Abstract The disciplines of landscape ecology and quantitative dynamic stratigraphy have developed independently, but in parallel directions. Both disciplines are concerned with the description, origins, evolution, and consequences of spatial heterogeneity. Both have benefited from rapid developments in remote sensing, geographic information systems, and high-speed computer modeling. Researchers in both fields have recognized the significance of scale-dependent and scale-independent processes and thus the uses of fractal geometry; consequently, numerical methods used for describing spatial heterogeneity in ecological landscapes should be readily applied to the analysis of both model and real-world stratigraphic systems. This paper discusses how landscape statistics, such as diversity, contagion, and lacunarity, can be fruitfully applied to the description of stratigraphic sequences and have the potential for improving model data comparisons.

Abstract Opinion of geological experts is often formed despite a paucity of data and is usually based on prior experience. In such situations humans employ heuristics (rules of thumb) to aid analysis and interpretation of data. As a result, future judgements are bootstrapped from, and hence biased by, both the heuristics employed and prior opinion. This paper reviews the causes of bias and error inherent in prior information derived from the probabilistic judgements of people. Parallels are developed between the evolution of scientific opinion on one hand and the limits on rational behaviour on the other. We show that the combination of data paucity and commonly employed heuristics can lead to herding behaviour within groups of experts. Elicitation theory mitigates the effects of such behaviour, but a method to estimate reliable uncertainties on expert judgements remains elusive. We have also identified several key directions in which future research is likely to lead to methods that reduce such emergent group behaviour, thereby increasing the probability that the stock of common knowledge will converge in a stable manner towards facts about the Earth as it really is. These include: (1) measuring the frequency with which different heuristics tend to be employed by experts within the geosciences; (2) developing geoscience-specific methods to reduce biases originating from the use of such heuristics; (3) creating methods to detect scientific herding behaviour; and (4) researching how best to reconcile opinions from multiple experts in order to obtain the best probabilistic description of an unknown, objective reality (in cases where one exists).

Abstract Engineering is currently expanding its conceptual boundaries by accepting the challenge of interdisciplinarity, while often adopting social and biological concepts in developing tools (e.g. evolutionary optimization or interactive autonomous agents) or even world views (e.g. co-evolution, resilience, adaptation). The emerging socio-technical knowledge domain is still very much restricted by partial knowledge associated with the lack of long-term transdisciplinary research effort and the unavailability of robust, integrated tools able to cover both the technical and the socio-economic domains and to act as ‘thinking platforms’ for long-term scenario planning and strategic decision making under (high-order) uncertainties. Here we present an example of a toolkit that attempts to bridge this gap focusing on urban water (UW) systems and their management. The toolkit consists of three tools: the UW Optioneering Tool (UWOT); the UW Agent Based Modelling Platform (UWABM); and the UW System Dynamic Environment (UWSDE). The tools are briefly presented and discussed, focusing on interactions and data flows between them and their typical results are illustrated through a case study example. A further tool (a Cellular Automata Based Urban Growth Model) is currently under development and an early coupling with the other tools is also discussed. It is argued that this type of extended model fusion, beyond what has traditionally been thought of as ‘integrated modelling’ in the engineering domain is a new frontier in the understanding of environmental systems and presents a promising, emerging field in modelling interactions between our societies and cities, and our environment.