We investigate the fair allocation of indivisible goods to agents with possibly different entitlements represented by weights. Previous work has shown that guarantees for additive valuations with existing envy-based notions cannot be extended to the case where agents have matroid-rank (i.e., binary submodular) valuations. We propose two families of envy-based notions for matroid-rank and general submodular valuations, one based on the idea of transferability and the other on marginal values. We show that our notions can be satisfied via generalizations of rules such as picking sequences and maximum weighted Nash welfare. In addition, we introduce welfare measures based on harmonic numbers, and show that variants of maximum weighted harmonic welfare offer stronger fairness guarantees than maximum weighted Nash welfare under matroid-rank valuations.

We present the first game characterization of contrasimilarity, the weakest form of bisimilarity. It corresponds to an elegant modal characterization of nested trees of impossible future behavior. The game is exponential but finite for finite-state systems and can thus be used for contrasimulation equivalence checking, of which no tool has been capable to date. By reduction from weak trace equivalence, we establish that contrasimilarity is PSPACE-complete. A machine-checked Isabelle/HOL formalization backs our work and enables further use of contrasimilarity in verification contexts.