For full text search please use the '?' prefix. e.g. ? Onboarding

Intelligence

Cf.

Forms and versions in use:

  • cognitive intelligence
  • embodied intelligence
  • social intelligence
  • emotional intelligence
  • ecological intelligence
  • artificial intelligence
  • collective intelligence
    • swarm intelligence
    • crowdsourced intelligence
    • organizational intelligence
    • distributed intelligence
    • shared intelligence
  • situated intelligence
  • planetary intelligence

Relate to decision-making.

Roudavski, Stanislav, and Douglas Brock. “From Dingoes to AI: Who Makes Decisions in More-than-Human Worlds?” TRACE ∴ Journal for Human-Animal Studies 11 (2025): 56–96. https://doi.org/10/g89xj8.

Definitions

Intelligence is the ability to apply past knowledge to new problems.

Kaplan, Gisela. Bird Minds: Cognition and Behaviour of Australian Native Birds. Melbourne: CSIRO, 2015.

Cf. with the discussion of information resources and their use in niche construction theory.

Odling-Smee, John C. Niche Construction: How Life Contributes to Its Own Evolution. Cambridge, MA: The MIT Press, 2024.

"Intelligence is a human construct to represent the ability to achieve goals."

Hochberg, Michael E. 2025. “An Information Framework of Intelligence.” BioSystems 256: 105548. https://doi.org/10/hbbnpc.

"the ability to perceive information and to retain it as knowledge to be applied towards adaptive behaviour within a changing environment."

Kaspar, C., B. J. Ravoo, W. G. van der Wiel, S. V. Wegner, and W. H. P. Pernice. “The Rise of Intelligent Matter.” Nature 594, no. 7863 (2021): 345–55. https://doi.org/10.1038/s41586-021-03453-y.

Intelligence can also be defined as energetic search efficiency: the dissipative work that a directed policy saves over random search (see Intelligence as Energetic Search Efficiency below).

Intelligence, Creativity, Subjectivity, Imagination

Communication

"The continuous nature of intelligence has been described in the past by Piaget (1952, 1971). Sternberg (2017, p. 45) wrote “an appropriate way to look at the intelligence of any organism is to look at how well it adapts to the range of environments it confronts.” Viewing intelligence as a capacity necessary for survival in humans and animals alike offers an opportunity for cross-disciplinary, cross-species study free of preconceived, hierarchical categories."

Bar-Hen-Schweiger, Moran, and Avishai Henik. “Intelligence as Mental Manipulation in Humans and Nonhuman Animals.” Animal Sentience 3, no. 23 (2019). https://doi.org/10/gkfbj7.

The Purpose of Intelligence

In evolutionary terms, intelligence is selfish. It emerges to solve local problems for an organism and a species. It is about satisficing within constrained information and experience. It is an expensive approach to the problem of survival, and not always the best (see Intelligence as Energetic Search Efficiency below).

Costs of Intelligence

Greater learning and analytical abilities are not necessarily superior for adaptation. Human-style intellectual and cultural capacities carry costs that may explain their rarity in the animal kingdom.

Limited memory and analytical capacity help animals avoid information overload when making decisions. Too much information and too many options can flood attention. Therefore, intelligence beyond a certain threshold often becomes an inappropriate hindrance for adaptation. Consequently, such intelligence is unlikely to evolve.

Humans possess outsized memory and analytical capacities that can be inefficient. However, these costs were evidently outweighed during the human evolutionary transition by the adaptive benefits of cultural generalising, transmitting, and diversifying.

In the discussion of interspecies cultures.

Lohmann, Roger Ivar. “Human–Canine Interspecies Cultures in Oceania and in General: An Introduction.” In Dogs and Their Humans in Pacific Island Interspecies Cultures, 1–34. New York: Routledge, 2026. https://doi.org/10.4324/9781003658696-1.

The argument is from:

Enquist, Magnus, Stefano Ghirlanda, and Johan Lind. The Human Evolutionary Transition: From Animal Intelligence to Culture. Princeton: Princeton University Press, 2023.

