Our main result is a succinct counterpoint to Courcelle's meta-theorem as follows: every cw-nontrivial monadic second-order (MSO) property is either NP-hard or coNP-hard over graphs given by succinct representations. Succint representations are Boolean circuits computing the adjacency relation. Cw-nontrivial properties are those which have infinitely many models and infinitely many countermodels with bounded cliquewidth. Moreover, we explore what happens when the cw-nontriviality condition is dropped and show that, under a reasonable complexity assumption, the previous dichotomy fails, even for questions expressible in first-order logic.

In this article we investigate the computational complexity of predicting two dimensional freezing majority cellular automata with states { − 1 , + 1 } , where the local interactions are based on an L-shaped neighborhood structure. In these automata, once a cell reaches state + 1 , it remains fixed in that state forever, while cells in state − 1 update to the most represented state among their neighborhoods. We consider L-shaped neighborhoods, which mean that the vicinity of a given cell c consists in a subset of cells in the north and east of c . We focus on the prediction problem, a decision problem that involves determining the state of a given cell after a given number of time-steps. We prove that when restricted to the simplest L-shaped neighborhood, consisting of the central cell and its nearest north and east neighbors, the prediction problem belongs to NC , meaning it can be solved efficiently in parallel. We generalize this result for any L-shaped neighborhood of size two. On the other hand, for other L-shaped neighborhoods, the problem becomes P -Complete, indicating that the problem might be inherently sequential.

We prove general complexity lower bounds on automata networks, in the style of Rice’s theorem, but in the computable world. Our main result is that testing any fixed first-order property on the dynamics of an automata network is either trivial, or NP-hard, or coNP-hard. Moreover, there exist such properties that are arbitrarily high in the polynomial-time hierarchy. We also prove that testing a first-order property given as input on an automata network (also part of the input) is PSPACE-hard. Besides, we show that, under a natural effectiveness condition, any nontrivial property of the limit set of a nondeterministic network is PSPACE-hard. We also show that it is PSPACE-hard to separate deterministic networks with a very high and a very low number of limit configurations; however, the problem of deciding whether the number of limit configurations is maximal up to a polynomial quantity belongs to the polynomial-time hierarchy.

Boolean automata networks (BANs) are a generalisation of Boolean cellular automata. In such, any theorem describing the way BANs compute information is a strong tool that can be applied to a wide range of models of computation. In this paper we explore a way of working with BANs which involves adding external inputs to the base model (via modules), and more importantly, a way to link networks together using the above mentioned inputs (via wirings). Our aim is to develop a powerful formalism for BAN (de)composition. We formulate three results: the first one shows that our modules/wirings definition is complete; the second one uses modules/wirings to prove simulation results amongst BANs; the final one expresses the complexity of the relation between modularity and the dynamics of modules.

Since their introduction in the 80s, sandpile models have raised interest for their simple definition and their surprising dynamical properties. In this survey we focus on the computational complexity of the prediction problem, namely, the complexity of knowing, given a finite configuration c and a cell x in c, if cell x will eventually become unstable. This is an attempt to formalize the intuitive notion of “behavioral complexity” that one easily observes in simulations. However, despite many efforts and nice results, the original question remains open: how hard is it to predict the two-dimensional sandpile model of Bak, Tang and Wiesenfeld?

We consider a partitioning problem for grid graphs with special constraints: a (square) grid graph as well as a number of colors is given, a solution is a coloring approximatively assigning the same number of vertices to each color and such that the induced subgraph for each color is connected. In a "rooted" variant, a vertex to be included in the coloring for each color is specified as well. This problem has a concrete motivation in multimedia streaming applications. We show that the general problem is NP-complete. On the other hand, we define a reasonable easy subclass of grid graphs for which solutions always exist and can be computed by a greedy algorithm.