Abstract
This paper proposes a new way of handling the uncertainties present in transport decision making based on infrastructure appraisals. The paper suggests to combine the principle of Optimism Bias, which depicts the historical tendency of overestimating transport related benefits and underestimating investment costs, with a quantitative risk analysis based on Monte Carlo simulation and to make use of a set of exploratory scenarios. The analysis is carried out by using the CBA-DK model representing the Danish standard approach to socio-economic cost-benefit analysis. Specifically, the paper proposes to supplement Optimism Bias and the associated Reference Class Forecasting (RCF) technique with a new technique that makes use of a scenario-grid. We tentatively introduce and refer to this as Reference Scenario Forecasting (RSF). The final RSF output from the CBA-DK model consists of a set of scenario-based graphs which function as risk-related decision support for the appraised transport infrastructure project.
Original language | English |
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Title of host publication | The 6th International Mediterranean Modelling Multiconference : The International Workshop on Modelling & Applied Simulation |
Place of Publication | Universidad de La Laguna |
Publisher | Universidad de La Laguna |
Publication date | 2009 |
Pages | 85-91 |
ISBN (Print) | 978-84-692-5417-2 |
Publication status | Published - 2009 |
Event | The 6th International Mediterranean Modelling Multiconference - Tenerife - Canary Islands, Spain Duration: 23 Sept 2009 → 25 Sept 2009 |
Conference
Conference | The 6th International Mediterranean Modelling Multiconference |
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Country/Territory | Spain |
City | Tenerife - Canary Islands |
Period | 23/09/2009 → 25/09/2009 |
Other | Features:<br/>The 21st European Modelling & Simulation Symposium - EMSS<br/>The International Workshop on Modeling & Applied Simulation- MAS<br/>The International Workshop on Harbour, Maritime & Multimodal Logistics M&S - HMS |
Keywords
- reference class forecasting
- decision support
- reference scenario forecasting
- risk analysis