# Information-Theoretic Bounded Rationality

arXiv: https://arxiv.org/abs/1512.06789 · HTML: https://mlsanity.com/paper/1512.06789/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2015.

## Built on by (most cited first, 21 of 21)

- [Three Dogmas of Reinforcement Learning](https://mlsanity.com/paper/2407.10583/) (2024)
- [Inverse Decision Modeling: Learning Interpretable Representations of Behavior](https://mlsanity.com/paper/2310.18591/) (2023)
- [Towards the Transferability of Rewards Recovered via Regularized Inverse Reinforcement Learning](https://mlsanity.com/paper/2406.01793/) (2024)
- [Factorised Active Inference for Strategic Multi-Agent Interactions](https://mlsanity.com/paper/2411.07362/) (2024)
- [Bounded rationality for relaxing best response and mutual consistency: The Quantal Hierarchy model of decision-making](https://mlsanity.com/paper/2106.15844/) (2021)
- [Solving Dynamic Principal-Agent Problems with a Rationally Inattentive Principal](https://mlsanity.com/paper/2202.01691/) (2022)
- [Convergence of a model-free entropy-regularized inverse reinforcement learning algorithm](https://mlsanity.com/paper/2403.16829/) (2024)
- [A space of goals: the cognitive geometry of informationally bounded agents](https://mlsanity.com/paper/2111.03699/) (2021)
- [Fundamental Limits for Sensor-Based Robot Control](https://mlsanity.com/paper/2202.00129/) (2022)
- [Variational Inference for Model-Free and Model-Based Reinforcement Learning](https://mlsanity.com/paper/2209.01693/) (2022)
- [Decision-Making Among Bounded Rational Agents](https://mlsanity.com/paper/2210.08672/) (2022)
- [Subgoal Discovery Using a Free Energy Paradigm and State Aggregations](https://mlsanity.com/paper/2412.16687/) (2024)
- [On the Variational Costs of Changing Our Minds](https://mlsanity.com/paper/2509.17957/) (2025)
- [Ailed: A Psyche-Driven Chess Engine with Dynamic Emotional Modulation](https://mlsanity.com/paper/2603.05352/) (2026)
- [Deep imagination is a close to optimal policy for planning in large decision trees under limited resources](https://mlsanity.com/paper/2104.06339/) (2021)
- [Principles of frugal inference and control](https://mlsanity.com/paper/2406.14427/) (2024)
- [Notes on the Reward Representation of Posterior Updates](https://mlsanity.com/paper/2602.02912/) (2026)
- [Bounded-Rationality, Hedging, and Generalization](https://mlsanity.com/paper/2605.15340/) (2026)
- [Generalised Bellman recurrence and three dualities in sequential decision-making](https://mlsanity.com/paper/2607.18077/) (2026)
- [Self-Balancing Sequential Sampling: Fast Convergence with Controlled Predictability](https://mlsanity.com/paper/2607.20818/) (2026)
- [Propose to Learn, Learn to Propose: Evaluability-Aware Assistance under Bounded Rationality](https://mlsanity.com/paper/2609.02242/) (2026)

## Abstract (arXiv)

Bounded rationality, that is, decision-making and planning under resource limitations, is widely regarded as an important open problem in artificial intelligence, reinforcement learning, computational neuroscience and economics. This paper offers a consolidated presentation of a theory of bounded rationality based on information-theoretic ideas. We provide a conceptual justification for using the free energy functional as the objective function for characterizing bounded-rational decisions. This functional possesses three crucial properties: it controls the size of the solution space; it has Monte Carlo planners that are exact, yet bypass the need for exhaustive search; and it captures model uncertainty arising from lack of evidence or from interacting with other agents having unknown intentions. We discuss the single-step decision-making case, and show how to extend it to sequential decisions using equivalence transformations. This extension yields a very general class of decision problems that encompass classical decision rules (e.g. EXPECTIMAX and MINIMAX) as limit cases, as well as trust- and risk-sensitive planning.