mlsanityData to 7 Oct 2026

A space of goals: the cognitive geometry of informationally bounded agents

arXiv 2111.03699 ↗First posted 5 Nov 2021cs.AI cs.IT cs.SYcited by 1

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Abstract (arXiv)

Traditionally, Euclidean geometry is treated by scientists as a priori and objective. However, when we take the position of an agent, the problem of selecting a best route should also factor in the abilities of the agent, its embodiment and particularly its cognitive effort. In this paper we consider geometry in terms of travel between states within a world by incorporating information processing costs with the appropriate spatial distances. This induces a geometry that increasingly differs from the original geometry of the given world as information costs become increasingly important. We visualise this "cognitive geometry" by projecting it onto 2- and 3-dimensional spaces showing distinct distortions reflecting the emergence of epistemic and information-saving strategies as well as pivot states. The analogies between traditional cost-based geometries and those induced by additional informational costs invite a generalisation of the notion of geodesics as cheapest routes towards the notion of infodesics. In this perspective, the concept of infodesics is inspired by the property of geodesics that, travelling from a given start location to a given goal location along a geodesic, not only the goal, but all points along the way are visited at optimal cost from the start.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 12 of its 74 references that are in the map. First posted on arXiv 5 Nov 2021.

Builds on

12 of 74 references.

References · 12

  1. 2019A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptationcited by 166
  2. 2021Neural population geometry: An approach for understanding biological and artificial neural networkscited by 52
  3. 2021People construct simplified mental representations to plancited by 45
  4. 2013On the Complexity of Solving Markov Decision Problemscited by 31
  5. 2021Neural tuning and representational geometrycited by 31
  6. 2021Cognitive science as a source of forward and inverse models of human decisions for robotics and controlcited by 22
  7. 2015Information-Theoretic Bounded Rationalitycited by 21
  8. 2019Mutual-Information Regularization in Markov Decision Processes and Actor-Critic Learningcited by 18
  9. 2019Towards a Quantum-Like Cognitive Architecture for Decision-Makingcited by 17
  10. 2020Resource-rational Task Decomposition to Minimize Planning Costscited by 9
  11. 2015The evolution of lossy compressioncited by 2
  12. 2017Hierarchical State Abstractions for Decision-Making Problems with Computational Constraintscited by 1

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