Memory loss can prevent chaos in games dynamics.

Recent studies have raised concerns on the inevitability of chaos in congestion games with large learning rates. We further investigate this phenomenon by exploring the learning dynamics in simple two-resource congestion games, where a continuum of agents learns according to a simplified experience-weighted attraction algorithm. The model is characterized by three key parameters: a population intensity of choice (learning rate), a discount factor (recency bias or exploration parameter), and the cost function asymmetry.

Entropy-Based Measure of Statistical Complexity of a Game Strategy.

In this note, we introduce excess strategic entropy-an entropy-based measure of complexity of the strategy. It measures complexity and predictability of the (mixed) strategy of a player. We show and discuss properties of this measure and its possible applications.

An equivalent condition for a sequence to be the sequence of partial entropies.

We show that a sequence is the sequence of partial entropies of some dynamical system with respect to a partition of the phase space if and only if it is non-negative, increasing, and has decreasing increments.