Do we advise as one likes? The alignment bias in social advice giving

Xitong Luo, Lei Zhang and Yafeng Pan

PLOS Computational Biology, 2025, vol. 21, issue 12, 1-29

Abstract: We often give advice to influence others, but could our own advice also be shaped by the very individuals we aim to influence (i.e., advisees)? This reverse flow of social influence—from those typically seen as being influenced to those who provide the influence—has been largely neglected, limiting our understanding of the reciprocal nature of human communications. Here, we conducted a series of experiments and applied computational modelling to systematically investigate how advisees’ opinions shape the advice-giving process. In an investment game, participants (n = 346, across four studies) provided advice either independently or after observing advisees’ opinions (Studies 1 & 2), with feedback on their advice (acceptance or rejection) provided by advisees (Studies 3 & 4). Our findings reveal that advisors tend to adjust their advice to align with the advisees’ opinions (we refer to this as the alignment bias) (Study 1). This tendency, which reflects normative conformity, persists even when advisors were directly incentivized to provide accurate advice (Study 2). As feedback is introduced, advisors’ behavior shifts in ways best captured by a reinforcement learning model, suggesting that advisees’ feedback drives adaptations in advice giving that maximize acceptance and minimize rejection (Study 3). This adaptation persisted even when acceptance is rare, as bolstered by the model-based evidence (Study 4). Collectively, our findings highlight advisors’ susceptibility to the consequence of giving advice, which can lead to counterproductive impacts on decision-making processes and misinformation exacerbation in social encounters.Author summary: Among the various forms of opinion exchange, advice stands out for its informational richness and its prevalence in word-of-mouth communication. Our research presents a counterintuitive view, suggesting that advice can be considerably biased—particularly by those receiving it (i.e., advisees). Advisors incline to align their opinions (advice) with those of their advisees (we refer to this as the alignment bias), even at the cost of compromising accuracy of their advice. By unraveling the advisors’ reactions to the acceptance/rejection from advisees using computational modeling, our data proposes an evolutionary perspective of how alignment bias emerges: advice-giving behavior can be shaped by advisees’ feedback (i.e., acceptance or rejection of advice). This nuanced bias, while understandable, can lead to poor decisions and spread inaccurate information. Zooming in, this susceptibility to the social outcomes of advice giving potentially leads to counterproductive decision-making and misinformation exacerbation. Zooming out, our work highlights a hidden social dilemma in everyday communication and shows how even well-meaning advice can become distorted by our need to connect with others.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1013732

DOI: 10.1371/journal.pcbi.1013732

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