Wendy S Parker
Biographic Data
| ID | 1074262 |
|---|---|
| NAME | Wendy S Parker |
| GIVEN NAMES | Wendy S |
| FAMILY NAME | Parker |
| SIGNATURE | PARKER W S |
| AFFILIATIONS | Ohio University |
| ORCID | 0000-0001-7796-3401 |
| VERIFIED | Yes |
| TOTAL WORKS | 16 |
| TOTAL CITATIONS | 197 |
| WORKS AS AUTHOR | 16 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2008 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 8 |
Understanding data uncertainty
Die Natur im Computer. Können wir aus Simulationen etwas über die Welt lernen
The Epistemic Projection Approach to Values in Science
I develop a novel account of how nonepistemic aims and values can appropriately influence scientific investigation. At its heart is a process of epistemic projection in which a nonepistemic aim or value is mapped to an epistemic research problem that aligns with that aim or value. Choices in research are then justified as a means of solving that research problem. This epistemic projection approach makes research responsive to nonepistemic aims an…
Evidence and Knowledge from Computer Simulation
Can computer simulation results be evidence for hypotheses about real-world systems and phenomena? If so, what sort of evidence? Can we gain genuinely new knowledge of the world via simulation? I argue that evidence from computer simulation is aptly characterized as higher - order evidence : it is evidence that other evidence regarding a hypothesis about the world has been collected. Insofar as particular epistemic agents do not have this other e…
Model Evaluation
According to an adequacy-for-purpose view, models should be assessed with respect to their adequacy or fitness for particular purposes. Such a view has been advocated by scientists and philosophers alike. Important details, however, have yet to be spelled out. This article attempts to make progress by addressing three key questions: What does it mean for a model to be adequate-for-purpose? What makes a model adequate-for-purpose? How does assessi…
Local Model-Data Symbiosis in Meteorology and Climate Science
I introduce a distinction between general and local model-data symbiosis and offer three examples of local symbiosis in the fields of meteorology and climate science. Local model-data symbiosis refers to a beneficial, two-way interdependence between a particular model and data set or between closely related models and data sets. For each example presented here, I show how the symbiotic relationship works—what the interdependence consists in and h…
Issues in the theoretical foundations of climate science
Computer Simulation, Measurement, and Data Assimilation
This article explores some of the roles of computer simulation in measurement. A model-based view of measurement is adopted and three types of measurement-direct, derived, and complex-are distinguished. It is argued that while computer simulations on their own are not measurement processes, in principle they can be embedded in direct, derived, and complex measurement practices in such a way that simulation results constitute measurement outcomes.…
Environmental Science
Arguments in environmental ethics often appeal to information from environmental science, such as information about the causes of environmental problems. Contemporary work in philosophy of science can shed light on the practice of environmental science as well as some of the challenges it faces. This chapter surveys some of this work, focusing on three interrelated topics: the nature of scientific evidence, including connections with uncertainty …
Ensemble modeling, uncertainty and robust predictions
Many studies of future climate change take an ensemble modeling approach in which simulations of future conditions are produced with multiple climate models (or model versions), rather than just one. These ensemble studies are of two main types—perturbed‐physics and multimodel—which investigate different sources of uncertainty about future climate change. Increasingly, methods are being applied which assign probabilities to future changes in clim…
When Climate Models Agree
This article identifies conditions under which robust predictive modeling results have special epistemic significance—related to truth, confidence, and security—and considers whether those conditions hold in the context of present-day climate modeling. The findings are disappointing. When today’s climate models agree that an interesting hypothesis about future climate change is true, it cannot be inferred—via the arguments considered here anyway—…
Predicting weather and climate
Comparative Process Tracing and Climate Change Fingerprints
Climate change fingerprint studies investigate the causes of recent climate change. I argue that these studies have much in common with Steel's streamlined comparative process tracing, illustrating a mechanisms-based approach to extrapolation in which the mechanisms of interest are simulated rather than physically instantiated. I then explain why robustness and variety-of-evidence considerations turn out to be important for understanding the evid…
Whose Probabilities? Predicting Climate Change with Ensembles of Models
