A map of decoy influence in human multialternative choice

Significance Imagine you are deciding between two goods: A is simple but inexpensive, B is luxurious but more costly. Introducing a less advantageous option (e.g., lower quality than A, same price) should not alter your choice between A and B. However, this principle is often violated; three classic biases known as “decoy effects” have been identified, each describing a stereotyped choice pattern in the presence of irrelevant information. Through behavioral testing in human participants and computer simulations, we show that these decoy effects are special cases of a wider principle, whereby stimulus value information is encoded in a relative, rather than an absolute, format. This work clarifies the origin of three behavioral phenomena that are widely studied in psychology and economics.
Human decisions can be biased by irrelevant information. For example, choices between two preferred alternatives can be swayed by a third option that is inferior or unavailable. Previous work has identified three classic biases, known as the attraction, similarity, and compromise effects, which arise during choices between economic alternatives defined by two attributes. However, the reliability, interrelationship, and computational origin of these three biases have been controversial. Here, a large cohort of human participants made incentive-compatible choices among assets that varied in price and quality. Instead of focusing on the three classic effects, we sampled decoy stimuli exhaustively across bidimensional multiattribute space and constructed a full map of decoy influence on choices between two otherwise preferred target items. Our analysis reveals that the decoy influence map is highly structured even beyond the three classic biases. We identify a very simple model that can fully reproduce the decoy influence map and capture its variability in individual participants. This model reveals that the three decoy effects are not distinct phenomena but are all special cases of a more general principle, by which attribute values are repulsed away from the context provided by rival options. The model helps us understand why the biases are typically correlated across participants and allows us to validate a prediction about their interrelationship. This work helps to clarify the origin of three of the most widely studied biases in human decision-making.