Friday, June 22, 2012 - 9:00am
Electrical Engineering and Computer Science graduate student Joong Bum Rhim was awarded First Prize in the Student Paper Contest at the 7th IEEE Sensor Array and Multichannel Signal Processing Workshop in Hoboken, NJ, June 17-20. Rhim's paper titled "Benefits of Collaboration and Diversity in Teams of Categorically-Thinking Decision Makers" was co-authored with Lav Varshney of IBM T. J. Watson Research Center and Vivek Goyal. Rhim is a graduate student in Goyal's research group in the Research Laboratory of Electronics at MIT, and Varshney completed his PhD working with Goyal and Prof. Sanjoy Mitter in 2010.
Rhim's research uses the tools of signal processing and detection theory to study models for human decision making. When deciding between alternatives based on uncertain or noisy observations, people are known to act approximately as Bayesian decision makers; it is as if they assign costs to incorrect decisions and attempt to minimize the expected cost. Such a minimization gives a decision rule that depends on prior probabilities for each of the alternatives being correct. However, it is implausible for a human to use a different decision rule for each decision, as the previously-studied models require.
In Rhim's work, the natural human tendency to form categories for decision problems is identified with quantization of the prior probabilities. Rhim is able to obtain intriguing qualitative conclusions about decisions made by voting. For example, even when each vote counts equally and each voter has an observation with the same accuracy, the voters collectively make better decisions when they form their categories differently. Rhim and his colleagues interpret this as a mathematical justification for preferring diversity when forming teams and committees. Their earlier work showed that differences in preferences lead to a quantifiable penalty of team discord, even when the team shares the common goal of making correct decisions. They are working to draw other new conclusions from their unique way of modeling human behavior.