From climate change to pandemics: decision science can help scientists
have impact
- URL: http://arxiv.org/abs/2007.13261v2
- Date: Thu, 21 Oct 2021 23:17:44 GMT
- Title: From climate change to pandemics: decision science can help scientists
have impact
- Authors: Christopher M. Baker, Patricia T. Campbell, Iadine Chades, Angela J.
Dean, Susan M. Hester, Matthew H. Holden, James M. McCaw, Jodie McVernon,
Robert Moss, Freya M. Shearer and Hugh P. Possingham
- Abstract summary: Decision science aims to pinpoint evidence-based management strategies.
It brings together mathematical modelling, stakeholder values and logistical constraints to support decision making.
The COVID-19 pandemic has thrust mathematical models into the public spotlight, but it is one of innumerable examples in which modelling informs decision making.
- Score: 2.2768021440549164
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Scientific knowledge and advances are a cornerstone of modern society. They
improve our understanding of the world we live in and help us navigate global
challenges including emerging infectious diseases, climate change and the
biodiversity crisis. For any scientist, whether they work primarily in
fundamental knowledge generation or in the applied sciences, it is important to
understand how science fits into a decision-making framework. Decision science
is a field that aims to pinpoint evidence-based management strategies. It
provides a framework for scientists to directly impact decisions or to
understand how their work will fit into a decision process. Decision science is
more than undertaking targeted and relevant scientific research or providing
tools to assist policy makers; it is an approach to problem formulation,
bringing together mathematical modelling, stakeholder values and logistical
constraints to support decision making. In this paper we describe decision
science, its use in different contexts, and highlight current gaps in
methodology and application. The COVID-19 pandemic has thrust mathematical
models into the public spotlight, but it is one of innumerable examples in
which modelling informs decision making. Other examples include models of storm
systems (eg. cyclones, hurricanes) and climate change. Although the decision
timescale in these examples differs enormously (from hours to decades), the
underlying decision science approach is common across all problems. Bridging
communication gaps between different groups is one of the greatest challenges
for scientists. However, by better understanding and engaging with the
decision-making processes, scientists will have greater impact and make
stronger contributions to important societal problems.
Related papers
- DISCOVERYWORLD: A Virtual Environment for Developing and Evaluating Automated Scientific Discovery Agents [49.74065769505137]
We introduce DISCOVERYWORLD, the first virtual environment for developing and benchmarking an agent's ability to perform complete cycles of novel scientific discovery.
It includes 120 different challenge tasks spanning eight topics each with three levels of difficulty and several parametric variations.
We find that strong baseline agents, that perform well in prior published environments, struggle on most DISCOVERYWORLD tasks.
arXiv Detail & Related papers (2024-06-10T20:08:44Z) - How should the advent of large language models affect the practice of
science? [51.62881233954798]
How should the advent of large language models affect the practice of science?
We have invited four diverse groups of scientists to reflect on this query, sharing their perspectives and engaging in debate.
arXiv Detail & Related papers (2023-12-05T10:45:12Z) - A data science axiology: the nature, value, and risks of data science [0.0]
Data science is a research paradigm with an unfathomed scope, scale, complexity, and power for knowledge discovery.
This paper presents an axiology of data science, its purpose, nature, importance, risks, and value for problem solving.
arXiv Detail & Related papers (2023-07-19T21:12:04Z) - Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems [268.585904751315]
New area of research known as AI for science (AI4Science)
Areas aim at understanding the physical world from subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales.
Key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods.
arXiv Detail & Related papers (2023-07-17T12:14:14Z) - Defining data science: a new field of inquiry [0.0]
Modern data science is in its infancy. Emerging slowly since 1962 and rapidly since 2000, it is one of the most active, powerful, and rapidly evolving 21st century innovations.
Due to its value, power, and applicability, it is emerging in over 40 disciplines, hundreds of research areas, and thousands of applications.
This research addresses this data science multiple definitions challenge by proposing the development of coherent, unified definition based on a data science reference framework.
arXiv Detail & Related papers (2023-06-28T12:58:42Z) - Modeling Information Change in Science Communication with Semantically
Matched Paraphrases [50.67030449927206]
SPICED is the first paraphrase dataset of scientific findings annotated for degree of information change.
SPICED contains 6,000 scientific finding pairs extracted from news stories, social media discussions, and full texts of original papers.
Models trained on SPICED improve downstream performance on evidence retrieval for fact checking of real-world scientific claims.
arXiv Detail & Related papers (2022-10-24T07:44:38Z) - Climate Change Policy Exploration using Reinforcement Learning [0.0]
We use four different Reinforcement Learning agents varying in complexity to probe the environment in different ways.
We use a reward function based on planetary boundaries that we modify to force the agents to find a wider range of strategies.
arXiv Detail & Related papers (2022-10-23T18:20:17Z) - Causal Fairness Analysis [68.12191782657437]
We introduce a framework for understanding, modeling, and possibly solving issues of fairness in decision-making settings.
The main insight of our approach will be to link the quantification of the disparities present on the observed data with the underlying, and often unobserved, collection of causal mechanisms.
Our effort culminates in the Fairness Map, which is the first systematic attempt to organize and explain the relationship between different criteria found in the literature.
arXiv Detail & Related papers (2022-07-23T01:06:34Z) - Quantum technologies for climate change: Preliminary assessment [0.0]
Climate change presents an existential threat to human societies and the Earth's ecosystems.
Quantum technologies in computing, sensing, and communication could become useful tools to diagnose and help mitigate the effects of climate change.
This report aims to identify potential high-impact use-cases of quantum technologies for climate change with a focus on four main areas.
arXiv Detail & Related papers (2021-06-23T18:02:19Z) - NLP for Climate Policy: Creating a Knowledge Platform for Holistic and
Effective Climate Action [2.482368922343792]
The paper thematically discusses how NLP techniques could be employed in climate policy research.
We exemplify symbiosis of NLP and Climate Policy Research via four methodologies.
The present theme paper further argues that creating a knowledge platform would help in the formulation of a holistic climate policy.
arXiv Detail & Related papers (2021-05-12T12:30:02Z) - Indecision Modeling [50.00689136829134]
It is important that AI systems act in ways which align with human values.
People are often indecisive, and especially so when their decision has moral implications.
arXiv Detail & Related papers (2020-12-15T18:32:37Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.