Behavioral dynamics and influence in networked coloring and consensus.

We report on human-subject experiments on the problems of coloring (a social differentiation task) and consensus (a social agreement task) in a networked setting. Both tasks can be viewed as coordination games, and despite their cognitive similarity, we find that within a parameterized family of social networks, network structure elicits opposing behavioral effects in the two problems, with increased long-distance connectivity making consensus easier for subjects and coloring harder. We investigate the influence that subjects have on their network neighbors and the collective outcome, and find that it varies considerably, beyond what can be explained by network position alone. We also find strong correlations between influence and other features of individual subject behavior. In contrast to much of the recent research in network science, which often emphasizes network topology out of the context of any specific problem and places primacy on network position, our findings highlight the potential importance of the details of tasks and individuals in social networks.

Behavioral experiments on biased voting in networks.

Many distributed collective decision-making processes must balance diverse individual preferences with a desire for collective unity. We report here on an extensive session of behavioral experiments on biased voting in networks of individuals. In each of 81 experiments, 36 human subjects arranged in a virtual network were financially motivated to reach global consensus to one of two opposing choices. No payments were made unless the entire population reached a unanimous decision within 1 min, but different subjects were paid more for consensus to one choice or the other, and subjects could view only the current choices of their network neighbors, thus creating tensions between private incentives and preferences, global unity, and network structure. Along with analyses of how collective and individual performance vary with network structure and incentives generally, we find that there are well-studied network topologies in which the minority preference consistently wins globally; that the presence of "extremist" individuals, or the awareness of opposing incentives, reliably improve collective performance; and that certain behavioral characteristics of individual subjects, such as "stubbornness," are strongly correlated with earnings.

Reliability of the long case.

OBJECTIVES: The use of long cases for summative assessment of clinical competence is limited by concerns about unreliability. This study aims to explore the reliability of long cases and how reliability is affected by supplementation with short cases. METHODS: We performed a statistical analysis of examinations held by the Royal Australasian College of Physicians in 2005 and 2006 to determine overall reliability and sources of variance in reliability according to candidate ability, case difficulty and inter-examiner differences. RESULTS: Scores for 546 long cases in 2005 and 773 long cases in 2006 were analysed. In 2006, 38% of the total variation in long case data was explained by variation in candidate ability, with other significant contributors to variance being candidate x case and candidate x examiner interactions. Similar figures were found for the 2005 examinations. A short case is less reliable than a long case, but when examiner time is taken into account, three short cases are as reliable as one long case. Any combination of short and long cases would require 4-5 hours of testing time in order to achieve dependability > 0.7. CONCLUSIONS: Long cases can be optimised for reliability but time limits their use as the sole tool in a high-stakes examination. Further examiner training, better case selection, or greater use of short cases would have minimal impact on reliability. Reliability can be improved by either increasing examination time or including additional methods of summative assessment, such as might be provided by workplace assessment.

Results from the fielding of the Bio-surveillance Analysis, Feedback, Evaluation and Response (B-SAFER) system in Albuquerque, New Mexico.

Public health authorities need a surveillance system that is sensitive enough to detect a disease outbreak early to enable a proper response. In order to meet this challenge we have deployed a pilot component-based system in Albuquerque, NM as part of the National Biodefense Initiative (BDI). B-SAFER gathers routinely collected data from healthcare institutions to monitor disease events in the community. We describe initial results from the deployment of the system for the past 6 months