Today's most sophisticated simulation studies of future climate employ not just one climate model but a number of models. I explain why this “ensemble” approach has been adopted—namely, as a means of taking account of uncertainty—and why a comprehensive investigation of uncertainty remains elusive. I then defend a middle ground between two camps in an ongoing debate over the transformation of ensemble results into probabilistic predictions of cli…
Does matter really matter? Computer simulations, experiments, and materiality
Computer simulation through an error-statistical lens
Model Evaluation
According to an adequacy-for-purpose view, models should be assessed with respect to their adequacy or fitness for particular purposes. Such a view has been advocated by scientists and philosophers alike. Important details, however, have yet to be spelled out. This article attempts to make progress by addressing three key questions: What does it mean for a model to be adequate-for-purpose? What makes a model adequate-for-purpose? How does assessi…
When Climate Models Agree
This article identifies conditions under which robust predictive modeling results have special epistemic significance—related to truth, confidence, and security—and considers whether those conditions hold in the context of present-day climate modeling. The findings are disappointing. When today’s climate models agree that an interesting hypothesis about future climate change is true, it cannot be inferred—via the arguments considered here anyway—…
Does matter really matter? Computer simulations, experiments, and materiality
Computer Simulation, Measurement, and Data Assimilation
This article explores some of the roles of computer simulation in measurement. A model-based view of measurement is adopted and three types of measurement-direct, derived, and complex-are distinguished. It is argued that while computer simulations on their own are not measurement processes, in principle they can be embedded in direct, derived, and complex measurement practices in such a way that simulation results constitute measurement outcomes.…
Whose Probabilities? Predicting Climate Change with Ensembles of Models
Today's most sophisticated simulation studies of future climate employ not just one climate model but a number of models. I explain why this “ensemble” approach has been adopted—namely, as a means of taking account of uncertainty—and why a comprehensive investigation of uncertainty remains elusive. I then defend a middle ground between two camps in an ongoing debate over the transformation of ensemble results into probabilistic predictions of cli…
Ensemble modeling, uncertainty and robust predictions
Many studies of future climate change take an ensemble modeling approach in which simulations of future conditions are produced with multiple climate models (or model versions), rather than just one. These ensemble studies are of two main types—perturbed‐physics and multimodel—which investigate different sources of uncertainty about future climate change. Increasingly, methods are being applied which assign probabilities to future changes in clim…
Computer simulation through an error-statistical lens
Comparative Process Tracing and Climate Change Fingerprints
Climate change fingerprint studies investigate the causes of recent climate change. I argue that these studies have much in common with Steel's streamlined comparative process tracing, illustrating a mechanisms-based approach to extrapolation in which the mechanisms of interest are simulated rather than physically instantiated. I then explain why robustness and variety-of-evidence considerations turn out to be important for understanding the evid…
The Epistemic Projection Approach to Values in Science
I develop a novel account of how nonepistemic aims and values can appropriately influence scientific investigation. At its heart is a process of epistemic projection in which a nonepistemic aim or value is mapped to an epistemic research problem that aligns with that aim or value. Choices in research are then justified as a means of solving that research problem. This epistemic projection approach makes research responsive to nonepistemic aims an…
Local Model-Data Symbiosis in Meteorology and Climate Science
I introduce a distinction between general and local model-data symbiosis and offer three examples of local symbiosis in the fields of meteorology and climate science. Local model-data symbiosis refers to a beneficial, two-way interdependence between a particular model and data set or between closely related models and data sets. For each example presented here, I show how the symbiotic relationship works—what the interdependence consists in and h…
Computer simulation through an error-statistical lens
Does matter really matter? Computer simulations, experiments, and materiality
Predicting weather and climate
Comparative Process Tracing and Climate Change Fingerprints
