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Uncertainty

This note examines uncertainty, especially in future-oriented approaches to design that involve all forms of life.

Definitions and Approach

The concept of uncertainty can refer to limits in knowledge, variability in systems, indeterminacy in processes, ambiguity in interpretation, or plurality in values and futures. Different disciplines frame it differently, for example as measurable probabilities in statistics, deep uncertainty in policy and futures studies, uncertainty propagation in complex systems, or lived uncertainty in anthropology and geography.

A practical working definition can be:

Uncertainty is a relational condition of living, knowing, and designing within worlds that are at the same time materially indeterminate, epistemically partial, socially filtered, and ontologically plural.1, 2, 3, 4

In this view, more-than-human design is a set of practices that stay open to plural knowledges, nonhuman agencies, and forms of life that can alter both the problem and the criteria of a good future.5, 6, 7, 8, 9, 10

Types of Uncertainty

Core Types

  • Ontic uncertainty (world-process indeterminacy): indeterminacy in material processes, from quantum and physical dynamics to open-ended ecological change.
  • Aleatory uncertainty (stochastic variability): irreducible variability in systems, including weather, populations, and ecological interactions.
  • Epistemic uncertainty (knowledge limits): uncertainty from missing data, partial models, weak proxies, and limited observation.
  • Model and structural uncertainty: uncertainty about model form, assumptions, scale transfer, boundary conditions, and causal structure.
  • Semantic and interpretive uncertainty: uncertainty from contested concepts, categories, and translation between knowledge systems.
  • Normative and political uncertainty: uncertainty about values, priorities, legitimacy, and who should decide under conflict.
  • Strategic uncertainty: uncertainty created by adaptive behaviour of other agents, institutions, and technical systems.
  • Temporal uncertainty (future openness): uncertainty from path dependence, feedbacks, tipping dynamics, and long time horizons.
  • More-than-human agency uncertainty: uncertainty produced by nonhuman perception, signalling, adaptation, and co-agency that remain only partly legible in human or cross-being epistemic systems.

Cross-Cutting Distinctions

  • Risk, uncertainty, and ignorance: risk has known outcomes with estimable probabilities; uncertainty has unknown probabilities; ignorance includes unknown unknowns.
  • Shallow versus deep uncertainty: under deep uncertainty, actors do not agree on models, probabilities, or values.
  • Reducible versus irreducible uncertainty: some uncertainty can be reduced through better observation or modelling; some should be managed through robustness, adaptability, and plural strategies.

Comparison of Uncertainty Types

TypeSourceHow to studyDesign responseFailure mode
Onticmaterial indeterminacyobservation, physical experimentkeep options open, avoid lock-infalse determinism
Aleatorystochastic variabilitystatistics, sampling, ensemblesbuffers, redundancy, safety marginsunderestimated variance
Epistemicmissing data, weak proxiestargeted measurement, value of informationmonitoring, iterative learningoverconfident estimates
Model and structuralmodel form, scale transfermulti-model comparison, sensitivity analysisuse multiple models, stress-testsingle-model dependence
Semantic and interpretivecontested concepts, translationconceptual analysis, cross-cultural dialogueshared glossaries, boundary objectshidden category errors
Normative and politicalvalue conflict, legitimacydeliberation, participatory methodsinclusive and contestable governancetechnocratic capture
Strategicother agents adaptingscenario play, strategic analysisadaptive, monitorable commitmentsbrittle fixed plans
Temporalfeedbacks, path dependencescenarios, foresight, backcastingreversibility, adaptive pathwaysdiscounting long-term harm
More-than-human agencynonhuman perception, signallingmultispecies, situated sensinguncertainty-tagged signals, co-designtreating signals as noise

Side Benefits of Uncertainty

  • building solidarities
  • cultivating humility
  • supporting adaptive and reversible design
  • broadening participation across knowledge systems
  • creating space for experimentation and learning

Academic Discussions

Main Academic Threads

  • Statistics, engineering, and risk analysis: focus on quantification, propagation, sensitivity, and confidence.
  • Ecology and Earth systems: focus on complexity, scale interactions, thresholds, and scenario diversity.
  • Decision sciences and policy: focus on robust decision-making under deep uncertainty rather than prediction alone.
  • Anthropology and geography: focus on lived uncertainty, social negotiation, and situated response capacities.
  • Science and technology studies (STS): focus on how institutions produce, distribute, and legitimise uncertainty claims.
  • Design and planning: focus on iterative pathways, trial-and-error prototypes, and governance that remains revisable.

