Bias

This note is about bias that results from subjectivity, for example the many human biases, including the bias for finding narrative, meaning, and order where they do not exist.

For evidence, see: Human Bias.

Human Bias, the evidence base for the human entries here
Preservation Bias, a specific case worked out separately
Sensory Ecology and Biosemiotics, where perceptual limits become meaning
Agency and Biological Individuality, which fix who or what bears a bias
Intelligence and Decision Making, since bias is what bounded decision-making looks like from outside
Uncertainty, the condition that makes selection necessary
Robustness and Redundancy, strategies that tolerate bias rather than removing it
Phase Space and Design Potential (Private), where bias becomes a claim about reachable futures
Imaginary, bias at the scale of what a collective can conceive
Niche, construction and inheritance as bias made environmental
Information and Knowledge, the currencies being filtered
Reference Frame, since a model is a bias with a boundary drawn around it
Justice, because bias plus reach produces harm
Life, for what kinds of things can be biased at all

Herzen wrote in an article entitled “Apropos of a Drama”:

"There is something about chance that is intolerably repellent to a free spirit…. He wants the misfortunes that overtake him to be predestined—that is, to exist in connection with a universal world order; he wants to accept disasters as persecutions and punishments: this allows him to console himself through submission or rebellion."

Herzen: the future is a variation improvised on the theme of the past.

Humans, as all living beings (cf. Biosemiotics) have inherent biases.

For example, apophenia, or patternicity, is a term/condition that refers to a tendency in humans where they see patterns where patterns do not exist.

Definitions

Bias is a tendency to prefer or favour one response over others.

More precisely, bias is the filtering a bounded agent performs, starting at its own boundary. Every bounded agent admits some of what arrives, converts some, and ignores the rest. The planetary case has the same shape, since late-stage planets are effectively closed to exchanges of matter while staying open to energy through incoming solar flux and outgoing thermal radiation, and that pattern recurs at every scale of living organisation.1

Filtering at the boundary is one mechanism among several. What an agent admits combines with its internal processes and with the traces that constitute its memories, which have to be reconstructed and interpreted, and necessary guessing supplements all of these. Together they produce the agent's umwelt, which is at once an internal mode and a practice. Filtering also shapes what the agent does, giving its acts a direction, a pull towards some conditions and away from others. Design Potential (Private) calls this the directional bias of a proposal and uses it to separate agents, whose acts are oriented by goals, from processes such as erosion that shape options without being oriented towards anything. Bias also arises through development and inheritance, through social learning and niche construction, and through the coordination of parts across nested scales, each treated below.

Four limits shape the filtering:

Sensing: the agent detects only some variables.
Processing: the agent compares only some of what it detects, satisficing rather than optimising.2^,3
Memory: the agent retains only some of what it compared.
Action: the agent can do only some of what it might prefer, since body plans, operational ranges, and social permission each constrain which acts are available.

Action is the limit that makes bias consequential. A bounded agent with little reach does little harm, so the danger of human bias tracks the scale of human reach.

An umwelt is a lossy and constructive mapping rather than a smaller copy of the world. It omits most of what is there, and it also contains conditions that the world does not permit, because agents act on models that are wrong. Bias therefore omits and constructs, which is why well-intentioned action damages as reliably as indifference does. Umwelt mapping can be made procedural for analysis and design.5

The note uses bias in two senses. One is the filtering itself, which no bounded agent escapes and which is often adaptive. The other is the errors that filtering produces, which count as errors only against a standard. A construction becomes an error where the world does not permit what it holds, and an act becomes a harm where it takes from other agents what they need.

A bias belongs to an agent, and every agent is also an observer. The catalogue below names that agent as the bearer of each bias. The same being carries different biases as its individuality shifts with scale, life stage, and interactions: a seedling and a veteran elm, a lone tree and one joined to a mycorrhizal network, a resident at home and the same resident on a council panel. Its biases also shift with how well its parts align their goals within a hierarchy. In cancer, cells lose the coordination that binds them into a body and revert to the goals of single cells, so the self they work to maintain shrinks from the whole body to one cell's surface.78 Where the boundary of the biased agent falls is itself a modelling choice (see Biological Individuality and Design Potential (Private)), so any named bias is a partial illustration of a wider and shifting set.

Whether acting on a bias helps or harms depends on whom the act reaches. Local interest makes a community of differently biased agents sample far more of what is possible than a remote planner, or one bound to a short project, can manage, and the same interest can close options for others. Acting on a bias is mutualistic when it raises design potential for others as well as for the biased agent, and selfish when it raises the agent's own potential to reach the outcomes it favours by lowering the potential of others. Whether an act counts as one or the other depends on where the agent's boundary is drawn, since cells in a body are as selfish as cancer cells and differ in the size of the self they defend.78 Design Potential (Private) states the test as its justice condition and sets out where bias enters its model.

The study of perception carries the biases it studies, so the problem is reflexive.4

In both human and nonhuman contexts, many biases are evolved traits or strategies that have been adaptive in particular environments. They help organisms, humans included, make efficient decisions, prioritise information, and respond to recurring challenges. For example, behavioural biases like predator avoidance or foraging preferences increase survival, while perceptual biases attune organisms to the most relevant cues in their environment.

However, biases can become less useful or even maladaptive when circumstances change, or when they are applied outside the context in which they evolved. In modern human societies, some cognitive and social biases may lead to systematic errors, discrimination, or misjudgement. Similarly, in nonhuman communities, specialisation or stability biases may reduce resilience to rapid environmental change.

Bias also operates at the institutional level: processes, protocols, policies, systems, and governance structures shape what is measured, resourced, considered legitimate, and ultimately done.

Open Questions

Processing or horizon. The four limits match three of the four conditions for steering in Design Potential (Private): sensing matches legibility, memory matches retention, and action matches competence. The remaining pair differs. Processing has no steering condition of its own, and horizon, how far ahead an agent is goal-directed, has no limit of its own here. Horizon may be a fifth limit, or the temporal extent of processing and action, which is how the cognitive light cone below treats it. Necessary guessing complicates the pair. A guess can be random, as when noise diversifies the responses of otherwise identical cells, or informed by what was experienced in the evolutionary or cultural past, in which case it carries forward processing done before the present agent existed (see Developmental and Variational Bias and Predictive Processing and Priors below). Whether guessing belongs with processing, as processing inherited from the past, or with horizon, as a reach beyond what has arrived, is part of the question.
Conception. Imaginary treats imaginaries as bias acting on what a collective can conceive, while design potential carries no conception term. The working reading is that conceiving belongs to representational acts, which councils, corvids, and honeybee swarms all perform, and that it bears on design potential through steering and action rather than directly.
How to conceptualise an umwelt. An umwelt can be approached in several ways: as a discrete set of conditions or of futures, as a continuous field in which conditions register more or less strongly, as an internal mode, or as a practice. Each supports different formalisms and drawings. Design Potential (Private) writes an umwelt's relation to admissible futures as an intersection, which takes the set approach, while also describing an umwelt as the conditions accessible to an agent. Drawing umwelten as regions of possibility follows the set approach and is one option among these.
Aggregation. How a composite's bias relates to its constituents' biases is unsettled. The section on nested scales below finds that bias does not simply aggregate upward, while coordination can give a composite a longer horizon than any of its parts.

Individuals ought to be selective with respect to when they rely on social learning, and from whom they learn.

Natural selection ought to have fashioned specific adaptive social learning strategies that dictate the contexts under which individuals will exploit learned information provided by others.

Culture, based on social learning is strategic. And the strategic social learning strategies could otherwise be considered biases. The same learning biases are exploitable: conservation interventions that ignore how a species weights social information tend to fail, so identifying the bias is a precondition for working with it.18

"Human cognition probably contains numerous heuristics and learning biases that facilitate the acquisition of useful knowledge, practices, beliefs, and behavior (“cultural traits” or “representations”)."

Henrich, Joseph, and Richard McElreath. “The Evolution of Cultural Evolution.” Evolutionary Anthropology: Issues, News, and Reviews 12, no. 3 (2003): 123–35. https://doi.org/10.1002/evan.10110.

