Is Knowledge Power? On the (Im)possibility of Learning from Strategic Interactions
Nivasini Ananthakrishnan, Nika Haghtalab, Chara Podimata, Kunhe Yang

TL;DR
This paper investigates whether agents in strategic environments can learn to achieve their optimal outcomes through repeated interactions, revealing that information asymmetry often prevents uninformed players from reaching their Stackelberg value.
Contribution
The study introduces a game-theoretic framework analyzing learning algorithms in strategic interactions, highlighting limitations in achieving optimal outcomes under information asymmetry.
Findings
Perfect knowledge by one player leads to persistent informational gaps.
No PNE guarantees partially informed players achieve Stackelberg value.
Repeated interactions alone are insufficient for uninformed players to learn optimal strategies.
Abstract
When learning in strategic environments, a key question is whether agents can overcome uncertainty about their preferences to achieve outcomes they could have achieved absent any uncertainty. Can they do this solely through interactions with each other? We focus this question on the ability of agents to attain the value of their Stackelberg optimal strategy and study the impact of information asymmetry. We study repeated interactions in fully strategic environments where players' actions are decided based on learning algorithms that take into account their observed histories and knowledge of the game. We study the pure Nash equilibria (PNE) of a meta-game where players choose these algorithms as their actions. We demonstrate that if one player has perfect knowledge about the game, then any initial informational gap persists. That is, while there is always a PNE in which the informed…
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Taxonomy
TopicsCritical Realism in Sociology · Education and Critical Thinking Development
MethodsFocus
