The First Attribute Heuristic in Risky Decision-Making.

Behavioural science theorists suggest that people’s judge- ments and decisions are often determined by the use of heuristics (e.g., Gigerenzer & Gaissmaier, 2011; Tversky & Kahneman, 1974) rather than computational processing (e.g., computing probability and money) as assumed by expected utility theory (von Neumann & Morgenstern, 1947) and prospect theory (Tversky & Kahneman, 1992). Accordingly, we propose that people’s preferences are deter- mined by binary comparison on the first contextually available attri- bute. Thus, we argue for first attribute heuristic (FAH) where human decision-makers prefer the option with the higher value on the first contextually available attribute. We explored FAH in an experiment where participants had to choose between a hypothetical safe (high probability of winning a smaller monetary prize) and risky (low probability of winning a larger monetary prize) gambles. Congru- ent with FAH predictions, we found that participants chose the risky gamble relatively more when the first contextually available attribute was money than when the first contextually available attribute was probability. In contrast, participants chose the safe gamble when the
first contextually available attribute was probability.