Since the seminal paper by Tversky and Kahneman, the conjunction fallacy has
been the subject of multiple debates and become a fundamental challenge for
cognitive theories in decision-making. In this article, we take a rather
uncommon perspective on this phenomenon. Instead of trying to explain the
nature or causes of the conjunction fallacy (intensional definition), we
analyze its range of factual possibilities (extensional definition). We show
that the majority of research on the conjunction fallacy, according to our
sample of experiments reviewed which covers literature between 1983 and 2016,
has focused on a narrow part of the a priori factual possibilities, implying
that explanations of the conjunction fallacy are fundamentally biased by the
short scope of possibilities explored. The latter is a rather curious aspect of
the research evolution in the conjunction fallacy considering that the very
nature of it is motivated by extensional considerations.

通过分析多个实验的文献，我们发现大多数关于连词谬误的研究集中在某个狭窄的前提事实可能性上，暗示了连词谬误的解释在本质上被所研究的可能性的短视所限制。

分析联结谬误的事实

Analyizing the Conjunction Fallacy as a Fact

Cognitive theories for reasoning are about understanding how humans come to
conclusions from a set of premises. Starting from hypothetical thoughts, we are
interested which are the implications behind basic everyday language and how do
we reason with them. A widely studied topic is whether cognitive theories can
account for typical reasoning tasks and be confirmed by own empirical
experiments. This paper takes a different view and we do not propose a theory,
but instead take findings from the literature and show how these, formalized as
cognitive principles within a logical framework, can establish a quantitative
notion of reasoning, which we call plausibility. For this purpose, we employ
techniques from non-monotonic reasoning and computer science, namely, a solving
paradigm called answer set programming (ASP). Finally, we can fruitfully use
plausibility reasoning in ASP to test the effects of an existing experiment and
explain different majority responses.

本文使用非单调推理和计算机科学中的答案集编码（ASP）来形式化作为认知原则的文献发现，建立一个名为‘plausibility’的推理概念，并将其用于测试现有实验的效果并解释不同的多数响应。

个体人类推理的量化符号方法

A Quantitative Symbolic Approach to Individual Human Reasoning

Current AI systems lack several important human capabilities, such as
adaptability, generalizability, self-control, consistency, common sense, and
causal reasoning. We believe that existing cognitive theories of human decision
making, such as the thinking fast and slow theory, can provide insights on how
to advance AI systems towards some of these capabilities. In this paper, we
propose a general architecture that is based on fast/slow solvers and a
metacognitive component. We then present experimental results on the behavior
of an instance of this architecture, for AI systems that make decisions about
navigating in a constrained environment. We show how combining the fast and
slow decision modalities allows the system to evolve over time and gradually
pass from slow to fast thinking with enough experience, and that this greatly
helps in decision quality, resource consumption, and efficiency.

本文提出了一种基于快速 / 慢速求解器和元认知组件的通用架构，意在通过现有认知人类决策理论对 AI 系统的一些重要人类能力进行提升，并在此基础上尝试应用于基于有限环境下导航决策等方面，实验结果表明，这种决策策略组合可以大大提高系统的决策质量、资源消耗和效率。

在受限环境中结合快速和慢速思考实现类人和高效导航

Combining Fast and Slow Thinking for Human-like and Efficient Navigation in Constrained Environments

This paper proposes a research direction to advance AI which draws
inspiration from cognitive theories of human decision making. The premise is
that if we gain insights about the causes of some human capabilities that are
still lacking in AI (for instance, adaptability, generalizability, common
sense, and causal reasoning), we may obtain similar capabilities in an AI
system by embedding these causal components. We hope that the high-level
description of our vision included in this paper, as well as the several
research questions that we propose to consider, can stimulate the AI research
community to define, try and evaluate new methodologies, frameworks, and
evaluation metrics, in the spirit of achieving a better understanding of both
human and machine intelligence.

本研究提出借鉴认知理论以推进 AI 的研究方向，通过嵌入因果组件来获得适应性、推理能力、常识理解等人类决策能力，以期激发 AI 技术研究，进而更好地理解人类和机器智能。