In an earlier post we have seen basic framework behind the Colonel Blotto game. We have assumed both warlords are equal in their strength, and that the castles are identical and of equal value. So, there are at least two obvious ways to generalize the game. In this post let us consider what happens when warlords differ in their troop count. Does the weaker warlord even have a chance? How can the stronger warlord make use of their superior strength?

In an earlier post, we invited you to coordinate presidential campaign within a simple web game. We have also mentioned that the framework of the web game relates to game theory and the Colonel Blotto game. Let us turn our attention for the next few posts to this classic game. As we will see it contains rich strategic landscape, and has numerous practical applications in various fields, including political campaigning (as previously discussed), warfare (as suggested by its original context), marketing, and even sports.

Here, in this post, will start by discussing the original formulation of the game.

In a few recent posts we have looked at the DeGroot model and its generalizations. In those posts I have mentioned that while the models are somewhat old and could be considered to be classical, they have resurfaced recently in the opinion dynamics literature.

This recent Master thesis is an example of a study conducted using the DeGroot model. Its novelty lies in an exploration of in-group and out-group biases within the framework of the DeGroot model. We invite you to watch the recording of the defense presentation shared by IFISC.

Another model I have seen recently featured a lot in the literature is the Friedkin-Johnsen model. This model is a generalization of the DeGroot model we have discussed earlier, but it adds a novel twist which allowed it to become a ground-breaking model on its own [1]. Namely, it has added stubbornness into the DeGroot model [2].

While looking through the recent opinion dynamics literature I have started noticing papers exploring various extensions of the DeGroot model [1]. Prior to those papers I haven't even heard or paid much attention to it. So I felt a bit curious.

In the previous post we have talked about the core ideas in the DeGroot model. In this post let me show how wisdom of the crowd effect emerges in the model.