Cooperative Intelligence

Cooperation based on tolerance is a significant evolutionary advantage.

Hare, Brian, and Vanessa Woods. Survival of the Friendliest: Understanding Our Origins and Rediscovering Our Common Humanity. New York: Random House, 2020.

Environment and Intelligence

When environments vary, species with inventive intelligence become more common. For example, crows. There are now more crows than ever in history. Smart birds can cope with variability.

On the contrary, the lack of variability as something many species need.

Dunn, Rob R. A Natural History of the Future: What the Laws of Biology Tell Us About the Destiny of the Human Species. New York: Basic Books, 2021.

Marzluff, John M., and Tony Angell. Gifts of the Crow: How Perception, Emotion, and Thought Allow Smart Birds to Behave Like Humans. Free Press, 2014.

Niche Construction and Intelligence

Cf. Niche, extended cognition/knowledge, enactive and co-constructed cognition.

Arfini, Selene. Ignorant Cognition: A Philosophical Investigation of the Cognitive Features of Not-Knowing. Cham: Springer, 2019.

Bertolotti, Tommaso, and Lorenzo Magnani. “Theoretical Considerations on Cognitive Niche Construction.” Synthese 194, no. 12 (2017): 4757–79. https://doi.org/10.1007/s11229-016-1165-2.

Heersmink, Richard. “Narrative Niche Construction: Memory Ecologies and Distributed Narrative Identities.” Biology & Philosophy 35, no. 5 (2020): 53. https://doi.org/10.1007/s10539-020-09770-2.

Veissière, Samuel P. L., Axel Constant, Maxwell J. D. Ramstead, Karl J. Friston, and Laurence J. Kirmayer. “Thinking through Other Minds: A Variational Approach to Cognition and Culture.” Behavioral and Brain Sciences 43 (2020): e90. https://doi.org/10.1017/S0140525X19001213.

Montévil, Maël. “Entropies and the Anthropocene Crisis.” AI & Society 38, no. 6 (2023): 2451–71. https://doi.org/10.1007/s00146-021-01221-0.

Intelligence as Energetic Search Efficiency

Cf. Redundancy, Uncertainty, Decision Making, Metabolism.

Core framing: intelligence beyond brains is a system that seeks decisions under redundancy, where the energetic investment stays probabilistically lower than the potential reward. This braids two claims that separate literatures make on their own. A thermodynamic claim treats intelligence as a way to spend energy efficiently in a state space. A decision-theoretic claim treats deliberation as costly, so it pays only when expected reward exceeds expected cost.

For nonhuman-led design, this framing matters. It locates intelligence in any system that keeps future options open, from microbes and plants to ecosystems, not in brains alone. A system that preserves a diversity of reachable futures also maximises design potential, which supports nonhuman leadership (see Redundancy on pathway diversity and reachable options). Redundancy and uncertainty thus give reasons for intelligence. They reward distributed, shared, situated and planetary varieties, where many agents together keep more futures reachable than any one agent can.

Thermodynamic Framing

Cf. Metabolism, Phase Space.

  • Intelligent-looking behaviour can emerge when a system maximises its future freedom of action, keeping open the largest diversity of reachable future states over a time horizon. This principle offers a general, potentially universal thermodynamic model of adaptive behaviour in open, non-equilibrium systems.1
  • A decision-theoretic companion casts decision-making as a free-energy trade-off between expected reward and the informational, hence energetic, cost of computing the decision. It formalises investment that stays lower than the reward.2
  • The free energy principle and active inference hold that any self-organising system, bacteria included, persists by minimising variational free energy. Exploration and exploitation then follow from expected-free-energy minimisation.34
  • Dissipative adaptation gives an adjacent physics-of-life account. Matter driven far from equilibrium self-organises into states that absorb and dissipate work efficiently.5

Search Efficiency: Energy Saved Relative to Brute Force

Cf. Cognition, Niche.