From Canonical Uncertainty to More-than-Human Design

Canonical frameworks treat uncertainty as a problem for human decision-makers who manage an external world. More-than-human design widens this frame and asks how plural knowledges, nonhuman agencies, and open-ended ecologies reshape both the problem and the criteria for a good future. The matrix below maps the shift from canonical typologies toward design that shares agency across species.

StrandCore moveWhat counts as uncertaintyMain contribution for more-than-human design
Canonical typologiesBuild diagnostic maps of uncertainty by location, level, and nature1, 11Missing data, model and scenario uncertainty, ignorance, and the split between epistemic and ontological uncertainty1, 12Gives a disciplined way to ask whether a problem needs better knowledge, broader scenarios, or adaptation to irreducible indeterminacy
Governance and post-normal scienceShift from prediction alone to action in contested, high-stakes settings13, 14, 2Uncertainty in systems, institutions, strategies, and public frames14Shows that design needs participation, precaution, flexibility, and learning, and cannot rely on optimisation alone
Relational and ignorance-centred approachesTreat uncertainty as a relation between knowers, objects, and institutions3, 15Knowing too little, knowing too differently, and not knowing because ignorance is organised or strategic3, 15Warns that design can misdiagnose conflict as a data gap when the real issue is plural framing or produced non-knowledge
Traditional and Indigenous knowledgeTreat knowledge as practical, historical, ethical, and cosmological at once16, 17, 18Uncertainty emerges when only the factual layer is extracted and the wider system of values, practices, and cosmology is stripped away16, 18Pushes design toward partnership with whole knowledge systems, beyond selective uptake of local observations
Pluriversal and ontological workTreat disagreements as conflicts about what exists, not only how one world is interpreted19, 4, 5, 20, 21Uncertainty arises at the meeting point of partially connected worlds and incompatible ontologies4, 5Recasts design as diplomatic coordination across worlds, beyond consensus-building inside one ontology
Multispecies and other-than-human agencyMove animals, plants, microbes, rivers, materials, and ecological processes from background to participant6, 22, 9, 23Uncertainty includes not knowing how nonhuman agencies perceive, respond, interrupt, or co-produce outcomes6, 22Opens the possibility that design follows nonhuman signals, rhythms, constraints, and interventions alongside human needs
More-than-human design methodsTranslate these arguments into participatory and design frameworks24, 25, 7, 8, 26, 27, 28Uncertainty includes not knowing how to represent, sense, or share agency with nonhuman participants without reducing them to proxies24, 25Supplies practical methods for attunement, mediation, representation, autonomy, and commoning across species

Canonical approaches

Canonical typologies sort uncertainty by location, level, and nature, and this architecture recurs even when labels vary.1, 11 (See Decision Making.)

  • They separate cases where probabilities help from cases where the outcome space or the causal model is itself unstable.29, 12
  • The distinction guides response: some situations call for better estimation, while others call for precaution, scenario thinking, or institutional buffering against surprise.2 (See Scenarios and Robustness.)
  • Governance research adds that uncertainty can be substantive, strategic, or institutional, and that each form can be epistemic, ontological, or ambiguous.14
  • For design, more data alone cannot resolve some problems or conflicts.

Anthropocentrism in the mainstream matrix

The mainstream matrix stays human-centred. Even when it admits ambiguity or ignorance, it assumes that the task is to help human decision-makers manage an external world.

  • Relational accounts locate uncertainty in the ties between a system and the many knowers who frame it differently, as well as in the system itself.3 (See Knowledge.)
  • Institutions also produce ignorance by maintaining, filtering, or strategically preserving some unknowns.15
  • Design failures and harms therefore often begin upstream, when the framing of a problem erases actors, knowledges, or consequences before deliberation starts.
  • Once uncertainty includes ignorance and the indeterminacy of open, socially mediated fields, designers can no longer justify a design by applying settled knowledge to passive matter or living recipients.2

Pluriversal knowledge

Traditional ecological knowledge holds more than observations. It also carries management systems, histories of land use, ethics, identity, and cosmology.17, 18 (See Indigenous.)

  • Many institutions admit only the factual layer, so a design process can look inclusive while it strips away the conditions that make the knowledge meaningful.30
  • Epistemic practices across different traditions often overlap in practice, which adds useful redundancy.16
  • Plural epistemologies can supply problem definitions, criteria of success, and modes of relation, so designers gain more than a store of extra facts for a fixed frame, such as the prior approval of intensive animal husbandry, carnivorous diets, or dominant economic relationships.
  • This move widens the frame, yet it still presumes human decision-makers who work with several forms of knowledge.

From plural knowledge to multiple worlds

Ontological and cosmopolitical work argues that disagreement reaches beyond misaligned beliefs about one world. It also concerns which beings are present and which relations count as real.19, 4, 5 (See Ontology.)

