Counterfactual explanations provide individuals with cost-optimal actions
that can alter their labels to desired classes. However, if substantial
instances seek state modification, such individual-centric methods can lead to
new competitions and unanticipated costs. Furthermore, these recommendations,
disregarding the underlying data distribution, may suggest actions that users
perceive as outliers. To address these issues, our work proposes a collective
approach for formulating counterfactual explanations, with an emphasis on
utilizing the current density of the individuals to inform the recommended
actions. Our problem naturally casts as an optimal transport problem.
Leveraging the extensive literature on optimal transport, we illustrate how
this collective method improves upon the desiderata of classical counterfactual
explanations. We support our proposal with numerical simulations, illustrating
the effectiveness of the proposed approach and its relation to classic methods.

我们的研究提出了一种集体方法来制定反事实解释，强调利用个体的当前密度来提供推荐行动，这种方法通过解决最优输运问题，在改进经典反事实解释的同时支持其提案，通过数值模拟验证了该方法的有效性及其与经典方法的关系。

通过最优传输实现的集体反事实解释

Collective Counterfactual Explanations via Optimal Transport

A domain-independent problem-solving system based on principles of Artificial
Life is introduced. In this system, DIAS, the input and output dimensions of
the domain are laid out in a spatial medium. A population of actors, each
seeing only part of this medium, solves problems collectively in it. The
process is independent of the domain and can be implemented through different
kinds of actors. Through a set of experiments on various problem domains, DIAS
is shown able to solve problems with different dimensionality and complexity,
to require no hyperparameter tuning for new problems, and to exhibit lifelong
learning, i.e. adapt rapidly to run-time changes in the problem domain, and do
it better than a standard non-collective approach. DIAS therefore demonstrates
a role for Alife in building scalable, general, and adaptive problem-solving
systems.

介绍了一种基于人工生命原理的领域无关问题解决系统 DIAS，该系统能够解决不同种类和复杂度的问题，且不需要对新问题进行超参数调整，并能展现终身学习适应能力和比传统的非集体方法更好的问题解决能力。因此，ALife 在构建可伸缩、通用和自适应问题解决系统方面具有重要作用。