According to the traditional economic theory, markets are fully efficient and humans operate in them in a rational way. In the late 70s Daniel Kahneman and Amos Tversky started disproving this efficient markets hypothesis, contrasting the consistently logical Homo Economicus (Econ) they depicted, with the more realistic Human who takes decisions based on his questionable points of view. Doing so they gave birth to the study of the psychological factors involved in the making of these decisions, called Behavioral Economics.

The same flawed reasoning also impacts other fields like software engineering: we cannot behave as cold Econ when spending or investing our money, or as rational Engeen when coding. We are humans and this inevitably influences our choices.

The anchoring effect and the availability bias affect how we benchmark and evaluate the performances of our programs. The pro-innovation and bandwagon biases drive our technical decisions, making us to blindly follow hypes and gurus. The not-invented-here syndrome pushes us to create homemade tools instead of using de-facto standards. The framing effect makes us solving the same problem in different ways, depending on how it is presented.

During this talk we will go through these and other heuristics and shortcuts used by our brain, as found by behavioral economists in almost 50 years of research, and examine them in the context of software engineering, discussing their consequences on the quality of our work.

Devoxx

A conference closing talk by Mario Fusco exploring how cognitive biases from behavioral economics affect software engineers in their daily work. Covers five key biases: patternicity (seeing false patterns in bugs and benchmarks), anchoring effect (being anchored to first estimates or current performance baselines), sunk cost fallacy (continuing bad projects due to prior investment), temporal discounting (preferring quick fixes over long-term quality, causing software rot), and the framing effect combined with loss aversion (how problem framing influences technical decisions). Each bias is illustrated with real-world developer examples and paired with practical mitigation strategies like measuring everything, forming independent opinions, failing fast with prototypes, quantifying technical debt, and using consistent framing when comparing options.

Behavioral Software Engineering by Mario Fusco