Cognitive biases

Anchoring bias: the tendency to rely too heavily on the first piece of information encountered (the "anchor") when making decisions.
Availability heuristic: overestimating the importance of information that is most readily available.
Cognitive dissonance bias: the tendency to rationalise contradictory information to maintain internal consistency.
Confirmation bias: the tendency to search for, interpret, and remember information in a way that confirms one's preconceptions or existing beliefs.
Hindsight bias: the inclination to see events as having been predictable after they have already occurred.
Recency bias: the tendency to weigh recent events more heavily than earlier events.

Age bias: favouring perspectives or outcomes associated with particular age groups.
Cultural bias: interpreting and judging phenomena by standards inherent to one's own culture or privileging particular cultural perspectives or values.
Economic bias: favouring perspectives or outcomes associated with particular economic statuses.
Educational bias: favouring perspectives or outcomes associated with particular levels of education.
Gender bias: favouring perspectives or outcomes associated with particular genders.
Group attribution bias: the tendency to generalise about a group based on the behaviour of a few individuals within that group.
Halo effect: the tendency to let an overall impression of a person influence specific judgments about them.
Historical bias: promoting specific historical narratives or interpretations.
Ingroup bias: favouring members of one's own group over those in other groups.
Negativity bias: the tendency to give more weight to negative experiences or information than positive ones. Wildlife research inherits this, framing human-wildlife relations around conflict and mitigation while leaving benefits, pleasure, and meaning largely unstudied.19
Self-serving bias: the tendency to attribute positive events to one's own disposition but attribute negative events to external factors.
Stereotyping: generalising about a group of people in which identical characteristics are assigned to virtually all members of the group.
Status quo bias: the preference for the current state of affairs and resistance to change.

Procedural and observational biases

Measurement bias: bias that arises from errors in data collection, leading to inaccurate or misleading data.
Methodological bias: preferring specific research methods or approaches. Study designs in environmental and social science skew heavily towards weaker forms, and the choice inflates or reverses estimated effects.20 Artificial nestboxes offer a design-specific case, where inconsistent dimensions, materials, and placement across studies confound the comparisons they were built to support.21
Novelty (or innovation) bias: favouring new or innovative ideas over established ones (cf. hype bias or trend bias).
Observer bias: when a researcher's expectations influence their interpretation of outcomes. Ground-based surveys of hollow-bearing trees illustrate the scale of the effect: compared against climbing inspections, observers detected between 9 and 44 per cent of hollows, and underestimated consistently across all trunk diameters.22
Omission bias: the tendency to judge harmful actions as worse, or less moral, than equally harmful omissions.
Publication bias: the tendency for positive or significant results to be published more frequently than negative or inconclusive results. In conservation the compounded effect is that the evidence available for a decision is unrepresentative of the interventions actually tried.68
Reporting bias: bias that occurs when certain outcomes or results are more likely to be reported than others.
Risk bias: prioritising research that addresses perceived risks or threats.
Sampling bias: a form of selection bias where the sample is not representative due to the method of selection. Biodiversity records are spatially and taxonomically skewed enough to distort what is believed about distributions,66 and citizen-science datasets carry their own structured gaps.67
Selective reporting bias: the tendency to report only certain results or outcomes, typically those that are positive, statistically significant, or in line with expectations, while omitting others, which can distort the scientific record.
Survivorship bias: concentrating on the people or things that "survived" some process and overlooking those that did not due to their lack of visibility.
Exaggeration bias: overstating the importance, magnitude, or certainty of findings, often in abstracts, press releases, or media coverage, which can mislead about the true implications or reliability of research results.
Pragmatism bias: what is feasible to study displaces what matters to study. Farm animal welfare science concentrates on intensive production systems, short-term gains, and symptoms rather than root causes, because funding and operational constraints make those tractable. Decisions then get made with whatever knowledge is at hand, which entrenches the systems that were convenient to research.23

Systemic and algorithmic biases

Automation is better understood as an operator on the four limits than as a category of bias in its own right. Sensing, processing, memory, and action can each be automated, and the tool that does so is another bounded agent with its own limits: a camera trap has a cone, a motion threshold, and a heat signature, while a classifier has a frequency band and a training distribution. Automation then acts in four directions.

Amplify: the tool inherits what was already deemed important and scales it, so the patterns such systems learn are biased.6
Shift: the tool substitutes its own filtering. Ecological niches get quantified through stimuli biased by human perception, greenness of vegetation among them, which fails to account for how other organisms sense.7
Resist: sensors record information that human observers cannot obtain, or could not practically collect.6
Homogenise: many varied local biases give way to one uniform bias applied everywhere. Privileging data-rich and standardised knowledge filters out marginalised perspectives, and recursive training on a system's own outputs compounds the effect.8

Homogenisation deserves separate attention, because diverse biases at least explore different regions of what is possible, whereas one model applied everywhere collapses that coverage.

Generative systems close the loop back to perception. Representations of rewilding produced by AI chatbots concentrate on conventional nature aesthetics and largely exclude aesthetically challenging ecosystem processes, non-charismatic species, and a diversity of people, which shapes which futures come to look desirable.9

Automation also makes bias less visible and more auditable at the same time. A model's training distribution can be described and its error rates measured by taxon, while conventional monitoring carries comparable geographic and socio-economic biases that stay tacit.10

Algorithmic bias: when algorithms produce systematically prejudiced results due to erroneous assumptions in the machine learning process. Risk prediction shows the structural version, where an algorithm trained on past enforcement reproduces that enforcement and presents the result as a neutral forecast.24
Automation bias: the tendency to favour suggestions from automated decision-making systems and to ignore contradictory information made without automation.
Confirmation bias in AI: when AI systems are designed or trained in a way that confirms the developers' preconceptions.
Speciesist bias in machine learning: image recognition, word embeddings, and language models trained on human corpora reproduce and normalise discrimination against animals, which the fairness literature has largely overlooked.26 Masked language models carry the same pattern in their treatment of animal terms.27
Data bias: bias that occurs when the data used to train a model is not representative of the real-world scenario it is meant to reflect. Treating biodiversity gaps as missing data rather than as absence makes the bias tractable instead of invisible.69
Disciplinary bias: giving precedence to certain scientific disciplines or fields of study. A long-running instance is the preference for disembodied formal models over embodied computation, which shapes what counts as a rigorous account of a process.25
Funding bias: promoting research that aligns with the interests of funding sources. Global conservation funding is concentrated enough that most threatened species receive no support at all.71
Institutional bias: prioritising research from specific institutions or organisations.
Interaction bias: bias that occurs when users interact with a system in a way that reinforces existing biases.
Language bias: privileging studies published in particular languages.
Selection bias: bias introduced by the non-random selection of data, leading to a sample that is not representative of the population.
Surveillance bias: increased monitoring leads to increased detection of outcomes, not necessarily increased incidence. Digital surveillance of animals extends this to nonhuman subjects, where what is watched becomes what is known and then what is managed.70

Bearer: individual humans, research communities, and the institutions they staff. Three names below recur in the organism-level list further down, where the bearer differs. Those are marked.

