When making decisions or providing a judgment, individuals often seek and receive advice from others. They may ask a friend whether or not they should spend their holidays in Japan and how much they should plan to budget for such a stay. Advice taking is typically investigated in a judge-advisor system. The judge provides an initial judgment before being introduced to the advice of the advisor. Afterward, the judge provides their final judgment. In this symposium, we combine advances in advice-taking research, outlining new perspectives for the field.

The first contribution demonstrates that individual differences can affect whether and how individuals take advice, and how much these influences have been overlooked. The second contribution presents a meta-analysis on how advice taking varies across different study contexts and designs using a dual hurdle model. The third contribution compares advice taking based on aggregated versus non-aggregated advice from multiple advisors, investigating why aggregated advice is heeded more by judges. The last two contributions focus on advice taking from algorithms. The fourth contribution investigates algorithmic advice demonstrating that without explicit communication advice can shape competition and collaboration among individuals. Finally, the fifth contribution examines algorithmic and hybrid advice combining human and algorithmic advice, demonstrating no algorithm aversion but instead algorithm appreciation.