The field of game theory and collective intelligence is rapidly evolving, with a focus on developing new frameworks and models to explain complex behaviors and improve decision-making. Recent research has explored the role of value learning, iterated learning, and altruism in shaping outcomes in various games and scenarios. The development of new algorithms and methods, such as local entropy search and disc game dynamics, has also improved our ability to analyze and optimize complex systems. Noteworthy papers in this area include: Learning the Value of Value Learning, which extends the Jeffrey-Bolker framework to model refinements in values and proves a value-of-information theorem for axiological refinement. Lossy communication constrains iterated learning, which investigates the effect of incremental changes to communication abilities on iterated learning performance. Universality in Collective Intelligence on the Rubik's Cube, which finds evidence for universality in the collective learning of the Rubik's Cube in both sighted and blindfolded conditions. On Altruism and Spite in Bimatrix Games, which relaxes the self-interest assumption and initiates the study of algorithmic aspects of bimatrix games under altruism or spite. Disc Game Dynamics: A Latent Space Perspective on Selection and Learning in Games, which axiomatically derives a latent space representation for pairwise, symmetric, zero-sum games and demonstrates its usefulness for studying learning dynamics.