Aesthetic bias: favouring species that are visually appealing while neglecting or undervaluing those considered unattractive, repulsive, or "ugly".
Anthropocentrism: viewing humans as the central or most significant entities. In comparative cognition the bias is methodological as much as attitudinal, since human capacities supply the yardstick, the tasks, and the interpretive vocabulary.28
Anthropomorphism: attributing human characteristics, emotions, or intentions to nonhuman entities, which can distort understanding of their characteristics. Conversely, fear of anthropomorphism can lead to reluctance in recognising similarities between humans and nonhuman beings. That converse has its own name, below.
Anthropectomy: cutting out human-like properties that are in fact present, which is the reciprocal error to anthropomorphism and attracts far less methodological suspicion. The asymmetry is procedural rather than evidential, sitting in how the null hypothesis gets formulated and in a standing preference for false negatives over false positives, so avoiding an anthropomorphic error is treated as more important than avoiding an anthropectic one.89 The practical effect is an uneven burden of proof, where asserting a capacity requires more evidence than denying one.90 Where a being cannot contest its own characterisation, the resulting credibility deficit has been analysed as epistemic injustice,91^,92 and precautionary reasoning has been proposed because waiting for proof of sentience carries its own costs.93 Plant studies show the same pull, with romanticism and denial as opposing failure modes.94
Mind blindness: the perceptual and methodological version of the same problem, where the failure lies in the observer's equipment rather than in the evidence. Levin borrows the term from autism research and applies it to the recognition of agency, arguing that perceptual systems tuned to medium-sized beings moving at medium speeds in three dimensions register only familiar kinds of agency, so unconventional intelligences go unrecognised as a matter of course.96 The reflexive consequence is that estimating another being's intelligence amounts to taking an intelligence test oneself, since behaviour that cannot be recognised cannot be scored.97 Morgan's Canon, which urges erring towards the lower estimate of agency, reads on this account as a scientific mind blindness installed as an a priori preference rather than as a neutral safeguard.98 The same limit is what makes highly diverse minds hard to imagine at all, whether in other substrates or elsewhere in the universe.99
Biomism: prejudice, discrimination, or antagonism against a particular biome.
Charismatic species bias: focusing on aesthetically appealing or "charismatic" species. Charisma operates within taxa as well as between them: across nine years of Australian newspaper coverage of pollination, introduced European honey bees dominated, while native bees appeared in 15 per cent of stories and non-bee pollinators in 17 per cent.29
Citation bias: preferentially citing already prominent studies, or studies from certain disciplines, languages, regions, or authors.
Disgust bias: allowing feelings of disgust or repulsion (e.g., toward invertebrates, reptiles, or scavengers) to influence conservation priorities or research attention.
Domestication bias: favouring domesticated species over wild species in research, conservation, or ethical considerations.
Ecological bias (bearer: human observers): undervaluing the roles of less visible or less understood organisms. Distinct from the organism-level Ecological bias below.
Evolutionary bias (bearer: human observers): favouring species that are evolutionarily closer to humans. Distinct from the organism-level Evolutionary bias below.
Extinction bias: focusing on species that are already extinct or critically endangered, while neglecting those that are declining but not yet at critical levels.
Functional bias: prioritising species or organisms based on their perceived utility or ecosystem services, while undervaluing those with less obvious roles.
Geographic bias: favouring certain regions (e.g., developed over remote areas). Researchers in high and upper-middle-income countries contributed 97 per cent of fossil occurrence data over three decades, so colonial history and economic position shape what is known about deep-time biodiversity.11
Habitat bias: focusing on certain habitats (e.g., forests) while neglecting others (e.g., wetlands, deserts). Soils, caves, and groundwater are neglected on the same pattern, where organisms are small, hard to observe, and rarely charismatic.95
Invasive species bias: automatically categorising non-native or invasive species as harmful, sometimes overlooking their ecological roles or potential benefits.
Keystone species bias: overemphasising the importance of keystone species while undervaluing the roles of other species in ecosystems.
Land over ocean bias: prioritising terrestrial ecosystems over marine ones.
Microbial bias: overlooking the importance of microorganisms in ecosystems and evolutionary processes.
Mineral or resource bias: prioritising human use of non-living resources over their intrinsic or ecological value.
Model bias: favouring certain models or frameworks for understanding ecological systems (e.g., over-reliance on model organisms, which may not be representative). Statistical model choice carries the same risk, as in biomass allometry, where log-transformation and model selection systematically shift the estimates they are meant to describe.31
Neophobia bias: exhibiting aversion or negative attitudes toward unfamiliar or novel species, which can include both invasive and native organisms.
Novel ecosystem bias: prioritising pristine or "natural" ecosystems over novel or human-altered ecosystems, despite their ecological significance. The wilderness ideal also erases long histories of Indigenous management.12
Parochial bias: focusing on local or regional ecosystems while neglecting global ecological interconnections.
Pest bias: devaluing or targeting species labelled as pests, often ignoring their ecological functions.
Plant blindness: overlooking the importance of plants in ecosystems.
Size bias: favouring larger species over smaller ones.
Speciesism: prioritising human interests over those of other species.
Taxonomic bias: focusing on specific taxonomic groups (e.g., vertebrates over invertebrates). European insect conservation concentrates on a small set of charismatic and legally listed species, leaving most of the fauna unstudied and unprotected.30
Temperate over tropical bias: prioritising temperate ecosystems over tropical ones.
Temporal bias (bearer: researchers, funders, managers): prioritising short-term ecological changes over long-term evolutionary or ecological processes. Distinct from the organism-level Temporal bias below.
Trophic bias: focusing on higher trophic levels (e.g., predators) while neglecting lower levels (e.g., decomposers, primary producers).

Institutional, policy, and governance biases

Process (SOP/workflow) bias: standard operating procedures and workflows steer attention and resources to established approaches, filtering out alternatives and novelty.
Protocol bias: rigid study, clinical, or operational protocols constrain design choices and outcomes, discouraging adaptation and shaping what can be measured or reported.
Policy bias: organisational or public policies privilege particular outcomes or stakeholders, embedding value judgements and trade-offs that are hard to revisit.
Governance and oversight bias: the composition, mandate, and incentives of boards, ethics committees, and advisory panels shape which options are considered legitimate; includes representation bias.
Regulatory bias: legal frameworks favour specific methods or industries (e.g., precautionary rules that block field trials, or permissive regimes that favour rapid deployment).
Procurement and vendor bias: purchasing rules, framework agreements, and vendor lock-in favour incumbents and bundled solutions, limiting competition and switching.
Standards and interoperability bias: formal and de facto standards channel design and measurement, entrenching path dependence and excluding alternative paradigms.
Infrastructure and systems bias: legacy architectures, data platforms, and tooling make some questions answerable and render others invisible; constraints propagate into decisions.
Data governance bias: retention, classification, consent, and access policies produce “data deserts” for some groups, regions, or topics, skewing evidence. Environmental data justice names the same problem for area-based conservation, where who collects and controls data shapes which places get protected.73
Metrics and KPI bias (Goodhart’s law): what is measured becomes the target; indicators distort behaviour and priorities (e.g., league tables, citation counts). Indicators that capture information identically regardless of local context stop being meaningful, which is the measurement form of homogenisation.72
Audit and compliance bias: checklists and compliance regimes prioritise procedural conformity over outcomes, encouraging risk aversion and discouraging exploration.
Risk management bias: frameworks emphasising blame avoidance over learning and innovation tilt choices toward the safest visible option, not the best outcome.
Resource allocation and budget-cycle bias: baseline budgeting, short cycles, and cost-centre logics entrench the status quo and crowd out prevention and long-term investments. Cycle length is a poor match for ecological time, so slow processes lose to fast ones by default.71
Access and eligibility bias: gatekeeping through credentials, eligibility criteria, or location systematically excludes some participants and forms of knowledge.
Scheduling and geography bias: meeting times, hubs, and service coverage advantage central offices and dominant time zones, marginalising remote or minority groups.
Public engagement and consultation bias: methods and sampling frames privilege already vocal or connected constituencies, under-hearing affected but less resourced groups. Participatory processes privilege articulation, deliberation, and formal representation, which sidelines beings that express constraints through behaviour, growth, metabolism, or slow environmental change.74
Ethical review bias: institutional review norms prioritise certain risks, which can bias topics and methods (e.g., human, animal, or fieldwork constraints shaping what can be studied).

Framing bias operates on the concepts used to govern, and not only on the procedures. A review of the limits literature finds that dominant conceptions of limits have overshadowed alternative material, ecological, and sociopolitical conceptions, producing the reification of nature and the depoliticisation of ecological crisis. Planetary boundaries offer an astronaut's eye view that only scientists can supply, and treating humanity as a single entity masks the unequal contribution of societies.13^,14

Imaginaries sit beneath framing. An imaginary is the shared background that settles which futures a collective, human in most accounts, can conceive at all, so it acts on conception, where most entries here act on perception or judgement, and through conception it influences relationships, processes, and consequences. Imaginaries limit and enable, sediment into law and institutions, resist correction by evidence, and can hold futures the world does not permit, as the inexhaustible ocean behind extractive ocean law shows. See Imaginary.

