The wisdom of the crowd theory states that a group of people that don't talk to each other will be better at guessing the right answer or the outcome to an event compared to any individual expert. If they do start to share their opinions, the influence from basic exposure to other information and regular social influences will produce groupthink that destroys the wisdom of the crowd.
Research has questioned this model by showing how this might not be the case exactly. For example, according to polls and the general consensus for the outcome of the 2016 United States Presidential Election, the wisdom of the crowd said Hillary Clinton would win. Yet this wasn't the case.
Inaccurate Information
What went wrong? Wisdom of the crowd is really in the network. People need to be able to share information and talk to each other, then the crowd with get smarter overall. By sharing information, the accuracy of the decisions made by individuals in a group will increase. But it can also lead to charismatic "opinion leading" individuals whose inaccurate information can influence the group in the wrong direction. Accuracy doesn't win out if inaccurate information is propagated more strongly and widely.
This contradicts the original wisdom of the crowd theory. The research shows that if you let people talk to each other then they're influenced by other people's information and conform to what others are saying, rather than what they honestly think the right answer is. This has been demonstrated to occur in Asch conformity experiments:
Asch Conformity Experiment
Networking
The particular accuracy of an outcome or answer will depend on the networks formed between individuals. If people aren't very accurate on their own, when they talk to each other they can help improve their accuracy overall. Think of averages, if one guess is too low and the other too high, they might agree to somewhere in the middle and guess more accurately relative to the less accurate guess.
The thing about these networks that are formed is that they have to be egalitarian, meaning everyone has to have an equal influence to have their voices heard and influence everyone else. This produces a strong social learning effect overall and improves the quality of everyone's judgments in the group.
Exchanging Ideas
While exchanging ideas can help make everyone smarter, influential opinion leaders can derail that process and have people judge more poorly. A trusted opinion leader can be accurate in their area of expertise, but when they venture too far from it and are erroneous in their judgments, they continue be influential in the decisions other people make.
Study
The study had 1300 people in three different experimental conditions:
- egalitarian networks of equal contact and influence;
- centralized networks with a single opinion leader who obviously had more influence;
- a control group of no social networks.
Given estimation challenges, participants had 3 chances to guess the number of calories in a plate of food. The first response was completely on their own. Then the groups that had social networks could see the guesses of others and revise their own answer for a second-guess, and repeat again for a third and final guess. The control group had individuals guess all on their own.
Egalitarian Networked Groups
The control group's accuracy was true to the original wisdom of the crowd theory, but they didn't improve after they kept revising their answers, and some got worse. However, all of the egalitarian networked groups had the same initial wisdom of the crowd but also had an increase in accuracy after they started to network and share their answers.
"In a situation where everyone is equally influential people can help to correct each other's mistakes. This makes each person a little more accurate than they were initially. Overall, this creates a striking improvement in the intelligence of the group. The result is even better than the traditional wisdom of the crowd! But, as soon as you have opinion leaders, social influence becomes really dangerous."
The wisdom of the crowd in the egalitarian network is reliable because those who are more accurate usually make smaller revisions than those who are less accurate who make larger revisions to their guesses. The average of the group then moves towards those who are more accurate which ends up representing the overall wisdom of the crowd.
When scientists or engineers are trying to figure something out, they might think that avoiding contamination of the opinions of others is better for them to not get into groupthink, but they're likely to arrive at more accurate judgments by sharing and cooperating than by remaining independent.
With this new understanding, we can see how the classic theory of the wisdom of the crowds signaled that Hillary Clinton was going to win, when the opposite happened and Donald Trump won. Trump spoke of things plainly. He related to people who saw evident truths that they recognized the establishment wouldn't talk about honestly.
Trump as the opinion leader, and the networks of groups that they belonged to, influenced people's decisions. Despite not being an expert or knowing what he is talking about on certain things, the establishment underestimated how much influence Trump had on the topics he was right about. He resonated with a lot of Americans and gained their trust and their vote, winning the election.
Wisdom of the crowd was wrong about Hilary Clinton winning because the model lacked a better explanation for how crowd wisdom and group think work to push and pull people towards more or less accurate information. Speaking some truth and mixing in opinion can heavily impact people and influence their decisions. Also, false information that appeals to people or carries an aura or authority can influence them to spread it to others, making misinformation or disinformation go viral.
References:
- New findings refute groupthink, proving that wisdom of crowds can prevail
- Wisdom of the crowd
- Asch conformity experiments
- Joshua Becker el al., "Network dynamics of social influence in the wisdom of crowds," PNAS (2017). www.pnas.org/cgi/doi/10.1073/pnas.1615978114
Thank you for your time and attention. I appreciate the knowledge reaching more people. Peace.
If you appreciate and value the content, please consider: Upvoting, Sharing or Reblogging below.
me for more content to come!
My goal is to share knowledge, truth and moral understanding in order to help change the world for the better. If you appreciate and value what I do, please consider supporting me as a Steem Witness by voting for me at the bottom of the Witness page; or just click on the upvote button if I am in the top 50.