Information Theory of Decisions and Actions
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The perception-action cycle and active inference make evaluation emergent from the agent's ongoing interaction with the environment, arising either through optimization of information flow or through minimization of expected surprise.
“The perception-action cycle (Tishby and Polani, 2010) and active inference (Friston et al., 2017) both make evaluation emergent from the agent's ongoing interaction with the environment, arising either through optimization of information flow or through minimization of expected surprise.”
From Toward Enactive Artificial Intelligence (2026) · cites this paper as Tishby and Polani, 2010
Cites
Causality: Models, Reasoning, and Inference
Pearl, J. (2009) · doi:10.1017/cbo9780511803161 · Cited by 11,653 · see citing works
The information bottleneck method
Tishby, N., Pereira, F. C., & Bialek, W. (2000) · arXiv · doi:10.48550/arxiv.physics/0004057 · Cited by 2,626 · cited 3× · see citing works
“The concept of relevant information stems from the information bottleneck formalism (Tishby et al. 1999).”
BackgroundMethodSelf-citation‘Infotaxis’ as a strategy for searching without gradients
Vergassola, M., Villermaux, E., & Shraiman, B. I. (2007) · Nature · doi:10.1038/nature05464 · Cited by 890 · see citing works
Information Flows in Causal Networks
AY, N., & POLANI, D. (2008) · Advances in Complex Systems · doi:10.1142/s0219525908001465 · Cited by 287 · cited 1× · see citing works
“On the one hand, the study of the information flows in such a system gives important insights into the operation of the agent-environment system (Ay and Wennekers 2003; Lungarella and Sporns 2006; Ay and Polani 2008).”
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Information-seeking, curiosity, and attention: computational and neural mechanisms
Gottlieb, J., Oudeyer, P.-Y., Lopes, M., & Baranes, A. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.09.001 · Cited by 784 · see citing works
“Balancing the costs and benefits of information sampling can also be cast in an information theoretic perspective (Tishby and Polani 2011).”
Active inference and epistemic value
Friston, K., Rigoli, F., Ognibene, D., Mathys, C., Fitzgerald, T., & Pezzulo, G. (2015) · Cognitive Neuroscience · doi:10.1080/17588928.2015.1020053 · Cited by 731 · see citing works
The anatomy of choice: active inference and agency
Friston, K., Schwartenbeck, P., FitzGerald, T., Moutoussis, M., Behrens, T., & Dolan, R. J. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00598 · Cited by 340 · see citing works
The anatomy of choice: dopamine and decision-making.
Friston, K., Schwartenbeck, P., FitzGerald, T., Moutoussis, M., Behrens, T., & Dolan, R. J. (2014) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2013.0481 · Cited by 289 · see citing works
Resource-rational decision making
Bhui, R., Lai, L., & Gershman, S. J. (2021) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2021.02.015 · Cited by 168 · see citing works
Happily entangled: prediction, emotion, and the embodied mind
Miller, M., & Clark, A. (2018) · Synthese · doi:10.1007/s11229-017-1399-7 · Cited by 114 · see citing works
Evidence for surprise minimization over value maximization in choice behavior
Schwartenbeck, P., FitzGerald, T. H. B., Mathys, C., Dolan, R., Kronbichler, M., & Friston, K. (2015) · Scientific Reports · doi:10.1038/srep16575 · Cited by 96 · see citing works
Action chunking as conditional policy compression
Lai, L., Huang, A. Z., & Gershman, S. J. (2022) · doi:10.31234/osf.io/z8yrv · Cited by 9 · see citing works
Towards Human-Like Emergent Communication via Utility, Informativeness, and Complexity
Tucker, M., Shah, J., Levy, R., & Zaslavsky, N. (2025) · Open Mind · doi:10.1162/opmi_a_00188 · Cited by 2 · see citing works
“Other works explore how complexity bounds limit expected utility in Markov Decision Processes, where agents accrue reward based on states and actions but penalties based on highly deterministic and specific (complex) policies (Rubin et al., 2012; Tishby & Polani, 2011).”
Toward Enactive Artificial Intelligence
Rafiee, B., & Sutton, R. (2026) · arXiv · doi:10.48550/arxiv.2605.24238
- Authors
- Tishby, N., & Polani, D.
- Affiliations
- Hebrew University of Jerusalem (IL)
- Corresponding author
- Tishby, Naftali
- Venue
- Perception-Action Cycle
- Publisher
- Springer New York
- Published
- 2010-12-31
- Pages
- 601-636
- OpenAlex
- W2239029832
- Cited by
- 267
- References
- 4
- Citations by year
- 2026: 5 · 2025: 15 · 2024: 16 · 2023: 15 · 2022: 20 · 2021: 28 · 2020: 22 · 2019: 29 · 2018: 12 · 2017: 17 · 2016: 18 · 2015: 19 · 2014: 21 · 2013: 11 · 2012: 6
- FWCI
- 17.08
- Type
- book chapter
- Language
- en
- Open access
- closed
- Licence
- Locations
- Springer eBooks (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Computer Science › Artificial Intelligence › Diverse Interdisciplinary Research Studies
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Keywords
- perception-action cycle, Shannon information measure, reinforcement learning, Bellman equation, Blahut-Arimoto algorithm, probabilistic graphical models, optimal control, grid world
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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