Biases that Affect Biological Agents and Communities

Bearer: any living system that senses and acts. The bearer can be a cell, an organ, an organism, a colony, a population, or a whole ecological community, and the same named bias can occur at several of these levels at once. Naming the bearer matters, because a bias that is adaptive for a cell can be costly for the organism that contains it, and a bias that serves a colony can harm its members.

A more intuitive take:

Adaptability bias: favouring flexible, generalist strategies that allow survival in a wide range of conditions, sometimes at the cost of efficiency or specialisation.
Behavioural bias: innate or learned behaviours favour certain responses over others, based on what has been adaptive in the past (e.g., predator avoidance, foraging preferences). Nest-building birds show the effect experimentally, where a prior material preference persists against both social and asocial information to the contrary.32
Communication bias: nonhuman communication systems (e.g., pheromones, bird song, root signalling) privilege certain types of information and may exclude others, shaping collective knowledge within a species or ecosystem. Chemical signalling in particular carries information that visually oriented observers routinely miss.35^,36
Ecological bias (bearer: organism): systematic tendencies in perception, behaviour, or knowledge shaped by an organism’s ecological niche and evolutionary history, which constrain what it can know or respond to in its environment. Behavioural syndromes tie these tendencies together, so a single organism's boldness or caution recurs across foraging, mating, and predator contexts rather than being tuned per situation.37^,38
Evolutionary bias (bearer: lineage): systematic tendencies shaped by evolutionary history, such as favouring traits that were adaptive in ancestral environments, which may not be optimal in current or changing conditions. Vestigial behaviours make the lag visible, persisting after the selective pressure that produced them has gone.39
Generality bias: evolving generalist traits or behaviours that allow use of diverse resources or habitats, sometimes at the cost of peak performance in any one context.
Innovation bias: tendency in some species or systems to favour novel behaviours, strategies, or mutations, which can drive rapid adaptation but may also increase risk. What counts as innovation is itself contested, which affects which species appear innovative.40
Opportunistic bias: preference for immediate, short-term gains or resources, sometimes at the expense of long-term stability or sustainability (e.g., boom-and-bust population cycles). Classical foraging theory formalises the discounting of future returns against immediate intake.41
Perceptual bias: species-specific sensory modalities (e.g., ultraviolet vision in bees, echolocation in bats) shape what information is salient or accessible to an organism. Colour vision varies so widely across taxa that a signal conspicuous to one viewer is invisible to another.42^,43
Social bias: in social species, group structure and hierarchies influence what information is shared, prioritised, or ignored (e.g., dominance hierarchies in primates or ants). Position in a dominance hierarchy determines both what an individual learns and what it can act on.44^,45
Affective bias: internal state shifts judgement under ambiguity. Agitated honeybees classify ambiguous odour cues pessimistically, which extends emotion-like biasing well beyond vertebrates and complicates any account that treats affective bias as a human trait.33
Specialisation bias: evolving highly specialised traits or behaviours that optimise performance in a narrow niche, potentially increasing vulnerability to environmental shifts. Niche conservatism describes the evolutionary stickiness that keeps lineages within inherited tolerances.46
Stability bias: favouring conservative, specialist strategies that maintain stability and efficiency in a consistent environment, but may reduce resilience to change. Niche conservatism gives the evolutionary form of the same tendency.46
Tameness bias: in domesticated or human-influenced environments, selection for tameness or reduced aggression, which can alter social structures, behaviour, and even cognition. The Belyaev fox experiment shows how fast the syndrome travels, since selecting on tameness alone dragged coat colour, ear carriage, skull shape, and hormonal cycles along with it.47
Temporal bias (bearer: organism or system): some organisms or systems are attuned to short-term cycles (e.g., circadian rhythms), while others are shaped by long-term processes (e.g., tree growth, geological change). Circadian organisation is itself an evolved bias about which periodicities matter.48
Wildness bias: in wild populations, selection for traits that favour independence, aggression, or avoidance of humans and novel situations. Urban populations show the counterpart, adjusting vigilance, foraging, and song to human presence,49 and fear of humans alone reshapes behaviour at landscape scale.50

A more technical take:

Homeostasis/allostasis bias: preference for maintaining internal set-points through stabilising responses, which can delay or damp adaptation under rapid change. Allostasis reframes this as predictive regulation, where the organism anticipates demand rather than correcting deviations after the fact, so the bias lies in what it predicts.51
Energy-efficiency (resource-rational) bias: reliance on low-cost heuristics and minimal sensing/processing to conserve energy, trading accuracy for frugality. This is the nonhuman case of the resource-rational account in the Definitions above.3
Supernormal-stimulus susceptibility: stronger responses to exaggerated cues than to natural ones (e.g., deceived pollinators drawn to intensified multimodal signals), a semiotic vulnerability exploitable by other organisms. Ecological traps are the applied case, where an attractive cue has been decoupled from the quality it once indicated.52
Signal-priority (conspecific) bias: privileging in-group signals and codes over heterospecific cues, reinforcing group identity and coordination while filtering external information. Public information from conspecifics is often cheaper than private sampling,53 and conspecific attraction is strong enough that habitat provision fails when settlers wait for cues from others.54
Self/non-self discrimination bias: strong default to treat unfamiliar signals or materials as “other,” shaping immune, developmental, and social responses. Plant self-incompatibility systems reject their own pollen,55 brood parasites exploit the same recognition thresholds in hosts,56 and immune discrimination now reads as a response to damage signals rather than to foreignness as such.57
Ecological inheritance (niche-scaffolding) bias: tendency to select, prefer, or perform better in environments previously modified by conspecifics/ancestors, reinforcing engineered habitat features across generations. Niche construction theory sets out the inheritance mechanism.58
Habitat-engineering feedback bias: niche construction that amplifies cues/resources an organism already detects well (e.g., microclimate tuning), creating self-reinforcing selection on perception and behaviour. Ecosystem engineers alter resource availability for other species, positively for some and negatively for others, so the feedback is never uniformly beneficial.59
Morphological affordance (structural coupling) bias: body plans channel sensing and action toward particular variables (e.g., bark-dwelling taxa tuned to fissure-scale cues), constraining what can be learned or done. Treating affordances as inferred action possibilities makes the constraint explicit, since what an agent can do is part of the model it uses to perceive.82
Quorum/threshold bias: collective decisions rely on simple thresholds (quorum, majority, “follow-the-most”), favouring rapid consensus over exhaustive evidence integration. Ant colonies emigrating to a new nest commit once a quorum of scouts is present rather than after comparing all options,60 and bacteria use the same logic to time collective behaviour.61
Conformist and prestige-copying bias: social learning weights common or high-status sources, accelerating spread of locally adaptive behaviours while risking lock-in. Deference freely conferred on skilled models is a distinct transmission channel from conformity to the majority.62
Risk-sensitive foraging bias: state-dependent shifts between risk-averse and risk-prone choices, coupling energetic condition to decision rules across taxa. Energetic state determines whether variance is worth accepting, so the same animal shifts between risk-averse and risk-prone within a day.41
Exploration–exploitation bias: adaptive oscillation between sampling novelty and repeating rewarded actions; context can over-bias toward either pole. The trade-off is documented across human and animal literatures alike.63
Imprinting/baseline bias: early-life exposure anchors later habitat choice, diet, or social preferences (e.g., natal homing), stabilising but narrowing option spaces. Imprinting is usefully read as a form of social learning rather than a separate mechanism.64
Redundancy-seeking (pattern) bias: preferential attention to repeated, predictable structure over noise, supporting robust communication but inviting pareidolic errors in novel conditions. Signal detection theory explains why receivers accept false alarms to avoid missed detections, which sets the bias by the relative cost of the two errors.65

Relevant Theories

Niche Construction Theory

Explores how organisms shape their own and each other’s selective environments, leading to systematic tendencies (biases) in evolution and ecology.

Constructed niches can themselves be biased. Where implicit bias is treated as an embodied perceptual habit, it scaffolds developmental niches that handicap the beings growing up inside them, which gives a mechanism by which social bias becomes environmental structure.34

Cf. Niche

Sensory Ecology, Umwelt, Biosemiotics, Ecosemiotics

The concept of umwelt describes how each organism experiences the world through its own sensory and perceptual biases, shaped by evolutionary history.

