Embedded Agency
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Abstract (arXiv)
Traditional models of rational action treat the agent as though it is cleanly separated from its environment, and can act on that environment from the outside. Such agents have a known functional relationship with their environment, can model their environment in every detail, and do not need to reason about themselves or their internal parts. We provide an informal survey of obstacles to formalizing good reasoning for agents embedded in their environment. Such agents must optimize an environment that is not of type "function"; they must rely on models that fit within the modeled environment; and they must reason about themselves as just another physical system, made of parts that can be modified and that can work at cross purposes.
Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Feb 2019.
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Citing papers per year: 2021: 3; 2022: 3; 2023: 2; 2024: 3; 2025: 4; 2026: 1.
16 papers.
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- 2022GPT-NeoX-20B: An Open-Source Autoregressive Language Modelcited by 711
- 2023Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervisioncited by 445
- 2025Absolute Zero: Reinforced Self-play Reasoning with Zero Datacited by 323
- 2021Goal Misgeneralization in Deep Reinforcement Learningcited by 122
- 2024Co-Supervised Learning: Improving Weak-to-Strong Generalization with Hierarchical Mixture of Expertscited by 28
- 2024On Goodhart's law, with an application to value alignmentcited by 9
- 2024Dynamic Normativity: Necessary and Sufficient Conditions for Value Alignmentcited by 3
- 2021Temporal Inference with Finite Factored Setscited by 2
- 2025Representation Learning on a Random Latticecited by 2
- 2021Induction, Popper, and machine learningcited by 1
- 2022Red Teaming with Mind Reading: White-Box Adversarial Policies Against RL Agentscited by 1
- 2023A Theory of Bounded Inductive Rationalitycited by 1
4 more
- 2025NP-Hard Lower Bound Complexity for Semantic Self-Verificationcited by 1
- 2025The World Is Bigger! A Computationally-Embedded Perspective on the Big World Hypothesiscited by 1
- 2022Arguments about Highly Reliable Agent Designs as a Useful Path to Artificial Intelligence Safetycited by 0
- 2026The Tao of Agency: Autotelic AI, Embedded Agency and Dissolution of the Selfcited by 0
- 2026The Tao of Agency: Autotelic AI, Embedded Agency and Dissolution of the Self18 Jun 2026
- 2025The World Is Bigger! A Computationally-Embedded Perspective on the Big World Hypothesis29 Dec 2025
- 2025Absolute Zero: Reinforced Self-play Reasoning with Zero Data6 May 2025
- 2025Representation Learning on a Random Lattice28 Apr 2025
- 2025NP-Hard Lower Bound Complexity for Semantic Self-Verification26 Jan 2025
- 2024On Goodhart's law, with an application to value alignment12 Oct 2024
- 2024Dynamic Normativity: Necessary and Sufficient Conditions for Value Alignment16 Jun 2024
- 2024Co-Supervised Learning: Improving Weak-to-Strong Generalization with Hierarchical Mixture of Experts23 Feb 2024
- 2023Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision14 Dec 2023
- 2023A Theory of Bounded Inductive Rationality11 Jul 2023
- 2022Red Teaming with Mind Reading: White-Box Adversarial Policies Against RL Agents5 Sep 2022
- 2022GPT-NeoX-20B: An Open-Source Autoregressive Language Model14 Apr 2022
4 more
- 2022Arguments about Highly Reliable Agent Designs as a Useful Path to Artificial Intelligence Safety9 Jan 2022
- 2021Induction, Popper, and machine learning2 Oct 2021
- 2021Temporal Inference with Finite Factored Sets23 Sep 2021
- 2021Goal Misgeneralization in Deep Reinforcement Learning28 May 2021
- 2025Absolute Zero: Reinforced Self-play Reasoning with Zero Datacited by 323
- 2022GPT-NeoX-20B: An Open-Source Autoregressive Language Modelcited by 711
- 2021Goal Misgeneralization in Deep Reinforcement Learningcited by 122
- 2024On Goodhart's law, with an application to value alignmentcited by 9
- 2021Temporal Inference with Finite Factored Setscited by 2
- 2025Representation Learning on a Random Latticecited by 2
- 2021Induction, Popper, and machine learningcited by 1
- 2023A Theory of Bounded Inductive Rationalitycited by 1
- 2025NP-Hard Lower Bound Complexity for Semantic Self-Verificationcited by 1
- 2025The World Is Bigger! A Computationally-Embedded Perspective on the Big World Hypothesiscited by 1
- 2022Arguments about Highly Reliable Agent Designs as a Useful Path to Artificial Intelligence Safetycited by 0
- 2026The Tao of Agency: Autotelic AI, Embedded Agency and Dissolution of the Selfcited by 0
- 2026The Tao of Agency: Autotelic AI, Embedded Agency and Dissolution of the Self18 Jun 2026
- 2025The World Is Bigger! A Computationally-Embedded Perspective on the Big World Hypothesis29 Dec 2025
- 2025Representation Learning on a Random Lattice28 Apr 2025
- 2025NP-Hard Lower Bound Complexity for Semantic Self-Verification26 Jan 2025
- 2024On Goodhart's law, with an application to value alignment12 Oct 2024
- 2023A Theory of Bounded Inductive Rationality11 Jul 2023
- 2022GPT-NeoX-20B: An Open-Source Autoregressive Language Model14 Apr 2022
- 2022Arguments about Highly Reliable Agent Designs as a Useful Path to Artificial Intelligence Safety9 Jan 2022
- 2021Induction, Popper, and machine learning2 Oct 2021
- 2021Temporal Inference with Finite Factored Sets23 Sep 2021
- 2021Goal Misgeneralization in Deep Reinforcement Learning28 May 2021