Aplicação do Balanced Scorecard à área de investimentos de uma seguradora

Contabilidade e Gestão das Instituições Financeiras

Buying behavior and competition in the software market

In the actual world, the impact of the software buying decisions has a rising relevance in social and economic terms. This research tries to explain it focusing on the organizations buying decisions of Operating Systems and Office Suites for personal computers and the impact on the competition between incumbent and alternative players in the market in these software categories, although the research hypotheses and conclusions may extend to other software categories and platforms. We concluded that in this market beside brand image, product features or price, other factors could have influence in the buying choices. Network effect, switching costs, local network effect, lock-in or consumer heterogeneity all have influence in the buying decision, protecting the incumbent and making it difficult for the competitive alternatives, based mainly on product features and price, to gain market share to the incumbent. This happens in a stronger way in the Operating Systems category.

A MAP approach to evidence accumulation clustering

The Evidence Accumulation Clustering (EAC) paradigm is a clustering ensemble method which derives a consensus partition from a collection of base clusterings obtained using different algorithms. It collects from the partitions in the ensemble a set of pairwise observations about the co-occurrence of objects in a same cluster and it uses these co-occurrence statistics to derive a similarity matrix, referred to as co-association matrix. The Probabilistic Evidence Accumulation for Clustering Ensembles (PEACE) algorithm is a principled approach for the extraction of a consensus clustering from the observations encoded in the co-association matrix based on a probabilistic model for the co-association matrix parameterized by the unknown assignments of objects to clusters. In this paper we extend the PEACE algorithm by deriving a consensus solution according to a MAP approach with Dirichlet priors defined for the unknown probabilistic cluster assignments. In particular, we study the positive regularization effect of Dirichlet priors on the final consensus solution with both synthetic and real benchmark data.