Biosemiotics theorises that all living systems interpret and respond to signs in their environment, leading to species- and context-specific biases in meaning-making and action.

Davies runs this argument through law. Living beings inherit ancestral norms and make new ones as they adapt, so normativity is plural across scales, and each being builds its umwelt by sensing, processing, and acting.100 Lyon supplies the bearer at its smallest, a bacterial cognitive toolkit that senses and perceives, attributes value, adapts behaviour, retains information, learns, anticipates, decides, and communicates.101 Meacham's criterion for cognition, which Davies quotes, is that a reaction follows from a selective process rather than from chemical necessity, which comes close to the definition used here, since it makes selection at a boundary the thing to look for.100 The same argument runs at the scale of what a collective can conceive in Imaginary.

Cf. Biosemiotics. Sensory Ecology

Collective Behaviour

Studies of animal societies (e.g., ants, bees, birds, fish) show how group structure, communication, and social learning create collective biases in decision-making and information flow.

Williams, Hannah J., Vivek H. Sridhar, Edward Hurme, Gabriella E. C. Gall, Natalia Borrego, Genevieve E. Finerty, Iain D. Couzin, et al. “Sensory Collectives in Natural Systems.” Edited by Meredith C. Schuman. eLife 12 (2023): e88028. doi:10/g9kdh4.

Couzin, Iain D. “Collective Cognition in Animal Groups.” Trends in Cognitive Sciences 13, no. 1 (2009): 36–43. doi:10/bnvxnw.

Developmental and Variational Bias

Evolutionary developmental biology uses the word in a different and more precise sense. Development does not offer selection an isotropic cloud of variants: some phenotypes are far easier to reach than others, so the organisation of development channels the direction evolution can take.83 This is variational rather than perceptual bias, and it operates before any selection occurs.

Two consequences matter here. Such bias is not only a constraint, since a developmental system that reliably produces functional variants makes adaptation faster rather than slower.84 And accumulated developmental biases can be read as something like learned priors, which is the sense in which evolution can be said to learn.85

Cf. Niche

Predictive Processing and Priors

A third vocabulary treats bias as the prior in an inference. Under the free energy principle, perception inverts a generative model the agent already holds, so what it expects partly determines what it perceives, and a prior is precisely a preference for some interpretations over others.86 The formulation is explicitly scale-free, which lets the same account run from cells to societies,87 and it has been applied to plants without a nervous system.88

This frame is useful here for one specific reason: it makes bias measurable as a divergence rather than only nameable as a tendency, and it applies to any system that maintains itself against surprise.

Bias Across Nested Scales

Naming the bearer raises a further question: how do biases at different levels relate? The literature exists, though it is written in the vocabulary of competency, agency, and decision-making rather than of bias.

The organising claim is the multi-scale competency architecture, in which biological systems occur as nested layers running from subcellular pathways through cells, tissues, and organs to organisms and swarms, with each layer solving problems in its own space.75^,76 Subunits are therefore agential materials rather than passive parts, and each carries an agenda of its own scale, which classical developmental biology called the struggle of the parts.77 Where the boundary of a self falls is itself a result rather than a given, since organs, cells, and molecular networks remain competent in their native contexts while being bound into a larger individual.78 Collective intelligence supplies the cross-cutting frame, treating swarms and cells under one set of concepts across scales and substrates.79

The consequence for this note is that bias does not simply aggregate upward. Cellular decision-making exploits noise to diversify an isogenic population, which is a bias against uniformity that benefits the collective at the expense of predictability for any individual cell.80 Microbial collectives reach decisions through distributed mechanisms with no centralised control, so the colony-level bias is not the sum of cell-level ones.81 A bias adaptive at one level can therefore be costly at the level above or below it, which makes the bearer a necessary part of any claim about bias.

This looks like a genuine gap. These literatures describe competency, valence, and decision-making across scales, and they do not systematically treat bias as a cross-scale phenomenon in its own right.

Cf. Agency, Biological Individuality, Intelligence

Cognitive Light Cones

An agent's goals reach only so far in space and time, and the cognitive light cone names that outer boundary.15 Radii and horizons vary enormously, so a root tip, a magpie, an elm, and a council operate at incommensurate scales. The asymmetries matter for design, since an elm represents no future at all while its effects run for centuries, whereas an institutional strategy represents a future explicitly on a horizon of years.

Cf. Intelligence, Design Potential (Private)

Evolutionary Constraints and Trade-offs

Evolutionary biology theorises that all organisms are subject to constraints and trade-offs (e.g., between specialisation and generality, adaptability and stability), which manifest as systematic biases in behaviour, physiology, and ecological roles.

Speed and accuracy trade off against each other across taxa, and the trade-off appears without a nervous system. Given a three-way food-quality discrimination task, an acellular slime mould decided faster under stress and was then more likely to select the worst available option.16 The same organism produces network designs comparable in efficiency to engineered ones.17 Bias in this sense follows from bounded information and bounded time rather than from any particular neural architecture.

Language

Payack, Paul J. A Million Words and Counting: How Global English Is Rewriting the World. New York: Kensington Books, 2013.

References

Henrich, Joseph, Steven J. Heine, and Ara Norenzayan. “The WEIRDest People in the World?” The Behavioral and Brain Sciences 33, nos. 2–3 (June 2010): 61–83; discussion 83-135. https://doi.org/10.1017/S0140525X0999152X.

