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Abstract: A model for inventing new signals is introduced in the context of sender–receiver games with reinforcement learning. If the invention parameter is set to zero, it reduces to basic Roth–Erev learning applied to acts rather than strategies, as in Argiento et al. (Stoch. Process. Appl. 119:373–390, 2009). If every act is uniformly reinforced in every state it reduces to the Chinese Restaurant Process—also known as the Hoppe–Pólya urn—applied to each act. The dynamics can move players from one signaling game to another during the learning process. Invention helps agents avoid pooling and partial pooling equilibria.

The article discusses legal issues concerning the reliability of latent fingerprint individualization applying ACE-V methodology to simultaneous impressions. It is noted that when no single latent print has sufficient information to warrant a conclusion of individualization, latent print examiners sometimes aggregate information across multiple impressions. It is further noted that the so-called ACE-V method satisfies standards for reliability of scientific evidence.

We propose an "explanation scheme" for why the Gibbs phase average technique in classical equilibrium statistical mechanics works. Our account emphasizes the importance of the Khinchin-Lanford dispersion theorems. We suggest that ergodicity does play a role, but not the one usually assigned to it.