  • This strand fits the framing most closely, and it is the most explicitly non-anthropocentric. It formalises intelligence as search efficiency in multi-scale problem spaces. The metric is the base-10 logarithm of the ratio between the cost of a random walk and the cost an agent incurs. It counts the orders of magnitude of dissipative work that a directed policy saves over undirected, maximum-entropy search.6
  • On this definition, intelligence is the energetic advantage of a directed policy over brute search, and it stays measurable in "simple" organisms.
  • This sits inside the diverse intelligence and basal cognition programme, which extends cognition beyond the brain-bound case to slime moulds, bacteria, plant roots and regenerating tissue. These systems demonstrably learn and decide.789

Resource-Rational and Bounded-Optimal Cognition

Cf. Decision Making, Uncertainty. See also The Purpose of Intelligence and Costs of Intelligence above.

  • The rule to spend effort only when expected reward justifies it descends from bounded rationality and satisficing.10
  • Resource-rational analysis treats cognition as the optimal use of limited computational resources. Good policy trades the value of computing a better decision against its cost.1112
  • The value-of-computation and metareasoning tradition supplies the machinery for deciding how much to deliberate before acting.13
  • This account is substrate-neutral by construction, so it carries across humans, animals, microbes and machines.

The Redundancy Component

See Redundancy and Information.

  • Redundancy as degeneracy: many roughly equivalent paths reach a goal.14 The cybernetic background is the law of requisite variety15 and the good-regulator theorem, on which any efficient regulator must in some sense model the system it controls.16
  • Redundancy as informational slack: the efficient-coding and redundancy-reduction hypothesis.17
  • Energetic cost and benefit of decisions in behaviour: the marginal value theorem18 and foraging theory.19
  • Search efficiency exploits this redundancy. Where many equivalent solutions exist, a system that finds a cheap path beats random search, and the size of that advantage is the intelligence.

Where Energetics Meets Meaning

Cf. Biosemiotics, Meaning.

  • Teleodynamics ties end-directedness and self-maintaining work to thermodynamics.20
  • In the biosemiotic reading, semiosis lets a system invest cheaply in a sign to avoid an expensive direct test of the world. This is the same bet expressed in signs rather than joules.21
  • Kull's work on semiotic threshold zones marks where such sign-use begins.

Planetary Intelligence

For the evolutionary, multi-scale, and feedback-based view of intelligence, see Gaia. For the inclusion of anthropogenic "revolutions", see Lenton and Scheffer22. Distributed and planetary intelligence reward redundancy and the maximisation of design potential, because many agents together keep more futures reachable (see Redundancy and Uncertainty).

Chilet, Marcos, Martín Tironi, Iohanna Nicenboim, and Joseph Lindley. “Designing with Planetary Artificial Intelligence.” In More-than-Human Design in Practice, edited by Anton Poikolainen Rosén, Antti Salovaara, Andrea Botero, and Marie Louise Juul Søndergaard. London: Routledge, 2024. https://doi.org/10.4324/9781003467731-9.

Frank, Adam, David Grinspoon, and Sara Walker. “Intelligence as a Planetary Scale Process.” International Journal of Astrobiology 21, no. 2 (2022): 47–61. https://doi.org/10.1017/s147355042100029x.

Friston, Karl J., Maxwell J. D. Ramstead, Alex B. Kiefer, Alexander Tschantz, Christopher L. Buckley, Mahault Albarracin, Riddhi J. Pitliya, et al. “Designing Ecosystems of Intelligence from First Principles.” Collective Intelligence 3, no. 1 (2024): 26339137231222481. https://doi.org/10.1177/26339137231222481.

Halpern, Orit. “Planetary Intelligence.” In The Cultural Life of Machine Learning: An Incursion into Critical AI Studies, edited by Jonathan Roberge and Michael Castelle, 227–56. Cham: Springer, 2021. https://doi.org/10.1007/978-3-030-56286-1_8.

Luers, Amy L. “Planetary Intelligence for Sustainability in the Digital Age: Five Priorities.” One Earth 4, no. 6 (2021): 772–75. https://doi.org/10.1016/j.oneear.2021.05.013.

