additional) information which can be exploited for modelling of a monotone target system. On the other hand, it is difficult to model a monotone system if the available information is not monotonically-ordered. In this paper, an interval-based method for analysing non-monotonically ordered information is proposed. The applicability of the proposed method to handling a non-monotone function, a non-monotone data set, and an incomplete and/or non-monotone fuzzy rule base is presented. The upper and lower bounds of the interval are firstly defined. The region governed by the interval is explained as a coverage measure. The coverage size represents uncertainty pertaining to the available information. The proposed approach constitutes a new method to transform non-monotonic information to interval-valued monotone system. The proposed interval-based method to handle an incomplete and/or non-monotone fuzzy rule base constitutes a new fuzzy reasoning approach.