Climate change fingerprint studies investigate the causes of recent climate change. I argue that these studies have much in common with Steel's streamlined comparative process tracing, illustrating a mechanisms-based approach to extrapolation in which the mechanisms of interest are simulated rather than physically instantiated. I then explain why robustness and variety-of-evidence considerations turn out to be important for understanding the evid…
Whose Probabilities? Predicting Climate Change with Ensembles of Models
Today's most sophisticated simulation studies of future climate employ not just one climate model but a number of models. I explain why this “ensemble” approach has been adopted—namely, as a means of taking account of uncertainty—and why a comprehensive investigation of uncertainty remains elusive. I then defend a middle ground between two camps in an ongoing debate over the transformation of ensemble results into probabilistic predictions of cli…
When Climate Models Agree
This article identifies conditions under which robust predictive modeling results have special epistemic significance—related to truth, confidence, and security—and considers whether those conditions hold in the context of present-day climate modeling. The findings are disappointing. When today’s climate models agree that an interesting hypothesis about future climate change is true, it cannot be inferred—via the arguments considered here anyway—…
Ensemble modeling, uncertainty and robust predictions
Many studies of future climate change take an ensemble modeling approach in which simulations of future conditions are produced with multiple climate models (or model versions), rather than just one. These ensemble studies are of two main types—perturbed‐physics and multimodel—which investigate different sources of uncertainty about future climate change. Increasingly, methods are being applied which assign probabilities to future changes in clim…
Environmental Science
Arguments in environmental ethics often appeal to information from environmental science, such as information about the causes of environmental problems. Contemporary work in philosophy of science can shed light on the practice of environmental science as well as some of the challenges it faces. This chapter surveys some of this work, focusing on three interrelated topics: the nature of scientific evidence, including connections with uncertainty …
Computer Simulation, Measurement, and Data Assimilation
This article explores some of the roles of computer simulation in measurement. A model-based view of measurement is adopted and three types of measurement-direct, derived, and complex-are distinguished. It is argued that while computer simulations on their own are not measurement processes, in principle they can be embedded in direct, derived, and complex measurement practices in such a way that simulation results constitute measurement outcomes.…
Issues in the theoretical foundations of climate science
Model Evaluation
According to an adequacy-for-purpose view, models should be assessed with respect to their adequacy or fitness for particular purposes. Such a view has been advocated by scientists and philosophers alike. Important details, however, have yet to be spelled out. This article attempts to make progress by addressing three key questions: What does it mean for a model to be adequate-for-purpose? What makes a model adequate-for-purpose? How does assessi…
Local Model-Data Symbiosis in Meteorology and Climate Science
I introduce a distinction between general and local model-data symbiosis and offer three examples of local symbiosis in the fields of meteorology and climate science. Local model-data symbiosis refers to a beneficial, two-way interdependence between a particular model and data set or between closely related models and data sets. For each example presented here, I show how the symbiotic relationship works—what the interdependence consists in and h…
Evidence and Knowledge from Computer Simulation
Can computer simulation results be evidence for hypotheses about real-world systems and phenomena? If so, what sort of evidence? Can we gain genuinely new knowledge of the world via simulation? I argue that evidence from computer simulation is aptly characterized as higher - order evidence : it is evidence that other evidence regarding a hypothesis about the world has been collected. Insofar as particular epistemic agents do not have this other e…
Die Natur im Computer. Können wir aus Simulationen etwas über die Welt lernen
The Epistemic Projection Approach to Values in Science
I develop a novel account of how nonepistemic aims and values can appropriately influence scientific investigation. At its heart is a process of epistemic projection in which a nonepistemic aim or value is mapped to an epistemic research problem that aligns with that aim or value. Choices in research are then justified as a means of solving that research problem. This epistemic projection approach makes research responsive to nonepistemic aims an…
Understanding data uncertainty
Computer Science (11 works) · Epistemology (10 works) · Philosophy (10 works) · Climate change (7 works) · Climate variability and models (7 works) · Philosophy and History of Science (6 works) · Climate science (5 works) · Ecology (5 works) · Econometrics (5 works) · Mathematics (5 works)