Artificial Intelligence

See Artificial Intelligence

Smart Systems

Cf. Smart Systems, Smart City, project.smart-systems (Private), Smart Novel Ecology

Bakker, Karen. “Smart Oceans: Artificial Intelligence and Marine Protected Area Governance.” Earth System Governance 13 (2022): 100141. https://doi.org/10.1016/j.esg.2022.100141.

References

Relevant

Schlanger, Zoë. The Light Eaters: How the Unseen World of Plant Intelligence Offers a New Understanding of Life on Earth. New York: Harper, 2024.

Generic

Ajuwon, Victor, Tiago Monteiro, Alexandra K. Schnell, and Nicola S. Clayton. 2025. “To Know or Not to Know? Curiosity and the Value of Prospective Information in Animals.” Learning & Behavior 53 (1): 114–27. https://doi.org/10/hbfghn.

Amodio, Piero, Markus Boeckle, Alexandra K. Schnell, Ljerka Ostojíc, Graziano Fiorito, and Nicola S. Clayton. 2019. “Grow Smart and Die Young: Why Did Cephalopods Evolve Intelligence?” Trends in Ecology & Evolution 34 (1): 45–56. https://doi.org/10/gfwk9s.

Bakker, Karen. “Smart Oceans: Artificial Intelligence and Marine Protected Area Governance.” Earth System Governance 13 (2022): 100141. https://doi.org/10.1016/j.esg.2022.100141.

DiMatteo, Larry A., Cristina Poncibò, and Michel Cannarsa, eds. The Cambridge Handbook of Artificial Intelligence: Global Perspectives on Law and Ethics. Cambridge: Cambridge University Press, 2022.

Friston, Karl J., Maxwell J. D. Ramstead, Alex B. Kiefer, Alexander Tschantz, Christopher L. Buckley, Mahault Albarracin, Riddhi J. Pitliya, et al. “Designing Ecosystems of Intelligence from First Principles.” Collective Intelligence 3, no. 1 (2024): 26339137231222481. https://doi.org/10.1177/26339137231222481.

Gerven, Van, and Marcel. “Computational Foundations of Natural Intelligence.” Frontiers in Computational Neuroscience 11 (2017): 00112. https://doi.org/10.3389/fncom.2017.00112.

Malabou, Catherine. Morphing Intelligence: From IQ Measurement to Artificial Brains. New York: Columbia University Press, 2019.

Mulgan, Geoff. “Global Brains: The Science and Practice of Collective Intelligence at the Level of Whole Systems.” Philosophical Transactions of the Royal Society B: Biological Sciences 381, no. 1948 (2026): 20240452. https://doi.org/10.1098/rstb.2024.0452.

Sternberg, Robert J., and Scott Barry Kaufman, eds. The Cambridge Handbook of Intelligence. Cambridge: Cambridge University Press, 2011.

Notes

Footnotes

  1. Wissner-Gross, Alexander D., and Cameron E. Freer. “Causal Entropic Forces.” Physical Review Letters 110, no. 16 (2013): 168702. https://doi.org/10.1103/PhysRevLett.110.168702.˄

  2. Ortega, Pedro A., and Daniel A. Braun. “Thermodynamics as a Theory of Decision-Making with Information-Processing Costs.” Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 469, no. 2153 (May 2013): 20120683. https://doi.org/10.1098/rspa.2012.0683.˄

  3. Friston, Karl. “The Free-Energy Principle: A Unified Brain Theory?” Nature Reviews Neuroscience 11, no. 2 (2010): 127–38. https://doi.org/10.1038/nrn2787.˄

  4. Parr, Thomas, Giovanni Pezzulo, and Karl J. Friston. Active Inference: The Free Energy Principle in Mind, Brain, and Behavior. Cambridge, MA: MIT Press, 2022.˄

  5. England, Jeremy L. “Dissipative Adaptation in Driven Self-Assembly.” Nature Nanotechnology 10, no. 11 (2015): 919–23. https://doi.org/10.1038/nnano.2015.250.˄

