As part of the TyReNe meeting in September 2012 a breakout group continued with writing a collaborative multi-disciplinary paper relating to the broad topic of modelling (summarised below). The group aim to carry on working together and to have a publishable paper in the not-too-distant future.
Summary of key topics
Modelling is “the process of generating a model as a conceptual representation of some phenomenon”. The output has a purpose, although this varies between disciplines and phenomena studied. Models have limitations; are unable to fully capture reality, although transparency and accuracy help to ensure credibility.
Modelling and Scenarios
Future-oriented studies widely apply scenario analysis that “attempts to describe in some detail a hypothetical sequence of events that could lead plausibly to the situation envisaged”. But different meanings exist for modelling used alongside scenarios: ‘what-if’ picture of reality; or a technique for developing scenarios, as well as contradictions to how scenario building links to forecasting.
The only source of data for these models is the market. The data has high degrees of abstraction and simplification; although agent based and stochastic models attempt to overcome this. Integrated assessment models (IAMs) can do be both stochastic and agent-based models. One such IAM is the Dynamic Integrated model of Climate and the Economy (DICE), which (as is the case with all IAMs) combines economics and climate sciences. Basic equations facilitate analysis, although questions have been raised about the appropriateness of simplistic assumptions.
Weather and Climate Models
Throughout history people attempted weather prediction. However, it took until the mid-20th century, to attempt modelling based on physical principles, and build global climate models; such as Atmosphere-Ocean General Circulation Models (AOGCMs).
Over recent decades the demand for resources has increased to which it outstrips supply to the extent that it is now widely considered to be a limiting factor and serious threat to the functionality of economies and society. As non-renewable resources are finite, the availability and abundance drives supply and demand projections. Whereas approaches for modelling infinite renewable resources tend to differ, the focus is still on projecting supply and demand, and quantifying the extent of the resource reserve.
The lifeline infrastructure networks provide: safe drinking water, sanitary conditions, warmth and light, communication, and transportation. Models that integrate various infrastructure networks are in their infancy and, similarly to research in the field of climate change, many recent decision support models have embraced uncertainties, assessing various solutions/strategies under multiple possible futures. Such models facilitated decision-making in land use, transport and energy technology sectors against the various scenarios, testing their resilience to the potential futures.
- Range of model of different complexity are useful
- Simplistic v/s complicated
- Complexity in the modelling process v/s ease of interpreting result
- Also models should fulfill standards
- Take in to account criticisms