This paper aims to develop a new attribution method to explain the conflict
between individual variables' attributions and their coalition's attribution
from a fully new perspective. First, we find that the Shapley value can be
reformulated as the allocation of Harsanyi interactions encoded by the AI
model. Second, based the re-alloction of interactions, we extend the Shapley
value to the attribution of coalitions. Third we ective. We derive the
fundamental mechanism behind the conflict. This conflict come from the
interaction containing partial variables in their coalition.

从全新的角度，本文旨在开发一种新的归因方法，以解释个体变量的归属和联合归属之间的冲突。通过重新分配交互作用，我们将 Shapley 值扩展到联合归属，并推导出该冲突的基本机制，该冲突来自于联合中包含部分变量的交互作用。

追溯一个联盟的输入变量归因

Towards Attributions of Input Variables in a Coalition

Social dilemmas are situations in which collective interests are at odds with
private interests: pollution, depletion of natural resources, and intergroup
conflicts, are at their core social dilemmas.
Because of their multidisciplinarity and their importance, social dilemmas
have been studied by economists, biologists, psychologists, sociologists, and
political scientists. These studies typically explain tendency to cooperation
by dividing people in proself and prosocial types, or appealing to forms of
external control or, in iterated social dilemmas, to long-term strategies.
But recent experiments have shown that cooperation is possible even in
one-shot social dilemmas without forms of external control and the rate of
cooperation typically depends on the payoffs. This makes impossible a
predictive division between proself and prosocial people and proves that people
have attitude to cooperation by nature.
The key innovation of this article is in fact to postulate that humans have
attitude to cooperation by nature and consequently they do not act a priori as
single agents, as assumed by standard economic models, but they forecast how a
social dilemma would evolve if they formed coalitions and then they act
according to their most optimistic forecast. Formalizing this idea we propose
the first predictive model of human cooperation able to organize a number of
different experimental findings that are not explained by the standard model.
We show also that the model makes satisfactorily accurate quantitative
predictions of population average behavior in one-shot social dilemmas.

本文研究社会困境中人们的合作行为，提出人类天生具有合作的倾向，并建立了第一个能够预测人类合作行为的模型，该模型考虑到人们能够形成联盟来预测社会困境的演化，并根据最乐观的预测来行动。实验结果表明，即使在没有外部控制的情况下，人们也能够在一次性的社会困境中合作。

社会困境中的人类合作模型

A Model of Human Cooperation in Social Dilemmas

Nash equilibrium is the most commonly-used notion of equilibrium in game
theory. However, it suffers from numerous problems. Some are well known in the
game theory community; for example, the Nash equilibrium of repeated prisoner's
dilemma is neither normatively nor descriptively reasonable. However, new
problems arise when considering Nash equilibrium from a computer science
perspective: for example, Nash equilibrium is not robust (it does not tolerate
``faulty'' or ``unexpected'' behavior), it does not deal with coalitions, it
does not take computation cost into account, and it does not deal with cases
where players are not aware of all aspects of the game. Solution concepts that
try to address these shortcomings of Nash equilibrium are discussed.

本文介绍了博弈论中最常用的平衡概念 Nash 均衡存在的缺陷，从计算机科学角度考虑，解决方案概念试图解决 Nash 均衡的弱点，例如不足的鲁棒性，处理联盟的不足，不考虑计算成本以及游戏中参与者不知晓所有方面的情况等。