Conversation Games and a Strategic View of the Turing Test

TL;DR

This paper introduces the conversation game, a strategic model of linguistic interaction, demonstrating its relevance to processes like the Turing test and showing that strategic agents outperform naive ones through simulations.

Contribution

It presents a novel game-theoretic framework for language-based interactions, including the Turing test as a special case, with empirical simulations validating the approach.

Findings

Strategic agents outperform naive agents in the conversation game.

The Turing test can be modeled as a verdict game within this framework.

Simulation results show the practical relevance of the proposed model.

Abstract

Although many game-theoretic models replicate real interactions that often rely on natural language, explicit study of games where language is central to strategic interaction remains limited. This paper introduces the \emph{conversation game}, a multi-stage, extensive-form game based on linguistic strategic interaction. We focus on a subset of the games, called verdict games. In a verdict game, two players alternate to contribute to a conversation, which is evaluated at each stage by a non-strategic judge who may render a conclusive binary verdict, or a decision to continue the dialogue. The game ends once a limit is reached or a verdict is given. We show many familiar processes, such as interrogation or a court process fall under this category. We also, show that the Turing test is an instance of verdict game, and discuss the significance of a strategic view of the Turing test in the age of advanced AI deception. We show the practical relevance of the proposed concepts by simulation experiments, and show that a strategic agent outperforms a naive agent by a high margin.