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From discrete scenarios to continuous scenarios

Conventionally, scenarios attempt to portray future possibilities using the concept of ‘representative country’ and conceptualise the energy systems as consisting of several identical and isolated components. This means that based on, for example, historical oil production of key producers (e.g., the US), world oil production is arbitrarily assumed to have (more or less) the same production characteristics. These conventional scenarios attempt to assess the pathways and impact of driving forces using the concept of ‘multi pathways’ (see figure 2). Selecting specific values for the most uncertain and important driving forces does this. Although the move to ‘multi pathways’ or ‘discrete scenarios’ was a key innovation in the 1967 when Shell’s Group Planning shifted away from single-line forecasting (called a Unified Planning Machinery), a key problem (for policymaking and long-term corporate strategy) of the discrete scenarios is that it fails to emphasise the inevitable uncertainty around the outlooks, say, of oil supply. It also fails to provide an assessment of the balance of risk between the different pathways. At a more technical level, the uncertainty and risk are reduced to finite sets of certain values. To emphasise the inherent uncertainty of the future outlook of energy supply and to avoid oversimplifying the heterogeneity of the energy system, the ACEGES suggest the use of continuous scenarios (see figure 3) that emphasise the uncertainty and give an assessment of the balance of risk. The use of continuous scenarios avoid suggesting a degree of precision that would be spurious and are appropriate when exactitude is elusive while being approximately right is still helpful for policy making and long-term corporate strategy.

Figure 2 A discrete scenario of plausible futuresii

Figure 3 A continuous scenario of plausible futuresiii

* Vlasios Voudouris is the Deputy Director of the Centre for International Business and Sustainability at London Metropolitan Business School. Dr. Voudouris provides evidence-based services to corporations, non-profit organisations and central governments by consulting regularly with executives. For example, he contributed to the House of Commons Energy and Climate Change Committee (inquiry into The UK’s Energy Supply: Security or Independence?) based upon the insights from the ACEGES decision-support tool, which he authors.

Beating Uncertainty

Because those involved in scenario development have knowledge of, say, the global energy scene, it is important to incorporate tacit knowledge or uncertainties that is not captured in databases. For example, personal interpretations of ‘forces in the pipeline’ might suggest major discontinuities as experienced by Shell in common with the other “Seven Sisters” when facing the Tehran Agreement or a forthcoming physical security of oil flows because of above-ground events such as activities by the movement for the emancipation of the Niger Delta (MEND), hurricanes in the Gulf of Mexico and political unrest in MENA (Middle East and North Africa). The ACEGES tool is designed to enable senior decision-makers to rehearse the future by interactively exploring the uncertain space using an ‘Exploratory Console”. This human-centred exploration of the uncertain space enables the incorporation of subject-specific expertise that cannot be easily captured by a system of equations or by compressed quantitative indicators. This emphasizes that developing energy scenarios and assessing business strategies is primarily a non-mechanistic mental process facilitated by computers by means of controlled experiments. The ACEGES-based scenarios are ‘early warning systems’ by focusing on the driving forces that makes a difference to senior energy decision-makers. To paraphrase Mark Buchanan , we can develop computational ‘wind tunnels’ that would allow decision makers to test the resilience of their strategies against plausible futures. Therefore, if ‘wind tunnels’ and related simulation methods work in the physical world (e.g., testing the essential aerodynamic features of scale-model bridges), then controlled computational experiments can also work to help decision makers prevent, for example, another oil crunch or anticipate local patterns of supply and demand that can generate price spikes within price spikes as exemplified by the price differential between Brent crude and West Texas Intermediate (WTI) in 2011. We’re at the early dawn of a fundamental shift in our energy system, one that also requires a fundamental shift in our personal microcosms (particularly in relation to physical and price of oil security). To close with Charles Darwin “It is not the strongest of the species [e.g., companies] that survives, nor the most intelligent that survives. It is the one that is the most adaptable to change”. For information please contact

Notes: i) Wack, P., (1985), “Scenarios: Uncharted Waters Ahead”, Harvard Business Review. September-October, 1985. ii) Wood, J., Long, G., Morehouse, D., (2004). “Long-Term World Oil Supply Scenarios: The Future Is Neither As Bleak Or Rosy As Some Assert”, U.S. Energy Information Administration - EIA. iii) Voudouris, V., Stasinopoulos, D., Rigby, R. and Di Mai, C., (2011), “The ACEGES laboratory for Energy Policy: Exploring the production of crude oil”, Energy Policy. Available from: science/article/pii/S0301421511003867 iv) Buchanan, M., (2009), “Meltdown modelling: could agent-based computer models prevent another financial crisis?”, Nature 460 (August), 680-682. EUROPEANBUSINESSREVIEW


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