We consider manipulations in the context of coalitional games, where a
coalition aims to increase the total payoff of its members. An allocation rule
is immune to coalitional manipulation if no coalition can benefit from internal
reallocation of worth on the level of its subcoalitions
(reallocation-proofness), and if no coalition benefits from a lower worth while
all else remains the same (weak coalitional monotonicity). Replacing additivity
in Shapley's original characterization by these requirements yields a new
foundation of the Shapley value, i.e., it is the unique efficient and symmetric
allocation rule that awards nothing to a null player and is immune to
coalitional manipulations. We further find that for efficient allocation rules,
reallocation-proofness is equivalent to constrained marginality, a weaker
variant of Young's marginality axiom. Our second characterization improves upon
Young's characterization by weakening the independence requirement intrinsic to
marginality.

在协作联盟游戏中，我们研究了协作联盟为了增加成员的总收益而进行的操纵行为。我们提出了一种新的 Shapley 值的基础，该值是唯一的高效和对联盟操纵免疫的分配规则，它将空置玩家分配为零，并满足弱协作单调性和次协作联盟再分配免疫性。此外，我们还发现对于高效的分配规则，次协作联盟再分配免疫性等价于约束边际性，这是 Young 的边际性公理的一种较弱变体。我们的第二个特征改进了 Young 的特征，从而减弱了边际性内在的独立性要求。

合作联盟的操纵和 Shapley 价值的免疫性

Coalitional Manipulations and Immunity of the Shapley Value

A variety of recent papers discuss the application of Shapley values, a
concept for explaining coalitional games, for feature attribution in machine
learning. However, the correct way to connect a machine learning model to a
coalitional game has been a source of controversy. The two main approaches that
have been proposed differ in the way that they condition on known features,
using either (1) an interventional or (2) an observational conditional
expectation. While previous work has argued that one of the two approaches is
preferable in general, we argue that the choice is application dependent.
Furthermore, we argue that the choice comes down to whether it is desirable to
be true to the model or true to the data. We use linear models to investigate
this choice. After deriving an efficient method for calculating observational
conditional expectation Shapley values for linear models, we investigate how
correlation in simulated data impacts the convergence of observational
conditional expectation Shapley values. Finally, we present two real data
examples that we consider to be representative of possible use cases for
feature attribution -- (1) credit risk modeling and (2) biological discovery.
We show how a different choice of value function performs better in each
scenario, and how possible attributions are impacted by modeling choices.

该研究论文讨论了在机器学习中解释协作游戏的 Shapley 值的应用，特别是在特征归因方面。作者提出了两种主要方法来连接机器学习模型和协同游戏，并讨论了如何在模型和数据之间进行选择。此外，他们提出了一个有效的方法来计算线性模型中 Shapley 值，并在两个实际的数据案例中研究了与特征属性相关的建模选择。

遵循模型还是遵循数据的真实性？

True to the Model or True to the Data?

Game theoretical techniques have recently become prevalent in many
engineering applications, notably in communications. With the emergence of
cooperation as a new communication paradigm, and the need for self-organizing,
decentralized, and autonomic networks, it has become imperative to seek
suitable game theoretical tools that allow to analyze and study the behavior
and interactions of the nodes in future communication networks. In this
context, this tutorial introduces the concepts of cooperative game theory,
namely coalitional games, and their potential applications in communication and
wireless networks. For this purpose, we classify coalitional games into three
categories: Canonical coalitional games, coalition formation games, and
coalitional graph games. This new classification represents an
application-oriented approach for understanding and analyzing coalitional
games. For each class of coalitional games, we present the fundamental
components, introduce the key properties, mathematical techniques, and solution
concepts, and describe the methodologies for applying these games in several
applications drawn from the state-of-the-art research in communications. In a
nutshell, this article constitutes a unified treatment of coalitional game
theory tailored to the demands of communications and network engineers.

介绍合作博弈理论的概念，分类讨论了基本组合博弈、联盟形成博弈和联盟图博弈三类，旨在帮助通信和网络工程师理解和分析联盟博弈理论的应用，以解决未来通信网络中节点的行为互动问题。