The Persistence of "Bad" Precedents and the Need for Communication: A Coordination Experiment

Precedents can facilitate successful coordination within groups by reducing strategic uncertainty, but they may lead to coordination failure when two groups with diverging precedents have to interact. This paper describes an experiment to explore how such coordination failure can be mitigated and whether subjects are aware of it. In an initial phase, groups were able to establish a precedent in a repeated weakest-link game, and in a second phase two groups with dierent precedents are merged into a larger group. As expected, this leads to coordination failures. Unlike most of the previous literature, subjects could endogenously choose to communicate in the merged group for a small fee. The results suggest that communication can mitigate the coordination failure in the merged group and, in most cases, leads to efficient coordination. However, subjects in particular from groups with an efficient precedent in the initial phase are inattentive to the potential coordination failure and choose not to communicate. This can have profound consequences since groups who fail to implement communication are unable to achieve efficient coordination in the second phase. The results may be useful for the understanding of how groups learn to solve coordination problems from past coordination success or failure.coordination, precedent, costly communication, cheap talk

Data Mining for Gene Networks Relevant to Poor Prognosis in Lung Cancer Via Backward-Chaining Rule Induction

We use Backward Chaining Rule Induction (BCRI), a novel data mining method for hypothesizing causative mechanisms, to mine lung cancer gene expression array data for mechanisms that could impact survival. Initially, a supervised learning system is used to generate a prediction model in the form of “IF <conditions> THEN <outcome>” style rules. Next, each antecedent (i.e. an IF condition) of a previously discovered rule becomes the outcome class for subsequent application of supervised rule induction. This step is repeated until a termination condition is satisfied. “Chains” of rules are created by working backward from an initial condition (e.g. survival status). Through this iterative process of “backward chaining,” BCRI searches for rules that describe plausible gene interactions for subsequent validation. Thus, BCRI is a semi-supervised approach that constrains the search through the vast space of plausible causal mechanisms by using a top-level outcome to kick-start the process. We demonstrate the general BCRI task sequence, how to implement it, the validation process, and how BCRI-rules discovered from lung cancer microarray data can be combined with prior knowledge to generate hypotheses about functional genomics

Application of Advanced Early Warning Systems with Adaptive Protection

This project developed and field-tested two methods of Adaptive Protection systems utilizing synchrophasor data. One method detects conditions of system stress that can lead to unintended relay operation, and initiates a supervisory signal to modify relay response in real time to avoid false trips. The second method detects the possibility of false trips of impedance relays as stable system swings “encroach” on the relays’ impedance zones, and produces an early warning so that relay engineers can re-evaluate relay settings. In addition, real-time synchrophasor data produced by this project was used to develop advanced visualization techniques for display of synchrophasor data to utility operators and engineers

IDENTIFICATION OF CHRONIC KIDNEY DISEASE USING NAIVE BAYES, ADABOOST, AND RANDOM FOREST LEARNING METHODS

Chronic kidney disease is a decrease in function in the kidneys where the condition leads to kidney damage. This disease causes damage to the body's immunity, because the body fails to maintain fluid balance. Therefore, it becomes a critical need to identify whether a patient is a sufferer of chronic kidney disease or not. The classification methods used in this study are Naive Bayes, AdaBoost, and Random Forest. Recently, proper early recognition is needed to detect chronic kidney disease to prevent delays in its treatment. Given the large number of chronic kidney disease cases that occur, this study is expected to be an effort to control the increase in sufferers. The results showed that the Naive Bayes approach achieved 95.4% accuracy, which increased to 98.6% after AdaBoost was implemented, and Random Forest led at 99.3%

Exploring the facets of overall job satisfaction through a novel ensemble learning

The aim of this work is to understand the relationship between the overall Job Satisfaction and the facet Job Satisfaction, using a comprehensive dataset of Italian Social Cooperatives workers. On this issue, recent works explored how ensemble learning like Random Forest and TreeBoost can be used to assess the importance of potential predictors in the Job Satisfaction. Taking a similar way, in this study we use a tailored data mining approach for hierarchical data, namely a new algorithm called CRAGGING, shedding some light about the drivers of Job Satisfaction. To this end we use a variable importance measure and then we grow a synthetic model to relate the overall Job Satisfaction with corresponding facets. In doing this we obtain a simple model with unambiguous results

The substance of interaction: design and policy implications of NGO- government projects in India

Collaboration between government and non-government organizations has been a recurrent feature of many development interventions in India. This paper is based on case studies of 11 such programs.Non-governmental organizations India., Government., India Economic development.,

Efficiency Gains from Team-Based Coordination: Large-Scale Experimental Evidence

The need for efficient coordination is ubiquitous in organizations and industries. The literature on the determinants of efficient coordination has focused on individual decision-making so far. In reality, however, teams often have to coordinate with other teams. We present an experiment with 825 participants, using six different coordination games, where either individuals or teams interact with each other. We find that teams coordinate much more efficiently than individuals. This finding adds one important cornerstone to the recent literature on the conditions for successful coordination. We explain the differences between individuals and teams using the experience weighted attraction learning model.coordination games, individual decision-making, team decision-making, experience-weighted attraction learning, experiment