Thanks to digital tools and AI, we have access to a large number of high-quality suggestions, but easy access to advice does not guarantee better decisions. In most professional and everyday contexts, the final decision still lies with a person, who must determine when to trust the adviser, when to rely on their own judgement and how to combine the two sources of information.
This aspect of our decision-making process was investigated in the study “Adaptive yet suboptimal integration of advice in decision-making”, published in the Nature Portfolio journal Communications Psychology and led by the University of Milano-Bicocca in collaboration with the University of Pavia.
The research group, composed of Joshua Zonca from Psychology, Alice Giampino from Statistics and Carlo Reverberi from Psychology at Milano-Bicocca, together with Paolo Cherubini from Behavioural Sciences at the University of Pavia, sought to measure how effectively people make use of the external advice they receive.
In the two experiments, 89 participants interacted with seven “artificial advisers”, each characterised by a different profile. Some advisers were better or worse than the participant, some were more overconfident or more cautious, and some expressed confidence in their answers more or less reliably. A final adviser was a kind of “double”, with characteristics similar to those of the participant.
The results show that people do not take the characteristics of advisers into sufficient account. Performance improves after receiving advice, but not as much as it could.
Joshua Zonca explains: «To understand the mechanism, we can use an analogy. Imagine that we have saved a small amount of money and want to invest it. We already have our own idea, but to avoid making mistakes, we ask two acquaintances for advice. The first is the classic show-off, always completely sure of himself, who tells us without hesitation: “Do this!”. The second is more cautious, but we know that he is often right: “I think it would be better to do something else”. To make the best decision, we should not simply listen to the person who speaks with greater conviction: we should consider how competent each person really is and how reliable their confidence is. In some cases, we should even give the advice we receive more weight than our initial opinion. And yet, this often does not happen.»
The study identifies two main causes. The first is an egocentric bias: decision-makers consistently give too much weight to their own judgement and underestimate that of the adviser. The second is the difficulty of moving from the general to the specific, namely of translating general knowledge about the quality of a source into concrete adjustments in individual decisions. It is like responding to the opinion of an adviser who is more expert than we are by saying to ourselves a little too often: “Yes, on average they know more than I do, but not in this particular case.” These difficulties persist even when people are explicitly informed about their own abilities and those of the adviser.
The gap from optimal performance was particularly large with the highest-quality advisers, precisely in the cases in which the advice could offer the greatest benefit.
«When designing AI-based decision-support systems, it is not enough to provide accurate and transparent advice: people must also be trained to use it consciously and helped to understand, decision by decision, how much weight to give each piece of advice,» concludes Carlo Reverberi.