The complexity of the group discussion
The group discussion is complicated due to the following reasons: First, the size of the group that has 250 members. The higher number of members increases the cost of coordination and more schedules for accommodation. The opinions of the group members render the group complex. This is due to the large number of members who have different ideas, and each member needs to listen. This is time-consuming and makes the group complex. Second, the task interdependence in the group makes it complicated. This means that all the different tasks by the members are interdependent, and the success of the group is based on the proper execution of such duties.
Difficulties of getting a consensus with high experts
The top experts encourage the diffusion of responsibility as compared to non-experts. This leads to a lack of accountability for the expert group members. That is, in case of problems created during reaching of consensus, there will be no individual responsible for such issues. This creates an atmosphere in which each one top expert blames the other for the problems created (Bui et al. 2017).
The diffusion of responsibilities leads to lower efficiency. This occurs where the top experts fail to reach a consensus on best the best form of action to take. High time consuming is witnessed in a case where top experts are involved in a group. This is because each expert thinks he or she is right, and his or her ideas should be considered when reaching a consensus. On the other hand, non-experts will quickly reach an agreement due to the limited knowledge of their responsibilities. They work together to come up with the best results.
The contribution of swarm AI
Swarm AI connects individuals in an actual-time system that is modeled after nature. Swarm AI helps individuals to come up with better decisions. This was achieved where the intelligence facilitated broad expression beyond the intuition and more than what can be verbally expressed by individuals. It functions similarly to the swarms of bees. It maximizes the ability of the group to reason together and converge on an optimal system. It helps in developing algorithms that permit individuals to make decisions together. This is achieved via the distribution of architecture by the organization that allows the group the scatter around while logging the swarming platform (Zhao et al. 2019).
The group has been able to take part in the actual-time closed-loop intelligence. The closed-loop data moderated the artificial intelligence algorithms enabling the group to come up with better decisions. The swarm artificial intelligence creates a room for asking questions that are answered collectively by the group members. The algorithm helped in making accurate predictions. This indicates that swarm artificial intelligence contributed to coming up with better decisions. That is, through the swarm artificial intelligence, the group made the right decisions and accurate predictions.
Comparison between simple voting and swarm AI
First, a few duties are challenges for simple voting compared to swarm AI voting. Simple voting involves counting of votes, which might result in various errors as compared to the swarm AI voting. Simple voting may be bias compared to the swarm voting. This happens where the party in charge of the voting is in favor of one group. On the other hand, the swarm artificial intelligence is computer-based and cannot be biased. Simple voting can result in conflicts among the group members. This occurs where the members feel that voting is biased and has favored one group. The disputes that arise may lead to persistent disagreements that influence the groups into not reaching a consensus (Zhao et al. 2019). The similarity between the two types of voting is that they both aim at making a decision.