Notes

Footnotes

De Domenico, Manlio. “Decoding the Architecture of Living Systems.” Reports on Progress in Physics 89, no. 1 (2026): 014601. https://doi.org/10.1088/1361-6633/ae2ca2.˄
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.˄
Lieder, Falk, and Thomas L. Griffiths. “Resource-Rational Analysis: Understanding Human Cognition as the Optimal Use of Limited Computational Resources.” Behavioral and Brain Sciences 43 (2020). https://doi.org/10.1017/s0140525x1900061x.˄
Kull, Kalevi, Nelly Mäekivi, Riin Magnus, Timo Maran, Lona Päll, and Tiit Remm. “Umwelt-Based Analysis of Multispecies Places: Guidelines for Application.” Biosemiotics 19, no. 2 (2026): 335–57. https://doi.org/10.1007/s12304-026-09644-2.˄
Levin, Michael. “The Computational Boundary of a ‘Self’: Developmental Bioelectricity Drives Multicellularity and Scale-Free Cognition.” Frontiers in Psychology 10 (2019). https://doi.org/10.3389/fpsyg.2019.02688.˄
Caves, Eleanor M., Stephen Nowicki, and Sönke Johnsen. “Von Uexküll Revisited: Addressing Human Biases in the Study of Animal Perception.” Integrative and Comparative Biology 59, no. 6 (2019): 1451–62. https://doi.org/10.1093/icb/icz073.˄
Greggor, Alison L., Alex Thornton, and Nicola S. Clayton. “Harnessing Learning Biases Is Essential for Applying Social Learning in Conservation.” Behavioral Ecology and Sociobiology 71, no. 1 (2016): 16. https://doi.org/10.1007/s00265-016-2238-4.˄
Buijs, Arjen, and Maarten Jacobs. “Avoiding Negativity Bias: Towards a Positive Psychology of Human–Wildlife Relationships.” Ambio 50, no. 2 (2021): 281–88. https://doi.org/10.1007/s13280-020-01394-w.˄
Christie, Alec P., David Abecasis, Mehdi Adjeroud, Juan C. Alonso, Tatsuya Amano, Alvaro Anton, Barry P. Baldigo, et al. “Quantifying and Addressing the Prevalence and Bias of Study Designs in the Environmental and Social Sciences.” Nature Communications 11, no. 1 (2020): 6377. https://doi.org/10.1038/s41467-020-20142-y.˄
Lambrechts, Marcel M., Frank Adriaensen, Daniel R. Ardia, Alexandr V. Artemyev, Francisco Atiénzar, Jerzy Bańbura, Emilio Barba, et al. “The Design of Artificial Nestboxes for the Study of Secondary Hole-Nesting Birds: A Review of Methodological Inconsistencies and Potential Biases.” Acta Ornithologica 45, no. 1 (2010): 1–26. https://doi.org/10.3161/000164510x516047.˄
Harper, Michael J., Michael A. McCarthy, Rodney van der Ree, and Julian C. Fox. “Overcoming Bias in Ground-Based Surveys of Hollow-Bearing Trees Using Double-Sampling.” Forest Ecology and Management 190, no. 2 (2004): 291–300. https://doi.org/10.1016/j.foreco.2003.10.022.˄
Christie, Alec P., Tatsuya Amano, Philip A. Martin, Silviu O. Petrovan, Gorm E. Shackelford, Benno I. Simmons, Rebecca K. Smith, David R. Williams, Claire F. R. Wordley, and William J. Sutherland. “The Challenge of Biased Evidence in Conservation.” Conservation Biology 35, no. 1 (2021): 249–62. https://doi.org/10.1111/cobi.13577.˄
Hughes, Alice C., Michael C. Orr, Keping Ma, Mark J. Costello, John Waller, Pieter Provoost, Qinmin Yang, Chaodong Zhu, and Huijie Qiao. “Sampling Biases Shape Our View of the Natural World.” Ecography 44, no. 9 (2021): 1259–69. https://doi.org/10.1111/ecog.05926.˄
Backstrom, Louis J., Corey T. Callaghan, Hannah Worthington, Richard A. Fuller, and Alison Johnston. “Estimating Sampling Biases in Citizen Science Datasets.” Ibis 167, no. 1 (2025): 73–87. https://doi.org/10.1111/ibi.13343.˄
Mandel, Roi, Eva Read, Björn Forkman, Linda J. Keeling, Peter Sandøe, Frank A. M. Tuyttens, and Christine J. Nicol. “Pragmatism Bias in Farm Animal Welfare Science.” Frontiers in Animal Science 7 (2026): 1841684. https://doi.org/10.3389/fanim.2026.1841684.˄
Sarkar, Dipto, and Colin A. Chapman. “The Smart Forest Conundrum: Contextualizing Pitfalls of Sensors and AI in Conservation Science for Tropical Forests.” Tropical Conservation Science 14 (2021): 19400829211014740. https://doi.org/10.1177/19400829211014740.˄
Dominoni, Davide M., Wouter Halfwerk, Emily Baird, Rachel T. Buxton, Esteban Fernández-Juricic, Kurt M. Fristrup, Megan F. McKenna, et al. “Why Conservation Biology Can Benefit from Sensory Ecology.” Nature Ecology & Evolution 4, no. 4 (2020): 502–11. https://doi.org/10.1038/s41559-020-1135-4.˄
Hausmann, Anna, Tuuli Toivonen, and Gonzalo Cortés-Capano. “A Reflexive Artificial Intelligence Governance for Transformative Change in Sustainability.” Ambio, 2026, 1–15. https://doi.org/10.1007/s13280-026-02405-y.˄
Wartmann, Flurina M., and Emma Cary. “Digital Nature in the AI Era: How Human and AI-Generated Representations Shape Future Visions of Rewilding.” People and Nature 8, no. 4 (2026): 919–34. https://doi.org/10.1002/pan3.70266.˄
Binley, Allison D., and Joseph R. Bennett. “The Data Double Standard.” Methods in Ecology and Evolution 14, no. 6 (2023): 1389–97. https://doi.org/10.1111/2041-210x.14110.˄
Mayson, Sandra G. “Bias In, Bias Out.” The Yale Law Journal 50, no. 1 (2020): 63–90.˄
Hagendorff, Thilo, Leonie N. Bossert, Yip Fai Tse, and Peter Singer. “Speciesist Bias in AI: How AI Applications Perpetuate Discrimination and Unfair Outcomes against Animals.” AI and Ethics 3, no. 3 (2023): 717–34. https://doi.org/10.1007/s43681-022-00199-9.˄
Takeshita, Masashi, Rafal Rzepka, and Kenji Araki. Speciesist Language and Nonhuman Animal Bias in English Masked Language Models. arXiv:2203.05140. arXiv, 2022.˄
Bowler, Diana E., Robin J. Boyd, Corey T. Callaghan, Robert A. Robinson, Nick J. B. Isaac, and Michael J. O. Pocock. “Treating Gaps and Biases in Biodiversity Data as a Missing Data Problem.” Biological Reviews 100, no. 1 (2025): 50–67. https://doi.org/10.1111/brv.13127.˄
Cooper, S. Barry. “The Mathematician's Bias - and the Return to Embodied Computation.” In A Computable Universe: Understanding and Exploring Nature as Computation, edited by Hector Zenil, 125–42. Singapore: World Scientific, 2013.˄
Guénard, Benoit, Alice C. Hughes, Claudianne Lainé, Stefano Cannicci, Bayden D. Russell, and Gray A. Williams. “Limited and Biased Global Conservation Funding Means Most Threatened Species Remain Unsupported.” Proceedings of the National Academy of Sciences 122, no. 9 (2025): e2412479122. https://doi.org/10/g9mrn6.˄
Adams, William M. “Digital Surveillance of Animals and Nature Recovery.” People and Nature, ahead of print, 2026. https://doi.org/10.1002/pan3.70348.˄
Scotto, Carolina. “The Anthropocentric Bias in Animal Cognition.” ArtefaCToS. Revista de Estudios Sobre La Ciencia y La Tecnología 13, no. 1 (2024): 85–116. https://doi.org/10.14201/art2024.31800.˄
Andrews, Kristin, and Brian Huss. “Anthropomorphism, Anthropectomy, and the Null Hypothesis.” Biology & Philosophy 29, no. 5 (2014): 711–29. https://doi.org/10.1007/s10539-014-9442-2.˄
Bekoff, Marc. “Animal Emotions: Exploring Passionate Natures.” BioScience 50, no. 10 (2000): 861–70. https://doi.org/10.1641/0006-3568(2000)050[0861:aeepn]2.0.co;2.˄
Fricker, Miranda. Epistemic Injustice: Power and the Ethics of Knowing. Oxford: Oxford University Press, 2007.˄
Lopez, Andrew. “Nonhuman Animals and Epistemic Injustice.” Journal of Ethics and Social Philosophy 25, no. 1 (2023): 136–63. https://doi.org/10.26556/jesp.v25i1.2201.˄
Birch, Jonathan. “Animal Sentience and the Precautionary Principle.” Animal Sentience 16, no. 1 (2017): 1–15.˄
