In this talk, I will introduce the domain of distributed decision, and review some of the results and insights gathered during my PhD.

The underlying model of this study is the local model. The local model is defined to answer questions of the following type: given a communication network, whose nodes are machines, and edges are communication links, is it possible that the nodes solve some task X, if they communicate only with the nodes that are close to them? A classic problem is colouring: can a node choose a colour, with only the knowledge of a small neighbourhood of the graph, such that the colours chosen by the nodes form a proper colouring of the graph? As in the centralized setting, it is interesting to study decision problems, that are yes-no questions, and to define complexity classes to classify these problems; this is distributed decision.

The complexity class we use as the equivalent of the class P in the centralized setting, is pretty small, and it is then natural to look at some form of non-determinism, to have a chance to understand more problems. In this model, non-determinism can be thought as a piece of global information that can be verified locally. The theoretical motivation is that to understand how local a problem is, one can ask how much global information is needed to solve it. The more practical motivation is that if one can design schemes with little global information then it can help to design more robust distributed algorithms such as self-stabilizing algorithms. The results I will present play with different natural notions of non-determinism, and how they influence the complexity classes defined.

I will spend time to carefully describe the model, thus no prior knowledge is needed.