# Thinking Fast and Slow in AI

arXiv: https://arxiv.org/abs/2010.06002 · HTML: https://mlsanity.com/paper/2010.06002/

## Key facts

- Cited by 70 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2020.

## Built on by (most cited first, 30 of 70)

- [LightThinker: Thinking Step-by-Step Compression](https://mlsanity.com/paper/2502.15589/) (2025)
- [Large Language Models Are Not Strong Abstract Reasoners](https://mlsanity.com/paper/2305.19555/) (2023)
- [Towards Data-and Knowledge-Driven Artificial Intelligence: A Survey on Neuro-Symbolic Computing](https://mlsanity.com/paper/2210.15889/) (2022)
- [MORAL: Aligning AI with Human Norms through Multi-Objective Reinforced Active Learning](https://mlsanity.com/paper/2201.00012/) (2021)
- [Neurosymbolic Reinforcement Learning and Planning: A Survey](https://mlsanity.com/paper/2309.01038/) (2023)
- [Towards Cognitive AI Systems: a Survey and Prospective on Neuro-Symbolic AI](https://mlsanity.com/paper/2401.01040/) (2024)
- [DSGBench: A Diverse Strategic Game Benchmark for Evaluating LLM-based Agents in Complex Decision-Making Environments](https://mlsanity.com/paper/2503.06047/) (2025)
- [Learning by Self-Explaining](https://mlsanity.com/paper/2309.08395/) (2023)
- [Reasoning on a Spectrum: Aligning LLMs to System 1 and System 2 Thinking](https://mlsanity.com/paper/2502.12470/) (2025)
- [Neuro-symbolic Learning Yielding Logical Constraints](https://mlsanity.com/paper/2410.20957/) (2024)
- [Thinking Fast and Slow in AI: the Role of Metacognition](https://mlsanity.com/paper/2110.01834/) (2021)
- [The Instinctive Bias: Spurious Images lead to Illusion in MLLMs](https://mlsanity.com/paper/2402.03757/) (2024)
- [TELLER: A Trustworthy Framework for Explainable, Generalizable and Controllable Fake News Detection](https://mlsanity.com/paper/2402.07776/) (2024)
- [Symbolic Regression via Control Variable Genetic Programming](https://mlsanity.com/paper/2306.08057/) (2023)
- [Knowledge Engineering using Large Language Models](https://mlsanity.com/paper/2310.00637/) (2023)
- [Making Human-Like Trade-offs in Constrained Environments by Learning from Demonstrations](https://mlsanity.com/paper/2109.11018/) (2021)
- [Towards Efficient Neuro-Symbolic AI: From Workload Characterization to Hardware Architecture](https://mlsanity.com/paper/2409.13153/) (2024)
- [MADRA: Multi-Agent Debate for Risk-Aware Embodied Planning](https://mlsanity.com/paper/2511.21460/) (2025)
- [Combining Fast and Slow Thinking for Human-like and Efficient Navigation in Constrained Environments](https://mlsanity.com/paper/2201.07050/) (2022)
- [When Is It Acceptable to Break the Rules? Knowledge Representation of Moral Judgement Based on Empirical Data](https://mlsanity.com/paper/2201.07763/) (2022)
- [Describing and Organizing Semantic Web and Machine Learning Systems in the SWeMLS-KG](https://mlsanity.com/paper/2303.15113/) (2023)
- [Hierarchical Prompting Taxonomy: A Universal Evaluation Framework for Large Language Models Aligned with Human Cognitive Principles](https://mlsanity.com/paper/2406.12644/) (2024)
- [The Roles and Modes of Human Interactions with Automated Machine Learning Systems](https://mlsanity.com/paper/2205.04139/) (2022)
- [Sampling Through the Lens of Sequential Decision Making](https://mlsanity.com/paper/2208.08056/) (2022)
- [Pixels and Predictions: Potential of GPT-4V in Meteorological Imagery Analysis and Forecast Communication](https://mlsanity.com/paper/2404.15166/) (2024)
- [Disentangling Reasoning Capabilities from Language Models with Compositional Reasoning Transformers](https://mlsanity.com/paper/2210.11265/) (2022)
- [Knowledge-enhanced Neural Machine Reasoning: A Review](https://mlsanity.com/paper/2302.02093/) (2023)
- [Fast and Slow Planning](https://mlsanity.com/paper/2303.04283/) (2023)
- [Synergising Human-like Responses and Machine Intelligence for Planning in Disaster Response](https://mlsanity.com/paper/2404.09877/) (2024)
- [Intersymbolic AI: Interlinking Symbolic AI and Subsymbolic AI](https://mlsanity.com/paper/2406.11563/) (2024)

## Abstract (arXiv)

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.