Resumen
Using agents to represent decision-makers is a complex task. It is important that agents can understand the context and be more proactive. Here we propose a model and an algorithm that will allow the agent to analyse tendencies regarding the number of supporters for each alternative along the process. It is intended that agents can be more dynamic and intelligent and can evaluate different contexts throughout the decision-making process. We believe agents will achieve better and consensual decisions more easily. We tested our model in three simulation environments with different complexity levels. Our model proved that agents that use it will obtain higher average consensus and satisfaction levels. Besides that, agents using this model will obtain those higher consensus and satisfaction levels in most of the times compared to agents that do not use it.