The Data Science View: Can Simplicity Win Over Complexity?

Paula Parpart's research explores why sometimes simpler algorithms can outperform more complex algorithms. Since the 1970s, a rare point of agreement between Nobel Laureate Daniel Kahneman and prominent Max Planck director Gerd Gigerenzer has been that decision heuristics are an alternative to Bayesian rationality. In cognitive science and psychology, heuristics are decision making algorithms that follow a set of simple rules and deliberately ignore information in the input data. For example, when making real-world decisions such as choosing which coffee to buy or choosing which apartment to rent, there are potentially thousands of features that could play into the decision, but we usually do not have the time or memory capacity to use them all. In choosing between two apartments, instead of considering all available information sources such as proximity to work, proximity to schools, crime rates, neighbourhood sport facilities or market trends, a simple heuristic called "Take-The-Best" (Gigerenzer & Goldstein, 1996) would just rely on the first most important cue that is able to discriminate among the apartments, and ignore all other cues.