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.

The next few years will be crucial for the future of the UK landscape. There are important decisions that need to be made about agricultural policy, nature conservation and how we respond to a changing climate. All these decisions will involve large amounts of uncertainty. How can we produce decision support tools that will help in the process? In this talk I will investigate some fo the methods currently used to aid decision makers by combining data and models and point out their advantages and disadvantages. I will also look at some other new directions that could solve some of these problems.  

This talk will summarise discussions from the Work
Programme on ‘Mathematical and Statistical Challenges in Landscape Decision
Making’, which took place between 3 July to 2 August 2019, focusing on coupling
models to represent interactions within landscape systems. Many studies of
landscape decisions are based on models of individual sectors, such as
agriculture, forestry and water use, without considering interactions between
these sectors. Yet, many drivers (be they climate change, policies or

other) may lead to altered interactions between sectors
and scales. Coupling models across sectors and scales enables interactions,
trade-offs and synergies between different components of landscape systems to
be captured in a systemic manner. This is important because modelling
assessments that do not account for cross-sectoral or cross-scale interactions
have the potential to misrepresent impacts and thus, the need or otherwise for
adaptive action through landscape decision-making. Hence, this topic was
discussed in detail during the 2019 INI Programme. Research priorities were
divided into four

themes: (i) transparency, reproducibility and
communication in coupled models; (ii) model coupling toolbox; (iii) model
coupling technicalities; and

(iv) taking advantage of the benefits of model coupling.
The key insights that emerged in these four themes were captured within short,
medium and longer term research roadmaps.

The Food, Farming and Countryside Commission (FFCC) has already engaged in innovative new approaches to engage citizens around the UK in the big questions about countryside, environment, climate, nature and land use. Yet it remains difficult to get diverse perspectives into research and debate. Learning from FFCC’s experiences, Sue will use her talk to explore different strategies for democratic decision making around land use in the UK.  

This project aims to extend theory for probabilistic risk
analysis of continuous systems, test its use against forest data, use process
models to predict future risks, and develop decision-support tools.



Risk is commonly defined as the expectation value for
loss. Most risk theory is developed for discrete hazards such as accidents,
disasters and other forms of sudden system failure and not for systems where
the hazard variable is always present and continuously varying, with matching
continuous system response.



Risks from such continuous hazards (levels of water,
pollutants) are not associated with sudden discrete events, but with extended
periods of time during which the hazard variable exceeds a threshold. To manage
such risks, we need to know whether we should aim to reduce the probability of
hazard threshold exceedance or the vulnerability of the system.



In earlier work, we showed that there is only one
possible definition of vulnerability that allows formal decomposition of risk
as the product of hazard probability and system vulnerability. We have used
this approach to analyse risks from summer droughts to the productivity of
vegetation across Europe under current and future climatic conditions; this
showed that climate change will likely lead to greatest drought risks in
southern Europe, primarily because of increased hazard probability rather than
significant changes in vulnerability.



We plan to improve on this earlier work by: adding
exposure to hazard; quantifying uncertainties in our risk estimates for risk;
relaxing assumptions via Bayesian hierarchical modelling; testing our approach
on both observational data from forests in the U.K., Spain and Finland and on
simulated data from process-based modelling of forest response to climate
change; embedding the approach in Bayesian decision theory; and developing an
interactive web application as a tool for preliminary exploration of risk and
its components to support decision-making.

In good condition, peatlands are the most efficient carbon store of all soils.  The UK has 2 Mha of peatlands (10% land area). 80% of these peatlands are damaged to some degree and estimated to emit 10 Mt C a-1, a similar magnitude to oil refineries or landfill sites.  Restoring degraded peatlands to halt carbon losses is an essential part of a global strategy to fight climate change. In the UK, £100s millions of public money have been pledged to restore peatland, yet we do not have a reliable and cost-effective way to direct and evaluate investment in restoration over large and often remote areas.   In a previous research project, we showed that peatland condition can be found from satellite data that measures surface motion of the peat. However, our satellite-based approach produces too much complex data that cannot be reliably and consistently analysed by eye.   To address this, we will develop a new statistical method that can robustly and consistently quantify the changes in the peatland landscape from the satellite data. This requires methods capable of handling extremely large and complex structured datasets. In statistics, a new framework, known as Object-Oriented Data Analysis (OODA), is ideally suited to achieve this purpose by building models based on suitable choices of data objects. OODA can be used for developing parsimonious models for detecting change, and for quantifying uncertainty in predictions. OODA of the satellite data as functions of space and time will enable the modelling of trends and variability in the different regions, and the detection of change in the peatland.   The result will be a series of maps that illustrate the change in peatland landscape over time that are designed to be used by land managers and policy makers to guide decision making, help reduce unnecessary spending and evaluate investment.  

