Exploring Monte Carlo Negotiation Search with Nontrivial Agreements

The application of automated negotiations to general game playing is a research area with far-reaching implications. Non-zero sum games can be used to model a wide variety of real-world scenarios and automated negotiation provides a framework for more realistically modeling the behavior of agents in these scenarios. A particular recent development in this space is the Monte Carlo Negotiation Search (MCNS) algorithm, which can negotiate to find valuable cooperative strategies for a wide array of games (such as those of the Game Description Language). However, MCNS only proposes agreements corresponding to individual sequences of moves without any higher-level notions of conditional or stateful strategy. Our work attempts to lift this restriction. We present two contributions: extensions to the MCNS algorithm to support more complex agreements and an agreement language for GDL games suitable for use with our algorithm. We also present the results of a preliminary experiment in which we use our algorithm to search for an optimal agreement for the iterated prisoners dilemma. We demonstrate significant improvement of our algorithm over random agreement sampling, although further work is required to more consistently produce optimal agreements.

Same but Different: A Comparison of Players’ Perceptions and Motivational Factors in Two Commercially Available AR Games

In 2019, the Harry Potter-themed augmented reality (AR) game Wizards Unite was released, one which employed the same game mechanics as Pokémon Go. In order to explore the similarities and differences in players’ perceptions, motivations to play, and missing functions in Wizards Unite and Pokémon Go, we conducted a web-based survey which targeted active players of Wizards Unite 14 weeks after its release. The results are compared to a survey applying the same methodology and questions directed at active players of Pokémon Go. An important reason why many players started playing Wizards Unite was that they were already Harry Potter fans. For Wizards Unite players the motivation stemming from public attention was lower compared to that for Pokémon Go players. The main motivating factors drawing players to these games include fun, curiosity, and being physically active. The AR function was not mentioned as a motivating factor for either game. The aspect of players being able to collect and complete the game was more of a motivation for playing Pokémon Go. Although the game mechanisms are similar, we identified specific differences in the motivational factors of the two games. The main difference is the misfit between the general game story with hunting and collecting within the Harry Potter universe. Nevertheless, the theme of the game and its background story can be important long-term motivating factors of Wizards Unite. The findings in this paper provide insights into how one can create AR games that meet players’ needs.

The Econometrics of Static Games

This article reviews the econometrics of static games, with a focus on discrete-choice cases. These models have been used to study a rich variety of empirical problems, ranging from labor force participation to entry decisions. We outline the components of a general game and describe the problem of doing robust inference in the presence of multiple solutions, as well as the different econometric approaches that have been applied to tackle this problem. We then describe the specific challenges that arise in different variations of these models depending on whether players are assumed to have complete or incomplete information, as well as whether or not nonequilibrium play is allowed. We describe the results in 2 × 2 games (the most widely studied games in econometrics), and we present extensions and recent results in games with richer action spaces. Areas for future research are also discussed.