Segundo-Ortin, Miguel, and Paco Calvo. “Plant Sentience? Between Romanticism and Denial: Science.” Animal Sentience 8, no. 33 (2023). https://doi.org/10.51291/2377-7478.1772.˄
Clawson, Wesley P., and Michael Levin. “Endless Forms Most Beautiful 2.0: Teleonomy and the Bioengineering of Chimaeric and Synthetic Organisms.” Biological Journal of the Linnean Society 139, no. 4 (2023): 457–86. https://doi.org/10.1093/biolinnean/blac073.˄
Levin, Michael. “Bioelectric Networks: The Cognitive Glue Enabling Evolutionary Scaling from Physiology to Mind.” Animal Cognition, ahead of print, 2023. https://doi.org/10.1007/s10071-023-01780-3.˄
Fields, Chris, and Michael Levin. “Competency in Navigating Arbitrary Spaces as an Invariant for Analyzing Cognition in Diverse Embodiments.” Entropy 24, no. 6 (2022): 819. https://doi.org/10.3390/e24060819.˄
Fields, Chris, and Michael Levin. “Life, Its Origin, and Its Distribution: A Perspective from the Conway-Kochen Theorem and the Free Energy Principle.” Communicative & Integrative Biology 18, no. 1 (2025): 2466017. https://doi.org/10.1080/19420889.2025.2466017.˄
Smith, Tobias J., and Manu E. Saunders. “Honey Bees: The Queens of Mass Media, Despite Minority Rule Among Insect Pollinators.” Insect Conservation and Diversity 9, no. 5 (2016): 384–90. https://doi.org/10.1111/icad.12178.˄
Raja, Nussaïbah B., Emma M. Dunne, Aviwe Matiwane, Tasnuva Ming Khan, Paulina S. Nätscher, Aline M. Ghilardi, and Devapriya Chattopadhyay. “Colonial History and Global Economics Distort Our Understanding of Deep-Time Biodiversity.” Nature Ecology & Evolution 6, no. 2 (2022): 145–54. https://doi.org/10.1038/s41559-021-01608-8.˄
Mammola, Stefano, Isabel R. Amorim, Maria E. Bichuette, Paulo A. V. Borges, Naowarat Cheeptham, Steven J. B. Cooper, David C. Culver, et al. “Fundamental Research Questions in Subterranean Biology.” Biological Reviews 95, no. 6 (2020): 1855–72. https://doi.org/10.1111/brv.12642.˄
Mascaro, Joseph, Creighton M. Litton, R. Flint Hughes, Amanda Uowolo, and Stefan A. Schnitzer. “Minimizing Bias in Biomass Allometry: Model Selection and Log-Transformation of Data: Model Bias in Tree Allometry.” Biotropica 43, no. 6 (2011): 649–53. https://doi.org/10.1111/j.1744-7429.2011.00798.x.˄
Fletcher, Michael-Shawn, Rebecca Hamilton, Wolfram Dressler, and Lisa Palmer. “Indigenous Knowledge and the Shackles of Wilderness.” Proceedings of the National Academy of Sciences 118, no. 40 (2021): e2022218118. https://doi.org/10.1073/pnas.2022218118.˄
Leandro, Camila, Pierre Jay-Robert, and Alan Vergnes. “Bias and Perspectives in Insect Conservation: A European Scale Analysis.” Biological Conservation 215 (2017): 213–24. https://doi.org/10.1016/j.biocon.2017.07.033.˄
Goldstein, Jenny E., Dan Brockington, Chris Sandbrook, Patrick Meyfroidt, Jonas Geldmann, Tobias Kuemmerle, Marion Pfeifer, et al. “Environmental Data Justice Is Key for Developing More Effective Area-Based Conservation Approaches.” Nature Reviews Biodiversity 2, no. 2 (2026): 116–26. https://doi.org/10.1038/s44358-025-00126-w.˄
Sterling, Eleanor J., Christopher Filardi, Anne Toomey, Amanda Sigouin, Erin Betley, Nadav Gazit, Jennifer Newell, et al. “Biocultural Approaches to Well-Being and Sustainability Indicators Across Scales.” Nature Ecology & Evolution 1, no. 12 (2017): 1798–806. https://doi.org/10.1038/s41559-017-0349-6.˄
Giaccardi, Elisa, Stanislav Roudavski, and Martín Tironi. “Participation and the Troubles with Intelligence.” PDC '26: Proceedings of the 19th Participatory Design Conference 2026 1 (2026): 385–90. https://doi.org/10.1145/3796624.3796632.˄
Allain, Sandrine, Lucas Berard-Chenu, Thomas Bolognesi, Isabelle Boulangeat, Arnaud Buchs, Jonathan Cognard, Matthieu Combaud, Grace Kassis, Jean-François Ruault, and Yves Schaeffer. “‘Limits’ at the Human–Nature Interface: A Meta-Ethnographic Review.” Ecology and Society 31, no. 1 (2026). https://doi.org/10.5751/ES-16991-310121.˄
Biermann, Frank, and Rakhyun E. Kim. “The Boundaries of the Planetary Boundary Framework: A Critical Appraisal of Approaches to Define a ‘Safe Operating Space’ for Humanity.” Annual Review of Environment and Resources 45 (2020): 497–521. https://doi.org/10.1146/annurev-environ-012320-080337.˄
Whittaker, Ben, Gopika Balasubramanian, Andres Camacho-Alpizar, Connor Lambert, and Lauren Guillette. “The Roles of Social Information, Asocial Information, and Initial Bias in Nest-Building Decisions.” Journal of Comparative Psychology, ahead of print, 2024. https://doi.org/10.1037/com0000374.˄
Karban, Richard. Plant Sensing and Communication. Chicago: University of Chicago Press, 2015.˄
Wyatt, Tristram Dick. Pheromones and Animal Behavior: Chemical Signals and Signature Mixes. 2nd ed. Cambridge: Cambridge University Press, 2014.˄
Sih, Andrew, and Marco Del Giudice. “Linking Behavioural Syndromes and Cognition: A Behavioural Ecology Perspective.” Philosophical Transactions of the Royal Society B: Biological Sciences 367, no. 1603 (2012): 2762–72. https://doi.org/10.1098/rstb.2012.0216.˄
Stevens, Martin. Sensory Ecology, Behaviour, and Evolution. Oxford: Oxford University Press, 2013.˄
Rayner, Jack G., Samantha L. Sturiale, and Nathan W. Bailey. “The Persistence and Evolutionary Consequences of Vestigial Behaviours.” Biological Reviews 97, no. 4 (2022): 1389–407. https://doi.org/10/hbbc96.˄
Ramsey, Grant, Meredith L. Bastian, and Carel van Schaik. “Animal Innovation Defined and Operationalized.” Behavioral and Brain Sciences 30, no. 4 (2007): 393–407. https://doi.org/10.1017/s0140525x07002373.˄
Stephens, David W., and John R. Krebs. Foraging Theory. Princeton: Princeton University Press, 1986.˄
Cuthill, Innes C., William L. Allen, Kevin Arbuckle, Barbara Caspers, George Chaplin, Mark E. Hauber, Geoffrey E. Hill, et al. “The Biology of Color.” Science 357, no. 6350 (2017): eaan0221. https://doi.org/10.1126/science.aan0221.˄
Uexküll, Jakob von. A Foray into the Worlds of Animals and Humans: With a Theory of Meaning. Translated by Joseph D. O'Neil. 1934; Minneapolis: University of Minnesota Press, 2010.˄
Duboscq, Julie, Valéria Romano, Andrew MacIntosh, and Cédric Sueur. “Social Information Transmission in Animals: Lessons from Studies of Diffusion.” Frontiers in Psychology 7 (2016): 01147. https://doi.org/10.3389/fpsyg.2016.01147.˄
Hobson, Elizabeth A. “Differences in Social Information Are Critical to Understanding Aggressive Behavior in Animal Dominance Hierarchies.” Current Opinion in Psychology, Power, Status and Hierarchy, vol. 33 (2020): 209–15. https://doi.org/10.1016/j.copsyc.2019.09.010.˄
Bateson, Melissa, Suzanne Desire, Sarah E. Gartside, and Geraldine A. Wright. “Agitated Honeybees Exhibit Pessimistic Cognitive Biases.” Current Biology 21, no. 12 (2011): 1070. https://doi.org/10.1016/j.cub.2011.05.017.˄
Wiens, John J., and Catherine H. Graham. “Niche Conservatism: Integrating Evolution, Ecology, and Conservation Biology.” Annual Review of Ecology, Evolution, and Systematics 36 (2005): 519–39. https://doi.org/10.1146/annurev.ecolsys.36.102803.095431.˄
Dugatkin, Lee Alan, and Lyudmila Trut. How to Tame a Fox (and Build a Dog): Visionary Scientists and a Siberian Tale of Jump-Started Evolution. Chicago: University of Chicago Press, 2017.˄
Pittendrigh, Colin S. “Temporal Organization: Reflections of a Darwinian Clock-Watcher.” Annual Review of Physiology 55 (1993): 17–54. https://doi.org/10/c4wdhq.˄