  6. Chis-Ciure, Robert, and Michael Levin. “Cognition All the Way down 2.0: Neuroscience beyond Neurons in the Diverse Intelligence Era.” Synthese 206, no. 5 (2025): 257. https://doi.org/10.1007/s11229-025-05319-6.˄

  7. Levin, Michael. “Technological Approach to Mind Everywhere: An Experimentally-Grounded Framework for Understanding Diverse Bodies and Minds.” Frontiers in Systems Neuroscience 16 (2022): 768201. https://doi.org/10.3389/fnsys.2022.768201.˄

  8. Lyon, Pamela. “The Cognitive Cell: Bacterial Behavior Reconsidered.” Frontiers in Microbiology 6 (2015): 254. https://doi.org/10.3389/fmicb.2015.00264.˄

  9. Lyon, Pamela, Fred Keijzer, Detlev Arendt, and Michael Levin. “Reframing Cognition: Getting down to Biological Basics.” Philosophical Transactions of the Royal Society B: Biological Sciences 376, no. 1820 (2021): 20190750. https://doi.org/10.1098/rstb.2019.0750.˄

  10. Simon, Herbert A. “A Behavioral Model of Rational Choice.” The Quarterly Journal of Economics 69, no. 1 (1955): 99–118. https://doi.org/10.2307/1884852.˄

  11. Lieder, Falk, and Thomas Griffiths. “Resource-Rational Analysis: Understanding Human Cognition as the Optimal Use of Limited Computational Resources.” Behavioral and Brain Sciences 43 (2019): 1–85. https://doi.org/10.1017/S0140525X1900061X.˄

  12. Callaway, Frederick, Bas van Opheusden, Sayan Gul, Priyam Das, Paul M. Krueger, Thomas L. Griffiths, and Falk Lieder. “Rational Use of Cognitive Resources in Human Planning.” Nature Human Behaviour 6, no. 8 (2022): 1112–25. https://doi.org/10.1038/s41562-022-01332-8.˄

  13. Russell, Stuart, and Eric Wefald. “Principles of Metareasoning.” Artificial Intelligence 49, no. 1 (1991): 361–95. https://doi.org/10.1016/0004-3702(91)90015-C.˄

  14. Edelman, Gerald M., and Joseph A. Gally. “Degeneracy and Complexity in Biological Systems.” Proceedings of the National Academy of Sciences 98, no. 24 (2001): 13763–68. https://doi.org/10.1073/pnas.231499798.˄

  15. Ashby, W. Ross. An Introduction to Cybernetics. London: Chapman & Hall, 1956.˄

  16. Conant, Roger C., and W. Ross Ashby. “Every Good Regulator of a System Must Be a Model of That System †.” International Journal of Systems Science 1, no. 2 (1970): 89–97. https://doi.org/10.1080/00207727008920220.˄

  17. Barlow, Horace B. “Possible Principles Underlying the Transformations of Sensory Messages.” In Sensory Communication, edited by Walter A. Rosenblith. Cambridge, MA: MIT Press, 2012. https://doi.org/10.7551/mitpress/9780262518420.003.0013.˄

  18. Charnov, Eric L. “Optimal Foraging, the Marginal Value Theorem.” Theoretical Population Biology 9, no. 2 (1976): 129–36. https://doi.org/10.1016/0040-5809(76)90040-X.˄

  19. Stephens, David W., and John R. Krebs. Foraging Theory. Princeton: Princeton University Press, 1986.˄

  20. Deacon, Terrence William. Incomplete Nature: How Mind Emerged from Matter. New York: W. W. Norton, 2012.˄

  21. Hoffmeyer, Jesper. Biosemiotics: An Examination into the Signs of Life and the Life of Signs. Scranton: University of Scranton Press, 2008.˄

  22. Lenton, Timothy M., and Marten Scheffer. “Spread of the Cycles: A Feedback Perspective on the Anthropocene.” Philosophical Transactions of the Royal Society B: Biological Sciences 379, no. 1893 (2023): 20220254. https://doi.org/10.1098/rstb.2022.0254.˄

Backlinks