As decision-making moves to larger groups, the process becomes more difficult because of participants’ personal relationships. A new algorithm overcomes these problems.
Painful group indecision may soon be at an end, thanks to a fascinating new RUDN University decision-making algorithm. If your organization has ever struggled to make company-wide decisions in a timely fashion, this could be fantastic news.
The psychology of the group sparked an entire field of study based on organizational thinking. Still, a new algorithm could go beyond any guiding principle for speeding up the process of making decisions. We already know artificial intelligence (AI) has the potential to bring out our best by facilitating insights from more points of data than the human mind can grasp and relieving us of the burden of managing day to day data.
Researchers used an algorithm based on mathematical decision theory and tested it using an example of the market at the outbreak of COVID. The model-assisted administration and sellers to agree on closing the market and the sums of compensation in just three steps.
According to the university, first, the experts were clusterized. Then, the team identified a cluster with the opinion that differed the most from the collective judgment. After that, such opinion was corrected. The iterations were repeated until all participants agreed on one solution. The methods of opinion correction were irrelevant from the mathematical point of view. The only factor that mattered was the unit negotiation cost, which is based on resources (time, money, etc.) that had to be spent to reach the desired result.
The problem with group decisions
Decision-making is an optimization task based on multiple criteria. As decision-making moves to larger groups, the process becomes more difficult because of participants’ personal relationships.
Reaching a consensus becomes more difficult as social dynamics come out to play, but the algorithm categorizes participants into clusters based on relationship strength — known as the robust optimization technique.
The algorithms are suited for problems and tasks sensitive to changes in initial data — here, the relationship between the participants and perceived levels of trust.
Decision-making within group dynamics
AI doesn’t make the decision. Rather, it optimizes the process to reduce friction and back and forth by clustering members of the group together and identifying the cluster with opinions most different from the group.
As each opinion drew closer to the group consensus, the algorithm identified the route costing the fewest resources and time investment. Researchers plugged in real-life examples of a fish market in Wuhan at the outbreak of COVID. They found that the resulting decision had the lowest negotiation cost of all other existing group decision models.
It would be wonderful to have a machine to tell you what to do, but the reality of that isn’t possible (or desirable in practical applications). Instead, having a decision-making system to help reduce unnecessary debate, back and forth, renegotiation, and other decision-making resources could help even large organizations reduce the overall time to decision in seasons of disruption.
How this might appear in business contexts
It would be wonderful to have a machine to tell you what to do, but the reality of that isn’t possible (or desirable in practical applications). Instead, having a decision-making system to help reduce unnecessary debate, back and forth, renegotiation, and other decision-making resources could help even large organizations reduce the overall time to decision in seasons of disruption.