Lowry, Hélène, Alan Lill, and Bob B. M. Wong. “Behavioural Responses of Wildlife to Urban Environments.” Biological Reviews 88, no. 3 (2013): 537–49. https://doi.org/10.1111/brv.12012.˄
Zanette, Liana Y., and Michael Clinchy. “Ecology of Fear.” Current Biology 29, no. 9 (2019): R309–13. https://doi.org/10.1016/j.cub.2019.02.042.˄
Sterling, Peter. “Allostasis: A Model of Predictive Regulation.” Physiology & Behavior, Allostasis and Allostatic Load, vol. 106, no. 1 (2012): 5–15. https://doi.org/10.1016/j.physbeh.2011.06.004.˄
Schlaepfer, Martin A., Michael C. Runge, and Paul W. Sherman. “Ecological and Evolutionary Traps.” Trends in Ecology & Evolution 17, no. 10 (2002): 474–80. https://doi.org/10/d4nbgd.˄
Danchin, Étienne, Luc-Alain Giraldeau, Thomas J. Valone, and Richard H. Wagner. “Public Information: From Nosy Neighbors to Cultural Evolution.” Science 305, no. 5683 (2004): 487–91. https://doi.org/10.1126/science.1098254.˄
Ahlering, Marissa A., and John Faaborg. “Avian Habitat Management Meets Conspecific Attraction: If You Build It, Will They Come?” The Auk 123, no. 2 (2006): 301–12. https://doi.org/10.1093/auk/123.2.301.˄
Fujii, Sota, Ken-ichi Kubo, and Seiji Takayama. “Non-Self- and Self-Recognition Models in Plant Self-Incompatibility.” Nature Plants 2, no. 9 (2016): 16130. https://doi.org/10.1038/nplants.2016.130.˄
Kilner, Rebecca M., and Naomi E. Langmore. “Cuckoos versus Hosts in Insects and Birds: Adaptations, Counter-Adaptations and Outcomes.” Biological Reviews 86, no. 4 (2011): 836–52. https://doi.org/10.1111/j.1469-185x.2010.00173.x.˄
Kroemer, Guido, Léa Montégut, Oliver Kepp, and Laurence Zitvogel. “The Danger Theory of Immunity Revisited.” Nature Reviews Immunology 24, no. 12 (2024): 912–28. https://doi.org/10.1038/s41577-024-01102-9.˄
Odling-Smee, F. John, Kevin N. Laland, and Marcus W. Feldman. Niche Construction: The Neglected Process in Evolution. Princeton: Princeton University Press, 2003.˄
Jones, Clive G., John H. Lawton, and Moshe Shachak. “Positive and Negative Effects of Organisms as Physical Ecosystem Engineers.” Ecology 78, no. 7 (1997): 1946–57. https://doi.org/10.1890/0012-9658(1997)078[1946:paneoo]2.0.co;2.˄
Friston, Karl. “Affordance and Active Inference.” In Affordances in Everyday Life: A Multidisciplinary Collection of Essays, edited by Zakaria Djebbara, 211–19. Cham: Springer, 2022. https://doi.org/10.1007/978-3-031-08629-8_20.˄
Pratt, Stephen C., Eamonn B. Mallon, David J. Sumpter, and Nigel R. Franks. “Quorum Sensing, Recruitment, and Collective Decision-Making during Colony Emigration by the Ant Leptothorax albipennis.” Behavioral Ecology and Sociobiology 52, no. 2 (2002): 117–27. https://doi.org/10.1007/s00265-002-0487-x.˄
Waters, Christopher M., and Bonnie L. Bassler. “Quorum Sensing: Cell-to-Cell Communication in Bacteria.” Annual Review of Cell and Developmental Biology 21 (2005): 319–46. https://doi.org/10.1146/annurev.cellbio.21.012704.131001.˄
Henrich, Joseph, and Francisco J. Gil-White. “The Evolution of Prestige: Freely Conferred Deference as a Mechanism for Enhancing the Benefits of Cultural Transmission.” Evolution and Human Behavior 22, no. 3 (2001): 165–96. https://doi.org/10/cbhgdz.˄
Mehlhorn, Katja, Ben R. Newell, Peter M. Todd, Michael D. Lee, Kate Morgan, Victoria A. Braithwaite, Daniel Hausmann, Klaus Fiedler, and Cleotilde Gonzalez. “Unpacking the Exploration–Exploitation Tradeoff: A Synthesis of Human and Animal Literatures.” Decision 2, no. 3 (2015): 191–215. https://doi.org/10/gfrktv.˄
Johnston, Timothy D. “Imprinting as Social Learning.” In Oxford Research Encyclopedia of Psychology, edited by Frank C. Worrel. Oxford: Oxford University Press, 2020.˄
Wiley, R. Haven. “Signal Detection and Animal Communication.” Advances in the Study of Behavior 36 (2006): 217–47. https://doi.org/10.1016/s0065-3454(06)36005-6.˄
Carvalho, Felipe Nogueira de, and Joel Krueger. “Biases in Niche Construction.” Philosophical Psychology 37, no. 6 (2024): 1297–327. https://doi.org/10.1080/09515089.2023.2237065.˄
Davies, Margaret. “A Plural Nomos: Law, Life, and Knowledge.” Law and Critique 37, no. 1 (2026): 87–108. https://doi.org/10.1007/s10978-024-09392-7.˄
Lyon, Pamela. “The Cognitive Cell: Bacterial Behavior Reconsidered.” Frontiers in Microbiology 6 (2015): 264. https://doi.org/10.3389/fmicb.2015.00264.˄
Houle, David, and Daniela M. Rossoni. “Complexity, Evolvability, and the Process of Adaptation.” Annual Review of Ecology, Evolution, and Systematics 53, no. 1 (2022): 137–59. https://doi.org/10.1146/annurev-ecolsys-102320-090809.˄
Watson, Richard A. “Evolvability.” In Evolutionary Developmental Biology, edited by Laura Nuño De La Rosa and Gerd B. Müller, 133–48. Cham: Springer, 2021. https://doi.org/10.1007/978-3-319-32979-6_184.˄
Watson, Richard A., and Eörs Szathmáry. “How Can Evolution Learn?” Trends in Ecology & Evolution 31, no. 2 (2016): 147–57. https://doi.org/10.1016/j.tree.2015.11.009.˄
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.˄
Friston, Karl, Conor Heins, Tim Verbelen, Lancelot Da Costa, Tommaso Salvatori, Dimitrije Markovic, Alexander Tschantz, Magnus Koudahl, Christopher Buckley, and Thomas Parr. “From Pixels to Planning: Scale-Free Active Inference.” Frontiers in Network Physiology 5 (2025): 1521963. https://doi.org/10.3389/fnetp.2025.1521963.˄
Calvo, Paco, and Karl Friston. “Predicting Green: Really Radical (Plant) Predictive Processing.” Journal of The Royal Society Interface 14, no. 131 (2017): 20170096. https://doi.org/10.1098/rsif.2017.0096.˄
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.˄
Levin, Michael. “Darwin's Agential Materials: Evolutionary Implications of Multiscale Competency in Developmental Biology.” Cellular and Molecular Life Sciences 80, no. 6 (2023): 142. https://doi.org/10.1007/s00018-023-04790-z.˄
Clawson, Wesley P., and Michael Levin. “Endless Forms Most Beautiful 2.0: Teleonomy and the Bioengineering of Chimaeric and Synthetic Organisms.” Biological Journal of the Linnean Society 139, no. 4 (2023): 457–86. https://doi.org/10.1093/biolinnean/blac073.˄
McMillen, Patrick, and Michael Levin. “Collective Intelligence: A Unifying Concept for Integrating Biology across Scales and Substrates.” Communications Biology 7, no. 1 (2024): 378. https://doi.org/10.1038/s42003-024-06037-4.˄
Balázsi, Gábor, Alexander van Oudenaarden, and James J. Collins. “Cellular Decision Making and Biological Noise: From Microbes to Mammals.” Cell 144, no. 6 (2011): 910–25. https://doi.org/10.1016/j.cell.2011.01.030.˄
Ross-Gillespie, Adin, and Rolf Kümmerli. “Collective Decision-Making in Microbes.” Frontiers in Microbiology 5 (2014): 00054. https://doi.org/10/gfzwnt.˄
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.˄
Latty, Tanya, and Madeleine Beekman. “Speed–Accuracy Trade-Offs during Foraging Decisions in the Acellular Slime Mould Physarum polycephalum.” Proceedings of the Royal Society B: Biological Sciences 278, no. 1705 (2011): 539–45. https://doi.org/10.1098/rspb.2010.1624.˄
Tero, Atsushi, Seiji Takagi, Tetsu Saigusa, Kentaro Ito, Dan P. Bebber, Mark D. Fricker, Kenji Yumiki, Ryo Kobayashi, and Toshiyuki Nakagaki. “Rules for Biologically Inspired Adaptive Network Design.” Science 327, no. 5964 (2010): 439–42. https://doi.org/10.1126/science.1177894.˄

Backlinks