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TL;DR#
Many studies assume that agents in strategic games can overcome uncertainty about others’ preferences and achieve optimal outcomes solely through repeated interactions. This paper challenges this assumption by focusing on the impact of information asymmetry in such settings. It investigates whether agents can attain the value of their Stackelberg optimal strategy through repeated interactions, considering scenarios with different levels of information asymmetry.
The paper employs a meta-game framework to analyze the repeated strategic interactions, where players’ actions are learning algorithms. It demonstrates that when one player has complete game knowledge, information gaps persist, and the uninformed player may never achieve their optimal outcome even with repeated interactions. However, when both players initially have uncertain knowledge, the situation is far more complex and depends heavily on the game’s structure.
Key Takeaways#
Why does it matter?#
This paper is crucial for researchers studying learning in strategic environments. It challenges the common assumption that repeated interactions alone enable uninformed players to achieve optimal outcomes. The findings highlight the persistence of information asymmetry even with repeated interactions, and provide a more nuanced understanding of learning dynamics, which is vital for developing effective learning algorithms and strategies in strategic settings.
Visual Insights#
This figure shows two game matrices, G1 and G2, used in Theorem 3.2 to demonstrate the unachievability of the Stackelberg value for player 2 under certain conditions. Each cell contains a pair of utilities representing the payoffs for Player 1 and Player 2 for a given combination of actions. The shaded cells highlight the action profiles that form the Stackelberg equilibria when Player 2 is the leader. The parameter γ depends on p*, the maximum precision of Player 2’s signal.
This table presents two example game matrices, G1 and G2, used in an example to illustrate a scenario where information asymmetry between players shifts during repeated interactions. Each cell contains the utilities for Player 1 (row player) and Player 2 (column player). The example highlights a case where a less-informed player can become more informed faster than a better-informed player, negating the initial information advantage.
Full paper#
