The role of trust in relationship development and performance improvement

The importance of inter-organizational trust to project success has been increasingly highlighted in the construction industry. This study aims to explore the role of trust between project parties. It adopts a combination of quantitative and qualitative methodologies. Based on the analysis of the responses of a questionnaire survey, trust is demonstrated to have a significant contribution to the development of cooperative or collaborative relationships; fostering trust proves to have a major influence on the improvement of project performance; and some relationship and performance indicators are found to have closer associations with trust than others so that trust is more important to
the development of relationship and the improvement of performance in these aspects. The analysis of questionnaire responses also provides significant evidence for the reduction in monitoring and control following the increase of mutual trust. The questionnaire survey is followed by a series of expert interviews, both of which contribute to the establishment of a model that links trust with relationship and performance and distinguishes the new approach that is based on trust from the traditional mechanism that relies on monitoring and control.

Fusion of Static and Temporal Information for Threat Evaluation in Sensor Networks

In many CCTV and sensor network based intelligent surveillance systems, a number of attributes or criteria are used to individually evaluate the degree of potential threat of a suspect. The outcomes for these attributes are in general from analytical algorithms where data are often pervaded with uncertainty and incompleteness. As a result, such individual threat evaluations are often inconsistent, and individual evaluations can change as time elapses. Therefore, integrating heterogeneous threat evaluations with temporal influence to obtain a better overall evaluation is a challenging issue. So far, this issue has rarely be considered by existing event reasoning frameworks under uncertainty in sensor network based surveillance. In this paper, we first propose a weighted aggregation operator based on a set of principles that constraints the fusion of individual threat evaluations. Then, we propose a method to integrate the temporal influence on threat evaluation changes. Finally, we demonstrate the usefulness of our system with a decision support event modeling framework using an airport security surveillance scenario.

Rational Partial Choice Functions and Their Application to Belief Revision.

Necessary and sufficient conditions for choice functions to be rational have been intensively studied in the past. However, in these attempts, a choice function is completely specified. That is, given any subset of options, called an issue, the best option over that issue is always known, whilst in real-world scenarios, it is very often that only a few choices are known instead of all. In this paper, we study partial choice functions and investigate necessary and sufficient rationality conditions for situations where only a few choices are known. We prove that our necessary and sufficient condition for partial choice functions boils down to the necessary and sufficient conditions for complete choice functions proposed in the literature. Choice functions have been instrumental in belief revision theory. That is, in most approaches to belief revision, the problem studied can simply be described as the choice of possible worlds compatible with the input information, given an agent’s prior belief state. The main effort has been to devise strategies in order to infer the agents revised belief state. Our study considers the converse problem: given a collection of input information items and their corresponding revision results (as provided by an agent), does there exist a rational revision operation used by the agent and a consistent belief state that may explain the observed results?