  • Andean worlds, for example, treat mountains as participants in collective life.
  • Comparable disputes appear in science and policy when people contest the reality or the symptoms of entities such as individuality, selection, evolution, or innovation.
  • Ontological accountability makes inclusivity depend on questioning the assumptions built into the categories, practices, and institutions through which policy defines reality.21
  • The aim then shifts from consensus within one ontology to negotiated coexistence across worlds.5, 20

Nonhuman beings as knowers

Many nonhuman beings hold and pass on knowledge, so their loss is an epistemic harm as well as an ecological one. (See Knowledge and Agency.)

  • Animals know through know-how, social learning, and membership in epistemic communities, so destroying migration routes or killing older, knowledgeable animals breaks the transmission of animal knowledge.31, 32
  • Plants, fungi, and bryophytes pose a further challenge, since their attentiveness and response resist human-like models of cognition.33
  • Microbes deepen the problem, because the relevant lifeforms are invisible, distributed, fast-changing, and entangled with human bodies and infrastructures.22, 34, 35 Access to healthy microbiomes depends on housing, food, pollution, and labour, so microbial life sits within environmental and social justice (see Microbe and Justice).22
  • Metagenomics can build a feeling for the microbiome through sequencing, sensory attention, and household experiments, which unsettles germ-war metaphors and supports more hesitant, ecological relations.34
  • Fermentation studies show microbes as collaborators in place-based culture, where people cultivate microbial refugia and live within feedback loops of care, food, and management.35
  • Communities, systems, and places extend the same difficulty to larger scales. River co-learning arenas, for instance, treat rivers as participants, subjects, and warning presences whose dynamics shape what counts as relevant knowledge.23

Eels as a worked example

Eel movement reveals forms of sensing, preference, and vulnerability that can shape infrastructure design.36, 37 (See Eel.)

  • European eels respond to flow acceleration and other hydrodynamic conditions. Under abrupt acceleration they often reject a passage and turn back upstream; under lower acceleration they explore laterally and search more productively.36
  • This knowledge-bearing behaviour shows how an eel reads a river passage.
  • Other factors include variation within eel populations, exhaustion thresholds, slope, discharge, and the continuity of passable paths, so design must represent eel needs as distributed, relational, and variable.37
  • Eels give specific, technical guidance, which makes them a strong test of method: a design that translates their needs into coarse human abstractions will fail by ignoring the animals’ acute sensing.

How nonhuman beings can lead design

Mode of nonhuman leadershipWhat it meansConsequence for design
Epistemic guidesAnimal know-how, plant attentiveness, river warnings, microbial shifts, and seasonal rhythms can flag conditions that formal monitoring misses or notices too late32, 33, 22, 23Design should begin from situated ecological signals and long field attention, not only from abstract metrics
Ontological anchorsRivers, mountains, forests, and other beings may count as living participants whose status changes what the design problem is19, 4, 5, 21Design criteria must include the maintenance of relations, obligations, and life-supporting reciprocity, not only efficient resource use
Co-designersAnimals, plants, microbes, and technical mediators can shape siting, habitat, temporal sequencing, and acceptable forms of intervention7, 8, 27Design should open channels through which nonhuman preferences and capacities materially alter proposals
Intervening agentsDroughts, pathogens, fires, and other ecological processes can redirect research, governance, and collaboration in ways human planning did not foresee6, 9Design should stay adaptive and interruptible, leaving room for ecological feedback to redirect action

Methods of inclusion

Method familyHow it worksWhat it helps withMain caution
Ontological auditingQuestion the assumptions built into key terms, policies, and categories21Reveals who or what a design process cannot presently seeCan become procedural without real redistribution of authority
Attunement and arts of noticingUse situated observation, sensing, walking, and narrative methods to learn habitats, rhythms, and signs33, 27Helps register nonhuman temporalities and needs before formal abstractionRisks romantic projection without ecological and social specificity
Counter-mapping and co-learningBuild maps, stories, and arenas where local and more-than-human relations can challenge official models23, 27Surfaces hidden barriers, asymmetries, and alternative problem definitionsCan still recentre humans if nonhuman participation stays symbolic
Personas and proxy coalitionsBuild structured archetypes for species and validate them through coalitions of scientists, local actors, and advocates24Makes nonhuman stakeholders discussable within processes that otherwise exclude themPersonas can flatten difference, hide uncertainty, or smuggle in designer bias
Bioinclusive participation frameworksCombine human proxies, direct participation, and expanded notions of stakeholder and designer25, 7Gives a stronger normative basis for nonhuman inclusion and power-sharingCommunication stays asymmetric and often mediated by humans
Technical mediationUse sensors, digital twins, monitoring systems, computational models, or hydraulic experiments to translate nonhuman signals into decisions36, 37, 8, 28Makes long or hard-to-sense processes actionable in designCan intensify extraction or algorithmic control if framed only by human priorities28
Participation beyond invitationTreat design as already entangled with more-than-human participants and reorganise processes accordingly26Moves from consultation toward ongoing answerabilityRequires slower, less managerial practice

No method solves nonhuman representation once and for all. A method earns trust when it stays open to correction by nonhuman response, ecological feedback, and the situated knowledge of those who already live closely with these beings. (See Participation and Deliberation.)

