Abstract
In human society, the majority of individuals try to achieve objectives that are individual to them, carrying this out, in the majority of cases, in an independent manner. It has in fact been repeat edly verified that some individuals are able to function as catalysts, introducing ideas and technology that go against the ideological currents that govern their social relationships. A variety of factors can be indicated as being partly responsible for this capacity for innovation and social adaptation, one of them being the perpetuation of collective memory by means of written texts, the press, and by all the different new types of media that we have today at our disposal, and which are used by certain individuals in determined contexts to obtain some type of advantage. What effect, however, would pre judice have when a pplied to strategies of interaction in a social network? Prejudice is something that is generally considered to be pejorative. Taking the free circulation of information amongst individuals within a social network as a basic premise, would it be possible to affirm that we would always achieve improved performance of society as a whole when the collective memory is loaded with prejudice? This work attempts to provide an answer to that question, analysing the relationship between the efficiency of a social network and the memories of the individuals who make it up, when the network itself is not immune from information that is (potentially) incorrect.

Abstract
This paper aims at develop an evaluation of the functional polycentrism of Portuguese municipalities. For that purpose, we developed a critical assessment of the concepts of polycentrism and functional polycentrism whose assumptions guide most of the policies of regional planning, and present the main methodologies for its evaluation. We then proceed with the evaluation of polycentrism in Portugal, by using Social Network Analysis and Cluster Analysis. The study considers the variable "commuting flows home-to-work" and the results suggest that, from 1991 to 2001, the Portuguese urban system presents a network of commuting denser, less centralized, more dispersed and more clustered.

Abstract
This paper aims at evaluate the impact of imitation networks on organizations' survival rates within a Portuguese industrial cluster. We used a Multi-Agent framework to represent the industrial cluster, its firms and the rules underlying the imitation strategies. Several experiments were based on the density dependence model, where vital rates are related with the size of the population (population density). We have concluded that imitation seems to improve the vital dynamics of the population and that present information about a firm is enough to establish an imitation network.

Abstract
We propose a Multi-Agent framework to analyze the dynamics of organizational survival in cooperation networks. Firms can decide to cooperate horizontally (in the same market) or vertically with other firms that belong to the supply chain. Cooperation decisions are based on economic variables. We have defined a variant of the density dependence model to set up the dynamics of the survival in the simulation. To validate our model, we have used empirical outputs obtained in previous studies from the automobile manufacturing sector. We have observed that firms and networks proliferate in the regions with lower marginal costs, but new networks keep appearing and disappearing in regions with higher marginal costs.

Abstract
An increasing area of study for economists and sociologists is the varying organizational structures between business networks. The use of network science makes it possible to identify the determinants of the performance of these business networks. In this work we look for the determinants of inter-firm performance. On one hand, a new method of supervised clustering with attributed networks is proposed, SUWAN, with the aim at obtaining class-uniform clusters of the turnover, while minimizing the number of clusters. This method deals with representative-based supervised clustering, where a set of initial representatives is randomly chosen. One of the innovative aspects of SUWAN is that we use a supervised clustering algorithm to attributed networks that can be accomplished through a combination of weights between the matrix of distances of nodes and their attributes when defining the clusters. As a benchmark, we use Subgroup Discovery on attributed network data. Subgroup Discovery focuses on detecting subgroups described by specific patterns that are interesting with respect to some target concept and a set of explaining features. On the other hand, in order to analyze the impact of the network's topology on the group's performance, some network topology measures, and the group total turnover were exploited. The proposed methodologies are applied to an inter-organizational network, the EuroGroups Register, a central register that contains statistical information on business networks from European countries.

Abstract
Sponsored advertising on search engines is one of the fastest growing online advertising marketplaces. The space available for paid ads, or positions, is sold using auctions and payment is calculated considering the number of clicks each position receives. Two mechanisms are generally used in position auctions: Generalized Second Price (GSP) (e.g. Google, Yahoo!) and Vickrey-Clarke-Groves (VCG) (e.g. Facebook). To understand which mechanism guarantees the highest payoff to market players (search engines and advertisers), a multi-agent simulation is developed in Netlogo. Using the generated data, a supervised learning-based analysis on search engines and bidders' payoffs is made using linear regression models and regression trees. Results suggest that the average payoff for auctioneers (the search engines) and bidders (the advertisers), the price for each position, and first bidder's payment, are significantly different in the GSP and VCG mechanisms. We also found the mechanism that generates the highest payoff for the search engine is the VCG, while for the bidders it is the GSP.