Abstract
In this work we analyze the evolving dynamics of different strategies of collaborative networks that emerge from the creation and diffusion of knowledge. An evolutionary economic approach is adopted by introducing decision rules that are applied routinely and an agent-based model is developed. Firms (the agents) can collaborate and create networks for research and development purposes. We have compared three collaboration strategies (A-peer-to-peer complementariness, B-concentration process and C-virtual cooperation networks) that were defined on the basis of literature and on empirical evidence. Strategies are introduced exogenously in the simulation. The aims of this paper are twofold: (i) to analyze the importance of the networking effects; and (ii) to test the differences among collaboration strategies. It was possible to conclude that profit is associated with higher stock of knowledge and with smaller network diameter. In addition, concentration strategies are more profitable and more efficient in transmitting knowledge through the network. These processes reinforce the stock of knowledge and the profit of the firms located in the centers of the networks.

Abstract

Abstract
The economic impact of fraud is wide and fraud can be a critical problem when the prevention procedures are not robust. In this paper we create a model to detect fraudulent transactions, and then use a classification algorithm to assess if the agent is fraud prone or not. The model (BOND) is based on the analytics of an economic network of agents of three types: individuals, businesses and financial intermediaries. From the dataset of transactions, a sliding window of rows previously aggregated per agent has been used and machine learning (classification) algorithms have been applied. Results show that it is possible to predict the behavior of agents, based on previous transactions. © 2018, Springer International Publishing AG, part of Springer Nature.

Abstract
Mobility and commuting in metropolitan areas are very expensive, highly polluted and time wasting. The Four Step Model (FSM) is the key model to analyze a Transportation Network. However, being the FSM a combination of several models, combining them in one model have rarely been applied. To deal with this problem an Agent-Based Model (ABM) is proposed. An ABM uses the metaphor of autonomous agents and so, they can be a handful tool for combining different models in one. Therefore, this model can be used as a tool for simulation and integrate the FSM in one model. Here we present the preliminary results of this approach. (C) 2014 The Authors. Published by Elsevier B.V.

Abstract
Detection of Patient Zero is an increasing concern in a world where fast international transports makes pandemia a Public Health issue and a social fear, in cases such as Ebola or H5N1. The development of a medical social network and data visualization information system, which would work as an interface between the patient medical data and geographical and/or social connections, could be an interesting solution, as it would allow to quickly evaluate not only individuals at risk but also the prospective geographical areas for imminent contagion. In this work we propose an ideal model, and contrast it with the status quo of present medical social networks, within the context of medical data visualization. From recent publications, it is clear that our model converges with the identified aspects of prospective medical networks, though data protection is a key concern and implementation would have to seriously consider it.

Abstract
In this work we aim at increasing the utility of a preelection poll, by improving the quality of the vote share estimates, both at macro and micro level. Three different methodologies are applied with that purpose: (1) polls aggregation, using existing auxiliary polling; (2) application of multilevel regression methods, using the multilevel structure of the data; and (3) methods of small area estimation, making use of auxiliary information through the application of the Empirical Best Linear Unbiased Prediction (EBLUP). These methods are applied to real data collected from a survey with the aim of estimating the vote share in the Portuguese legislative elections. When auxiliary information is required, we concluded that polls aggregations and EBLUP have to be applied with caution, since this information is extremely important for a good application of these models to the data set and to obtain good reliable forecasts. On the other hand, if auxiliary information is not available or if it is not of good quality, then multilevel regression can and should be seen as a safe alternative to obtain more precise estimates, either at the micro or macro level. Besides, this is the method which further improves the precision of the estimates. In the presence of good auxiliary information, EBLUP proved to be the method with greater proximity with real values. © Springer-Verlag Berlin Heidelberg 2013.