Bringing Order to the Cognitive Fallacy Zoo
In the eyes of a rationalist like Descartes or Spinoza, human reasoning is flawless, marching toward uncovering ultimate truth. A few centuries later, however, culminating in the work of Kahneman and Tversky, human reasoning was portrayed as anything but flawless, filled with numerous misjudgments, biases, and cognitive fallacies. With further investigations, new cognitive fallacies continually emerged, leading to a state of affairs which can fairly be characterized as the cognitive fallacy zoo! In this largely methodological work, we formally present a principled way to bring order to this zoo. We introduce the idea of establishing implication relationships (IRs) between cognitive fallacies, formally characterizing how one fallacy implies another. IR is analogous to, and partly inspired by, the fundamental concept of reduction in computational complexity theory. We present several examples of IRs involving experimentally well-documented cognitive fallacies: base-rate neglect, availability bias, conjunction fallacy, decoy effect, framing effect, and Allais paradox. We conclude by discussing how our work: (i) allows for identifying those pivotal cognitive fallacies whose investigation would be the most rewarding research agenda, and importantly (ii) permits a systematized, guided research program on cognitive fallacies, motivating influential theoretical as well as experimental avenues of future research.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Testing Quantum Models of Conjunction Fallacy on the World Wide Web
The 'conjunction fallacy' has been extensively debated by scholars in cognitive science and, in recent times, the discussion has been enriched by the proposal of modeling the fallacy using the quantum formalism. Two majo…
Analyizing the Conjunction Fallacy as a Fact
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 r…
Decision MakingThe LLM Fallacy: Misattribution in AI-Assisted Cognitive Workflows
The rapid integration of large language models (LLMs) into everyday workflows has transformed how individuals perform cognitive tasks such as writing, programming, analysis, and multilingual communication. While prior re…
Reason from Fallacy: Enhancing Large Language Models' Logical Reasoning through Logical Fallacy Understanding
Large Language Models (LLMs) have demonstrated good performance in many reasoning tasks, but they still struggle with some complicated reasoning tasks including logical reasoning. One non-negligible reason for LLMs' subo…
Logical FallaciesLogical ReasoningWhat's the Problem, Linda? The Conjunction Fallacy as a Fairness Problem
The field of Artificial Intelligence (AI) is focusing on creating automated decision-making (ADM) systems that operate as close as possible to human-like intelligence. This effort has pushed AI researchers into exploring…
Decision MakingFairness