Between Theory and Uncertainty: Interpreting the World
Some researchers use algorithms provided by a software. Some adapt them to the issue being tackled. Someone else thinks about algorithms and studies their theoretical properties. "Algorithms are everywhere", says Sonia Petrone, Director of the PhD in Statistics at Bocconi. An algorithm is not just a method to solve a mathematical problem that cannot be solved analytically. It is a tool for facing the unknown. Stochastic algorithms come from physics: you ask a computer to generate data and use statistical techniques to estimate an unknown quantity that is otherwise impossible to calculate. Petrone began studying these methods in the early 90s when Alan Gelfand and Adrian Smith took the idea of algorithms that had been developed in the nuclear physics labs in Los Alamos, New Mexico and brought it to statistics.
"It was a revolution", Petrone recalls. "New computational tools allowed researchers to address and solve complex problems, paving the way for previously unimaginable applications. Today, algorithms are everywhere, from neuroscience to genetics. The challenge is their scalability to huge amounts of data. Increasingly complex data and models led to the growth of uncertainty: theoretical research is concerned with efficiency and 'goodness' of algorithms, and the reproducibility of the experiments". Another important topic is interconnection: complex applications require a network of local and international resources and partnerships. "Nowadays there is great emphasis on big data and business analytics. They are fascinating subjects, for certain, but statistics is the scientific language of uncertainty: it provides a broader look onto issues. That's the mission of a Phd in Statistics: to instill in young scholars the desire to participate in the great adventure of human knowledge".
Read the article by Emanuele Borgonovo on our lives with algorithms and how they enter the work of Bocconi researchers in various fields
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