The phenomenon of brain drain, which is the emigration of highly skilled people, has many undesirable effects, particularly for developing countries. In this study, an agent-based model is developed to understand the dynamics of such emigration. We hypothesize that skilled people's emigration decisions are based on several factors including the overall economic and social difference between the home and host countries, people's ability and capacity to obtain good jobs and start a life abroad, and the barriers of moving abroad. Furthermore, the social network of individuals also plays a significant role. The model is validated using qualitative and quantitative pattern matching with real-world observations. Sensitivity and uncertainty analyses are performed in addition to several scenario analyses. Linear and random forest response surface models are created to provide quick predictions on the number of emigrants and to understand the effect sizes of individual parameters. Overall, this study provides an abstract model where brain drain dynamics can be explored. Findings from the simulation outputs show that the future socioeconomic state of the country is more important than the current state, lack of barriers results in a large number of emigrants, and network effects ensure compounding effects on emigration. Upon further development and customization, future versions can assist in the decision-making of social policymakers regarding brain drain.

Deep-learning based computer vision models have proved themselves to be ground-breaking approaches to human activity recognition (HAR). However, most existing works are dedicated to improve the prediction accuracy through either creating new model architectures, increasing model complexity, or refining model parameters by training on larger datasets. Here, we propose an alternative idea, differing from existing work, to increase model accuracy and also to shape model predictions to align with human understandings through automatically creating higher-level summarizing labels for similar groups of human activities. First, we argue the importance and feasibility of constructing a hierarchical labeling system for human activity recognition. Then, we utilize the predictions of a black box HAR model to identify similarities between different activities. Finally, we tailor hierarchical clustering methods to automatically generate hierarchical trees of activities and conduct experiments. In this system, the activity labels on the same level will have a designed magnitude of accuracy and reflect a specific amount of activity details. This strategy enables a trade-off between the extent of the details in the recognized activity and the user privacy by masking some sensitive predictions; and also provides possibilities for the use of formerly prohibited invasive models in privacy-concerned scenarios. Since the hierarchy is generated from the machine’s perspective, the predictions at the upper levels provide better accuracy, which is especially useful when there are too detailed labels in the training set that are rather trivial to the final prediction goal. Moreover, the analysis of the structure of these trees can reveal the biases in the prediction model and guide future data collection strategies.

As organizations’ investments on information systems/information technology (IS/IT) increase, the assessment methods used during IS/IT investment decision-making process holds more and more importance. Since successful IS/IT projects are key to the sustainability of an organization, identifying the factors which have effects on project success carries useful insights. In this study, 18 assessment methods are identified based on the literature. A novel classification method is proposed and assessment methods are classified into financial, strategic, and organizational categories. A novel rule-based method for determining the size of IS/IT projects is also proposed. Detailed information on project characteristics, employed IS/IT assessment methods, and project success is collected for 110 real-world IS/IT projects. The collected data is utilized in ANOVA and Regression tests to examine the factors which affect project success. Use of organization-related assessment methods, which is proposed in this study, is found to increase the success rate of the projects. Obligation towards the project and use of multi-criteria methodology have significant relationships with project success whereas project size, use of gut feeling during evaluation, and employed system developmentmethodology do not have statistically significant impacts on project success.

This study proposes a novel network growth model named ComAwareNetGrowth which aims to mimic evolution of real-world social networks. The model works in discrete time. At each timestep, a new link (I) within-community or (II) anywhere in the network is created (a) between existing nodes or (b) between an existing node and a newcoming node, based on (i) random graph model, (ii) preferential attachment model, (iii) a triangle-closing model, or (iv) a quadrangle-closing model. Parameters control the probability of employing a particular mechanism in link creation. Experimental results on Karate and Caltech social networks shows that the model is able to mimic real-word social networks in terms of clustering coefficient, modularity, average path length, diameter, and power law exponent. Further experiments indicate that ComAwareNetGrowth model is able to generate variety of synthetic networks with different statistics.