Uncertainty Justice

Power runs through uncertainty and shapes how it is defined, distributed, and absorbed.31

  • Who defines uncertainty: institutions, funders, and experts decide which uncertainties count, which methods are legitimate, and which knowledge systems qualify as evidence. This framing can exclude Indigenous, local, and nonhuman ways of knowing.
  • Who benefits from simplification: reduction to a single number or model serves those who want fast decisions, clear liability limits, or defensible procedures. Simplification often hides disagreement and protects existing interests.
  • Who absorbs the downside: marginalised human communities and nonhuman beings often bear the residual harm when confident predictions fail. They rarely set the thresholds, yet they carry the consequences.38

A just approach records who defines, who benefits, and who bears uncertainty, then redistributes that burden through contestable and revisable decisions. Cf. Justice, Oppression, Knowing, and Deliberation.

Implications for More-than-Human Design

For the background on inclusion in design, see Roudavski 39. For the implications of intelligence used as a strategy to manage uncertainty, see Intelligence and Roudavski et al. 40

  • Diagnose and separate the kinds of uncertainty before choosing a method. Each type needs a different response: some problems need better measurement, others need scenarios, precaution, negotiation across frames, or acceptance of irreducible indeterminacy.1, 14, 12
  • Treat uncertainty as a standing design condition. When actors know too differently, more information can deepen conflict.14, 3
  • Use plural evidence protocols, and refuse to extract knowledge from its world. Record confidence, disagreement, and translation limits. Treat values, ethics, identity, and cosmology as design-relevant alongside observations.16, 17, 18
  • Treat some nonhumans as knowers in their own right. Animal know-how, plant attentiveness, river warnings, and microbial shifts count as evidence that carries uncertainty tags.31, 32, 33, 22, 23
  • Use being-specific evidence where it matters. Eel passage shows that design often depends on fine-grained behavioural and hydraulic knowledge that generic care language cannot supply.36, 37
  • Prefer robust, adaptive, and reversible strategies. Precaution does most work when causal models are weak, outcome spaces open, or ignorance likely.41, 12, 2, 20
  • Build diplomatic processes across partially connected worlds. In pluriversal settings the aim is working relations among worlds that share only some assumptions.4, 5, 20
  • Track who bears uncertainty burdens, risk exposure, and decision costs.38
  • Let nonhuman agencies redirect the process. More-than-human design builds procedures that register and respond when lifeforms and ecological processes push back.6, 7

Further Reading

Kochenderfer, Mykel J. Decision Making under Uncertainty: Theory and Application. Cambridge, MA: MIT press, 2015.

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Notes

Footnotes

  1. Walker, Warren E., Poul Harremoës, Jan Rotmans, Jeroen P. van der Sluijs, Marjolein B. A. van Asselt, Peter P. M. Janssen, and Martin P. Krayer von Krauss. “Defining Uncertainty: A Conceptual Basis for Uncertainty Management in Model-Based Decision Support.” Integrated Assessment 4, no. 1 (2003): 5–17. https://doi.org/10.1076/iaij.4.1.5.16466.˄

  2. Wynne, Brian. “Uncertainty and Environmental Learning: Reconceiving Science and Policy in the Preventive Paradigm.” Global Environmental Change 2, no. 2 (1992): 111–27. https://doi.org/10.1016/0959-3780(92)90017-2.˄

  3. Brugnach, Marcela, Art Dewulf, Claudia Pahl-Wostl, and Tharsi Taillieu. “Toward a Relational Concept of Uncertainty: About Knowing Too Little, Knowing Too Differently, and Accepting Not to Know.” Ecology and Society 13, no. 2 (2008): art30. https://doi.org/10.5751/ES-02616-130230.˄

  4. Cadena, Marisol de la. Earth Beings: Ecologies of Practice across Andean Worlds. Durham: Duke University Press, 2015.˄

  5. Yates, Julian S., Leila M. Harris, and Nicole J. Wilson. “Multiple Ontologies of Water: Politics, Conflict and Implications for Governance.” Environment and Planning D: Society and Space 35, no. 5 (2017): 797–815. https://doi.org/10.1177/0263775817700395.˄

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