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+<WRAP catbadge>Governance Innovation & Change</WRAP>
+====== Uncertainty ======
+<WRAP meta>
+lead-authors: [Name]
+contributors: [Names]
+reviewers: [Names]
+version: 0.2
+updated: 19 March 2026
+sensitivity: low
+ai-disclosure: Claude Sonnet 4.6 (Anthropic) assisted with research synthesis and section drafting; all sources independently verified.
+status: draft
+short-desc: The distinction between measurable risk and irreducible uncertainty, and how both shape investment, governance, and institutional design in energy systems.
+</WRAP>
+<WRAP intro>
+Risk and uncertainty are not the same thing. The difference matters for how energy systems are governed, how investments are made, and what kinds of institutions can actually reduce either.
+</WRAP>
+<WRAP callout>
+When decision-makers treat genuine uncertainty as if it were calculable risk, they tend to underinvest in resilience and overestimate the reliability of their forecasts.
+</WRAP>
+Energy transitions involve long planning horizons, capital-intensive infrastructure, new actors, and shifting regulatory frameworks. All of this generates both risk and uncertainty in ways that interact and compound. Understanding the difference between the two, and where each comes from in energy systems specifically, is a precondition for designing effective governance responses.
+===== A shared definition =====
+The canonical distinction comes from Frank Knight's 1921 work //Risk, Uncertainty and Profit//.((Knight, F. H. (1921). //Risk, uncertainty and profit//. Houghton Mifflin. [Public domain; available via https://oll.libertyfund.org/titles/knight-risk-uncertainty-and-profit])) Knight argued that risk applies to situations where the outcome is unknown but the odds are measurable, where probabilities can be estimated from prior data or general principles. Uncertainty, by contrast, applies to situations where the odds themselves cannot be known, where no reliable probability distribution can be assigned to future outcomes.
+The distinction is not merely academic. In conditions of risk, standard tools of insurance, hedging, diversification, and statistical forecasting can function. In conditions of genuine uncertainty, those tools give false assurance. Institutional economists and governance scholars draw on Knight's distinction to explain why energy system transitions are so difficult to manage: many of the most consequential variables, technology trajectories, political shifts, regulatory change, consumer behaviour at scale, are genuinely uncertain rather than risky in Knight's sense.
+===== Sources of risk and uncertainty in smart grid transitions =====
+Drawing on expert stakeholder research in the UK electricity sector, Connor et al. (2018) group the sources of risk and uncertainty in smart grid deployment into seven categories:((Connor, P. M., Axon, C. J., Xenias, D., & Balta-Ozkan, N. (2018). Sources of risk and uncertainty in UK smart grid deployment: An expert stakeholder analysis. //Energy//, 161, 1–9. https://doi.org/10.1016/j.energy.2018.07.115))
+^ Category ^ What it covers ^
+| **Markets** | Uncertainty about how electricity markets will develop, including the emergence of new market structures, price signals, and business models for distributed resources. |
+| **Users** | Uncertainty about consumer behaviour, adoption rates, and engagement with new services and tariff structures. |
+| **Data and information** | Risks around data access, ownership, privacy, and the governance of information flows that smart grid systems depend on. |
+| **Supply mix** | Uncertainty about the pace and pattern of renewable deployment, storage, and the changing generation portfolio. |
+| **Policy** | Uncertainty about regulatory change, policy continuity, and the investment signals that government frameworks send to network operators and developers. |
+| **Investment conditions** | Risks related to the terms under which regulators allow capital expenditure, and whether network operators will invest ahead of demonstrated need. |
+| **Networks** | Technical and operational risks arising from the increasing complexity of systems integrating distributed energy resources at scale. |
+These categories interact. Policy uncertainty raises investment risk. Data governance gaps create market uncertainty. Regulatory frameworks that do not allow investment ahead of need suppress network innovation. Risk and uncertainty in smart grid transitions are therefore systemic rather than sector-specific.
+===== Perspectives =====
+<WRAP perspectives>
+==== Actors and stakeholders ====
+Different actors face structurally different risk and uncertainty exposures. Network operators face regulatory risk about allowable returns and investment timing. Developers of new energy services face market uncertainty about whether viable business models will emerge. Consumers face uncertainty about tariffs, technology commitments, and data use. Aggregators and flexibility providers face compound uncertainty across multiple regulatory and market dimensions at once.
+The distribution of risk also raises equity questions. Where risk is borne by consumers through tariffs, or by communities through infrastructure siting decisions, the governance of that distribution matters as much as its aggregate level. See [[topics:stakeholders|Stakeholders]].
