Daniel S. Farenzena

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Social collaboration can benefit individuals by avoiding efforts and risks inherent of trial-and-error learning. However, social collaboration may demand considerable effort and time. We present a new model of social collaboration based on Informational Natural Selection in order to investigate social problem-solving. We performed a set of social network(More)
The Medical and Pharmaceutical industries have shown high interest in the precise engineering of protein hormones and enzymes that perform existing functions under a wide range of conditions. Proteins are responsible for the execution of different functions in the cell: catalysis in chemical reactions, transport and storage, regulation and recognition(More)
We propose a framework where humans and computers can collaborate seamlessly to solve problems. We do so by developing and applying a network model, namely Memenets, where human knowledge and reasoning are combined with machine computation to achieve problem-solving. The development of a Memenet is done in three steps: first, we simulate a machine-only(More)
Recently, the use of social and human computing has witnessed increasing interest in the AI community. However, in order to harness the true potential of social computing, human subjects must play an active role in achieving computation in social networks and related media. Our work proposes an initial desiderata for effective social computing, drawing(More)
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