Seminars (EBD)

The EU Smart Cities programme and sub programmes such as those on district heating and cooling (DHC), together with technological innovations such as electric cars and pollution free zones present a  vision of the modern city which may or may not be realised. But there are many lessons for similar visions of landscapes. In some sense the technology is the easy part. The real challenge, for modelling, is to integrate the technological, economic, social and environmental. There are matters of ownership, the development of new business models, public-private funding and  scale (national/regional/local). Contracts play a key part and we will advocate, as with cities, model-based contract design, to cope with the tensions between long-term and short-term investment and between CAPEX and OPEX. Some exemplar issues will be mentioned: the economic and social aspects of flooding, the use of wetlands for carbon capture and the citizen as energy prosumer.

Hunger and food poverty is on the increase even in developed nations like the UK, USA, Canada & Australia. With a growing population and a finite planet, there is urgent need for action, but in such a large, complex system identifying the most effective action requires decision support.
Food security exists when all people, at all times, have physical and economic access to sufficient, safe and nutritious food to meet their dietary needs and food preferences for an active and healthy life.
In order to provide decision support, it is necessary to elicit probability distributions to evaluate subjective expected utility scores associated with ameliorating policies that might be enacted. When the underlying process model is extremely large and complex, this brings its own peculiar challenges. It is first necessary to elicit the overall, agreed structure describing in broad terms the underlying nature of the system from representatives of all domain experts across the system as a whole. We have now shown that this can be done formally and consistently with probability models if the elicitations concern the elicitation of dependences – formally termed irrelevances (Smith, Barons and Leonelli (2016)). Within a probability model, these irrelevance statements then transform into assertions about various conditional independence statements. These, in turn, can be used to determine how the system can be divided up into (conditionally) independent segments. The quantitative expert judgements associated with each segment of the process can then be delegated to a relevant panel of experts. The implicit (albeit virtual) owner of beliefs expressed in the system will be referred to as the supraBayesian , meaning that the decision-making group acts as a single person would and it is her coherence that we are concerned with. Under suitable conditions it can then be shown that the elicited overarching structure can compose these judgments together to form a coherent probabilistic model to score different options available to the user, termed an integrating decisions support system (IDSS).

One element of the overarching food poverty models is food supply, and key to parts of this is an abundant and healthy population of pollinating insects to pollination services for food. In 2014 the UK government undertook a consultation and produced their pollinator strategy for the next 10 years “to see pollinators thrive, providing essential pollination services and benefits for food production, the wider environment and everyone.” However, the evidence base on the complex system driving pollinator vigour and numbers is patchy and held in disparate domains of expertise, making the evaluation of policy options problematic. In this talk I will describe how we are in the process of developing an IDSS based on these theoretical developments, and how a probabilistic model for pollinator abundance incorporating structured expert elicitation will then form a sub-module of this IDSS for policies relating to household food insecurity.

J. Q. Smith, M.J. Barons, and M. Leonelli. Coherent inference for integrating decision support systems. arXiv, 2016. http://arxiv.org/abs/1507.07394.

Co-authors: Jim Q. Smith, Manuele Leonelli

Computational modelling is a key tool in efforts to understand the dynamics of socio-ecological systems. Many models, however, tightly constrain those dynamics by making assumptions about economic equilibrium and optimisation in social systems, and mean-field or trend-based behaviour in ecological systems. Recent research has revealed that, contrary to these assumptions, small-scale behavioural processes shape the dynamics and interactions in socio-ecological systems across scales. Concurrently, data resources and computational tools have advanced to the point that simulation of these processes is increasingly feasible. I will present some recent advances, opportunities and challenges for simulating the role of human behaviour in land use change, building on conceptual and computational examples from both ecology and social science.

The land system is a complex system. It depends on a miriad of interactions between individual, heterogenous land users, on the variability of the physical environment (soils, climate, …) and on the diversity of societal structures including communities, institutions and public policy organisations. Many modelling approaches have been proposed to represent land systems, with the dominant paradigm based on economic optimisation. However, assuming that individual land managers make decisions about land use based on economic rational alone is a serious simplication of the complex land system. In practice, we know that individuals make land use decisions based on many, often conflicting factors such as risk aversion, social standing, tradition and environmental impact within the context of having imperfect access to knowledge. And that many of these processes play out at different spatial scale levels. In this presentation I will explore how different approaches to modelling land systems can be coupled across scales in order to capture the salient processes at each scale level. This includes modelling of land-based commodity trade-flows at the global scale, sub-national agent-based modelling of land use decision making, and the representation of public policy organisations that influence land users locally.

This talk examines landscape change from the perspective of complex social-ecological systems evolving through time. Such a perspective can reveal ‘hidden’ complex behaviour that may interact unpredictably with decision-making. These include feedback loops, system instabilities, critical transitions and trade-offs. Knowing the complex behaviours, such as feedback loops, within a system can help decision-makers avoid tipping points and traps in order to keep within ecologically sustainable and socially acceptable limits. There are two parts to the talk. a) Multiple time-series. Here, research analyses multiple time-series of social, economic, ecological, and climate variables covering the past few decades to help elucidate important changes in system interactions. In eastern China, studies at several sites show long term economic growth since 1950 as a trade-off with environmental deterioration, especially water quality. In western China, detailed studies of the lake Erhai lake-catchment system reveal the interactions between agriculture, climate and water management that led to a critical transition in the aquatic ecosystem in 2001. In the UK, a similar approach shows rapid agricultural intensification driving significant environmental degradation in England in the early 1980s, but with a recovery in most ecosystem services after 2000. However, the lack of recovery in farmland biodiversity and the ‘offshoring’ of some impacts represent major negative trade-offs. b) Systems modelling. Here, a case-study describes a systems model designed to guide decision-makers in the setting of ‘safe and just operating spaces’ for sustainable management. Monte Carlo simulations of fish catch from India's Chilika lagoon over the next 40 years are compared to conditions that are ecologically and socio-economically desirable. Akin to a satellite-navigation system, the model identifies multidimensional pathways giving at least a 75% chance of achieving the desirable future.

This talk will discuss research needs in evidence based decision making, based on scoping studies for the Centre for Digital Built Britain and the Supergen Hubnet project, the current Alan Turing Institute "Managing Uncertainty in Government Modelling" and "Use of Multiple Models Within and Organisation" project, and the presenter's own experience. This will be synthesised into suggestions of key topics for discussion during the subsequent four weeks of the Programme.