+==== Technologies and infrastructure ====
+At the technical level, uncertainty is embedded in the variability of renewable generation, the unpredictability of demand at high granularity, and the behaviour of large numbers of distributed devices coordinating through automated systems. Investment decisions about long-lived infrastructure must be made under uncertainty about what technology costs, capabilities, and market conditions will look like over decades.
+Planning electricity systems under uncertainty has become a recognised field of research, with stochastic optimisation methods developed specifically to improve investment decisions when future scenarios cannot be reduced to a single expected value.((Lara, C. L., Mallapragada, D. S., Papageorgiou, D. J., Venkatesh, A., & Grossmann, I. E. (2018). Deterministic electric power infrastructure planning: Mixed-integer programming model and nested decomposition algorithm. //European Journal of Operational Research//, 271(3), 1037–1054. [Representative of the planning-under-uncertainty literature; see also Royal Society special issue on energy management, flexibility, risk and optimisation, 2017.])) See [[topics:network_-_grid|Networks & Grids]].
+==== Institutional structures ====
+Institutions reduce uncertainty by creating stable rules, expectations, and coordination mechanisms. Property rights, contracts, regulatory frameworks, and standards all substitute shared expectations for the need to forecast each individual actor's behaviour. This is the institutional economics argument for why institutional quality matters for infrastructure investment: predictable rules lower the uncertainty premium that investors must price in.
+Regulatory uncertainty is particularly significant for long-lived capital investments. When the rules governing energy systems shift with political cycles or change unexpectedly, the investment case for smart grid infrastructure becomes harder to make. Mandate clarity, incentive structures, and the legal durability of regulatory commitments are therefore not merely administrative concerns; they shape what transitions are financially viable. See [[topics:institutions|Institutions]] and [[topics:regulation|Regulation]].
+</WRAP>
+===== Key terms =====
+^ Term ^ Definition ^
+| **Risk (Knightian)** | A situation where the outcome is uncertain but the probabilities can be measured or estimated from available data. Standard insurance, hedging, and statistical forecasting tools apply. |
+| **Uncertainty (Knightian)** | A situation where no reliable probability distribution can be assigned to future outcomes. The odds themselves are not knowable. Often called "true uncertainty" or "deep uncertainty." |
+| **Regulatory uncertainty** | Uncertainty arising from the possibility that rules, policies, or regulatory frameworks will change in ways that cannot be anticipated, affecting the investment case for infrastructure and services. |
+| **Risk distribution** | The allocation of risk exposure across actors in a system, including who bears the costs when adverse outcomes occur. Governance arrangements often determine this allocation as much as the underlying probabilities. |
+| **Stochastic optimisation** | A class of mathematical techniques for making investment or operational decisions that explicitly model uncertainty about future states, rather than assuming a single expected outcome. |
+===== Distinctions and overlaps =====
+**Risk and uncertainty are not on a continuum.** Knight's distinction is categorical, not scalar. Treating deep uncertainty as high risk may produce a false sense of quantitative rigour. Models that assign precise probabilities to genuinely uncertain outcomes can be more misleading than approaches that acknowledge the uncertainty directly.
+**Uncertainty reduction is not the same as risk management.** Institutional arrangements, regulatory frameworks, and governance structures reduce uncertainty by creating stable expectations. Risk management tools such as hedging and insurance address situations where probabilities can be estimated. The two require different instruments and different policy designs. This is why the institutional environment matters for investment in infrastructure: it performs uncertainty reduction rather than risk transfer.
+**Uncertainty and resilience.** A system designed for a known risk can be optimised around that risk's probability distribution. A system designed for genuine uncertainty needs different properties: flexibility, redundancy, and the ability to adapt to outcomes that were not anticipated. See [[topics:resilience|Resilience]].
+===== Related topics =====
+[[topics:institutions|Institutions]], [[topics:regulation|Regulation]], [[topics:governance|Governance]], [[topics:resilience|Resilience]], [[topics:scenarios|Scenarios]], [[topics:transitions|Transitions]]
+<WRAP callout>
+This topic is part of the ISGAN Wiki and is currently being developed. If you have relevant expertise, you can write the topic in one of two ways. You can directly edit this page by clicking the edit button at the top right corner of this page. You can also use the [[about:newtopic|Topic Builder]] if you prefer minimum syntax. Working on a topic requires a confirmed wiki account. To contribute, please register and allow up to three days for admin confirmation. Before contributing, please read the [[about:guidelines|ISGAN Wiki Editorial Guidelines]].
+</WRAP>