In this note, I introduce a new framework called $n$-person general-sum games with partial information, in which boundedly rational players have only limited information about the game -- including actions, outcomes, and other players. For example, playing an actual game of chess is a game of partial information. To analyze these games, I introduce a set of new concepts and metrics for measuring the performance of players, with a focus on the interplay between human- and machine-based decision-making. Specifically, I introduce (i) gaming-proofness, which is a property of a mechanism that players cannot game from a practical perspective, and (ii) the Net Game Points (NGP) mechanism, which measures the success of a player's performance in a game, taking into account both the outcome of the game and the ``mistakes'' made during the game. The NGP mechanism provides a practicable way to assess game outcomes and can potentially be applied to a wide range of games, from poker and football to AI systems, organizations, and companies. To illustrate the concept, I apply the NGP mechanism to select chess games played between some of the world's top players, including the world champion.

翻译：本文提出了一种新的框架，即带有部分信息的n人一般和博弈，其中有限理性的参与者仅拥有关于博弈的有限信息——包括行动、结果及其他参与者。例如，在真实的国际象棋对局中，便属于一种部分信息博弈。为分析此类博弈，本文引入了一系列新概念与度量标准，用于衡量参与者的绩效表现，重点关注人类与机器决策之间的相互作用。具体而言，本文引入：(i) 博弈抗性——一种机制特性，使得参与者从实践角度无法操纵该博弈；(ii) 净博弈分机制——用于衡量参与者在博弈中的表现成功度，既考虑博弈结果，也兼顾对局中出现的“失误”。净博弈分机制提供了一种评估博弈结果的可行方法，并可广泛应用于从扑克、足球到人工智能系统、组织及公司等各类场景。为阐明该概念，本文将该机制应用于选取部分世界顶尖棋手（包括世界冠军）之间的国际象棋对局。