Nowadays, people are increasingly interacting with others in social media environments where algorithms control the flow of social information they see. People's interactions with online algorithms may affect how they learn from others, with negative consequences including social misperceptions, conflict and the spread of misinformation.
On social media platforms, algorithms are mainly designed to amplify (放大) information that sustains engagement, meaning they keep people clicking on content and coming back to the platforms. There is evidence suggesting that a side effect of this design is that algorithms amplify information people are strongly biased (偏向的) to learn from. We call this information "PRIME", for prestigious, in-group, moral and emotional information.
In our evolutionary past, biases to learn from PRIME information were very advantageous: Learning from prestigious individuals is efficient because these people are successful and their behavior can be copied. Paying attention to people who violate moral norms is important because punishing them helps the community maintain cooperation. But what happens when PRIME information becomes amplified by algorithms and some people exploit (利用) algorithm amplification to promote themselves? Prestige becomes a poor signal of success because people can fake prestige on social media. News become filled with negative and moral information so that there is conflict rather than cooperation.
The interaction of human psychology and algorithm amplification leads to disfunction because social learning supports cooperation and problem-solving, but social media algorithms are designed to increase engagement. We call it functional mismatch. One of the key outcomes of functional mismatch is that people start to form incorrect perceptions of their social world, which often occurs in the field of politics. Recent research suggests that when algorithms selectively amplify more extreme political views, people begin to think that their political in-group and out-group are more sharply divided than they really are. Such "false polarization" might be an important source of greater political conflict.
So what's next? A key question is what can be done to make algorithms facilitate accurate human social learning rather than exploit social learning biases. Some research team is working on new algorithm designs that increase engagement while also punishing PRIME information. This may maintain user activity that social media platforms seek, but also make people's social perceptions more accurate.