9,815 research outputs found
How are emergent constraints quantifying uncertainty and what do they leave behind?
The use of emergent constraints to quantify uncertainty for key policy
relevant quantities such as Equilibrium Climate Sensitivity (ECS) has become
increasingly widespread in recent years. Many researchers, however, claim that
emergent constraints are inappropriate or even under-report uncertainty. In
this paper we contribute to this discussion by examining the emergent
constraints methodology in terms of its underpinning statistical assumptions.
We argue that the existing frameworks are based on indefensible assumptions,
then show how weakening them leads to a more transparent Bayesian framework
wherein hitherto ignored sources of uncertainty, such as how reality might
differ from models, can be quantified. We present a guided framework for the
quantification of additional uncertainties that is linked to the confidence we
can have in the underpinning physical arguments for using linear constraints.
We provide a software tool for implementing our general framework for emergent
constraints and use it to illustrate the framework on a number of recent
emergent constraints for ECS. We find that the robustness of any constraint to
additional uncertainties depends strongly on the confidence we can have in the
underpinning physics, allowing a future framing of the debate over the validity
of a particular constraint around the underlying physical arguments, rather
than statistical assumptions
Subjectively Interesting Subgroup Discovery on Real-valued Targets
Deriving insights from high-dimensional data is one of the core problems in
data mining. The difficulty mainly stems from the fact that there are
exponentially many variable combinations to potentially consider, and there are
infinitely many if we consider weighted combinations, even for linear
combinations. Hence, an obvious question is whether we can automate the search
for interesting patterns and visualizations. In this paper, we consider the
setting where a user wants to learn as efficiently as possible about
real-valued attributes. For example, to understand the distribution of crime
rates in different geographic areas in terms of other (numerical, ordinal
and/or categorical) variables that describe the areas. We introduce a method to
find subgroups in the data that are maximally informative (in the formal
Information Theoretic sense) with respect to a single or set of real-valued
target attributes. The subgroup descriptions are in terms of a succinct set of
arbitrarily-typed other attributes. The approach is based on the Subjective
Interestingness framework FORSIED to enable the use of prior knowledge when
finding most informative non-redundant patterns, and hence the method also
supports iterative data mining.Comment: 12 pages, 10 figures, 2 tables, conference submissio
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Mundane is the New Radical: The Resurgence of Energy Megaprojects and Implications for the Global South [Opinion]
Representing archaeological uncertainty in cultural informatics
This thesis sets out to explore, describe, quantify, and visualise uncertainty in a
cultural informatics context, with a focus on archaeological reconstructions. For quite
some time, archaeologists and heritage experts have been criticising the often toorealistic
appearance of three-dimensional reconstructions. They have been highlighting
one of the unique features of archaeology: the information we have on our heritage
will always be incomplete. This incompleteness should be reflected in digitised
reconstructions of the past.
This criticism is the driving force behind this thesis. The research examines archaeological
theory and inferential process and provides insight into computer visualisation.
It describes how these two areas, of archaeology and computer graphics,
have formed a useful, but often tumultuous, relationship through the years.
By examining the uncertainty background of disciplines such as GIS, medicine,
and law, the thesis postulates that archaeological visualisation, in order to mature,
must move towards archaeological knowledge visualisation. Three sequential areas
are proposed through this thesis for the initial exploration of archaeological uncertainty:
identification, quantification and modelling. The main contributions of the
thesis lie in those three areas.
Firstly, through the innovative design, distribution, and analysis of a questionnaire,
the thesis identifies the importance of uncertainty in archaeological interpretation
and discovers potential preferences among different evidence types.
Secondly, the thesis uniquely analyses and evaluates, in relation to archaeological
uncertainty, three different belief quantification models. The varying ways that these
mathematical models work, are also evaluated through simulated experiments. Comparison
of results indicates significant convergence between the models.
Thirdly, a novel approach to archaeological uncertainty and evidence conflict visualisation
is presented, influenced by information visualisation schemes. Lastly, suggestions
for future semantic extensions to this research are presented through the
design and development of new plugins to a search engine
Living within a One Planet reality: the contribution of personal Footprint calculators
During the last 50 years, humanity's Ecological Footprint has increased by nearly 190% indicating a growing unbalance in the human-environment relationship, coupled with major environmental and social changes. Our ability to live within the planet's biological limits requires not only a major re-think in how we produce and distribute 'things', but also a shift in consumption activities. Footprint calculators can provide a framing that communicates the extent to which an individual's daily activities are compatible with our One Planet context. This paper presents the findings from the first international study to assess the value of personal Footprint calculators in guiding individuals towards sustainable consumption choices. It focuses specifically on Global Footprint Network's personal Footprint calculator, and aims to understand the profile of calculator users and assess the contribution of calculators to increasing individual awareness and encouraging sustainable choices. Our survey of 4245 respondents show that 75% of users resided in 10 countries, 54% were aged 18–34 years and had largely used the calculator within an educational context (62%). The calculator was considered a valuable tool for knowledge generation by 91% of users, and 78% found it useful to motivate action. However, only 23% indicated the calculator provided them with the necessary information to make actual changes to their life and reduce their personal Footprint. The paper discusses how and why this personal Footprint calculator has been effective in enhancing individuals' understanding of the environmental impact of their actions, framing the scale of the problem and empowering users to understand the impacts of different lifestyle choices. Those individual-level and system-level changes needed to generate global sustainability outcomes are also discussed. Similar to other calculators, a gap is also identified in terms of this calculator facilitating individuals to convert new knowledge into action
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