Land system modelling is primarily focused on the question: how do land managers make decisions about the use of land resources. There is a long history of land system modelling stretching back to classical economists in the early 19th Century. Early concepts focused strongly on economic (land rent) models to explain land use distributions, but these began to also include the notion of relative location (e.g. distance to markets) in determining land use patterns. Much of the initial thinking from this time is still relevant today and shapes, to some extent, current thinking about how to model the decision-making processes of land users. However, as land use models evolved through time, non-economic aspects began to take on increasing importance. Processes such as access to information and the spatial diffusion of knowledge through space and time were shown to be critical in understanding landscape decisions. This has led to an evolution in land system modelling towards a focus on agency and social interaction in addition to economic aspects. Methods such as Agent-Based Modelling (ABM) are now able to accommodate a range of human behaviours that underpin decision making and the social interaction processes that foster knowledge exchange through cooperation or competition. In this talk I provide an overview of the evolution of land system modelling exploring the advantages and disadvantages of the many extant approaches. I also explore the considerable progress that is still needed to develop land system models further, e.g. better representing human decision-making processes, better testing against data, coupling land system models with other components of the broader environment, and endogenizing the policy making process within models.

Analytical sociology focuses on social interactions among individuals and the hard-to-predict aggregate outcomes they bring about. It seeks to identify generalizable mechanisms giving rise to emergent properties of social systems which, in turn, feed back on individual decision-making. This research program benefits from computational tools such as agent-based simulations, natural language processing, and large-scale web experiments, and has considerable overlap with the nascent field of computational social science. By providing relevant analytical tools to rigorously address sociology’s core questions, computational social science has the potential to considerably advance sociology. The disciplinary relationship, however, is not a one-way street, and this talk outlines how analytical sociology, with its theory-grounded approach to computational social science, can help to move the field forward from mere descriptions and predictions to the explanation of social phenomena.

The land resource is used to satisfy many different land
-related objectives:

food production and security, biodiversity, housing and
other developments, leisure and recreation, as well as flood protection,
biomass, energy production and waste. Landscape Decisions are fundamentally
concerned determining what to put where and have to balance competing demands
for these different Ecosystem Services. This allocation problem is further
complicated by a number of specifically social factors:

- Different actors in landscape decision making (from
policy to landowners to citizen ‘consumers’) have different objectives and
priorities and value landscape elements in different ways

- These values vary between and within groups, as well as
with socio-economic context

- Individual and institutional objectives also operate
over different time frames and spatial scales Thus some form of socio-economic
analysis or social modelling is integral to landscape decisions to

- incorporate stakeholder preferences (e.g. the relative
value of any given

ESs)

- model land management behaviours (e.g. risk seekers,
consolidators, market reactors, etc)

- evaluate the socio-economic impacts of landscape
decisions (e.g. to quantify the trade-offs between food production and flood
risk mitigation) This talk will outline and illustrate the impacts of a number
of key but frequently overlooked issues associated with incorporating *any spatial
data* (including data describing social processes) into landscape decision
models, related to scale, scales of decision making and model evaluation.

Controlled experiments with human subjects provide causal answers to questions that are otherwise difficult to address with observational methods. Specifically, laboratory experiments have been crucial for improving our understanding of individual behavior. Nowadays, online experiments allow us to scale up and also study collective behavior, group-level phenomena, and complex-system dynamics such as positive feedback loops, tipping points, path dependency, and self-organization. To demonstrate the potential of this method, I will present a project that uses an online game to study how differently endowed individuals who interact with each other can produce fair outcomes. We juxtapose fairness mechanisms that individuals employ – generosity, reciprocity, and inequity aversion – with competing concepts of societal fairness – meritocracy, equality of opportunity, equality of outcomes, and Rawls’ theory of justice. The work illuminates which interventions will work better for a specific desired outcome in a company, organization, school, or community. The game and method can be adapted to study topics as diverse as urban segregation, rural development, and immigrant integration.

Breakout Room Topics:
-The role of uncertainty in decision-making
-The role of spatial-temporal dynamics in landscape decision-making   
-The role of complexities and non-linearities in landscape decision-making-The role of human processes in landscape decision-making   
-The role of social influence in landscape decision-making processes   
- Interdisciplinary integration across the social, mathematical and environmental sciences to improve the scientific evidence base supporting landscape decision-making.