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079 — Escape from Model Land, a Conversation with Dr. Erica Thompson

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Manage episode 376703383 series 2604595
İçerik Alexander Schatten tarafından sağlanmıştır. Bölümler, grafikler ve podcast açıklamaları dahil tüm podcast içeriği doğrudan Alexander Schatten veya podcast platform ortağı tarafından yüklenir ve sağlanır. Birinin telif hakkıyla korunan çalışmanızı izniniz olmadan kullandığını düşünüyorsanız burada https://tr.player.fm/legal özetlenen süreci takip edebilirsiniz.

Todays guest is Dr. Erica Thompson who wrote the excellent book "Escape from Model Land", which I strongly recommend for reading.

Dr. Thompson is Associate Professor of Modelling for Decision Making at UCL’s Department of Science, Technology, Engineering and Public Policy. She is also a Fellow of the London Mathematical Laboratory, where she leads the research programme on Inference from Models, and a Visiting Senior Fellow at the LSE Data Science Institute.

She is working on the appropriate application of mathematical modelling in supporting real-world decisions, including ethical and methodological questions. For instance, what is the best use of models in climate change, public health and economics.

Making and using models in the real world is — as it turns out — quite a tricky business and in our conversation we go deep into the question: what constitutes model land and how can we escape model land to achieve good results for our society from what we learned in model land.

I covered similar topics in other podcast episodes, because this question can be tackled from a number of different perspectives.

The first question I ask Dr. Thompson is the obvious one: What is model land?

“Nobody actually cares at all about what happens in your model. […] unless you make a claim that what happens within that model land has some relationship to what happens in the real world. So, how to transfer your judgement about the model to the judgement about the real world, is the key question?”

What does Steven Wolfram mean with irreducibility of nature? Why do we have to treat different types of models differently?

What is the difference between interpolation and extrapolation, and why is this crucially important? Many models of complex systems incorporate significant amounts of expert judgement, especially when models are extrapolating. How should we deal with such models?

“All of these decisions about model construction imply value judgements about what we think to be important.”

Value judgements per se are not the problem — but are they shared by the people affected by the model? How did you get to those judegements? Are the transparent enough? Do the decision makers know and agree with these judgements?

Under what conditions can we assess the reliability of a model? In which category do models that are discussed in public fall, for instance climate models? What are the butterfly and hawkmoth effect? What is the difference between data driven vs. “expert driven” models and what role does data quality play in practice?

Most models also are partial models. What is incorporated in a model? What is left out? What conclusions are we allowed to draw from complex models? Do they highly successful data driven models distort our expections in the more assumption driven ones?

“The model is then very much part of the story. It is not just a prediction engine.”

There are models that influence the world and the world feeds back opposed to models that “just” describe the world, and performative models that actually create the reality they describe and counter-performative models. Why is it important to distinguish among these different types?

“Those [counter-performative models] were not made with the aim to be accurate models and correctly predicting the future. They were made with the aim of showing what could happen if we didn't action which would then avoid these worst case scenarios.”

What is the difference between a (conditional or unconditional) prediction and a scenario?

Models are tools and cannot replace judgement. But did we use these tools accordingly? Or did models in the recent past (e.g. Covid) inflict more harm than good on our society?

“This is exactly what models are for—to serve as working hypotheses for further research.”, Ludwig von Bertalanffy

and

“Build a society that is resistant to model errors”, Nassim Taleb

Is this true?

Models as narrative generating devices and communication tools and collective thinking — do we want that? Under what conditions — like flatten the curve? And, how to avoid group think and be captured by models?

“Plans are worthless but planning is everything”, Dwight D. Eisenhower
“Kein Plan überlebt die erste Feindberührung”, Helmuth Graf von Moltke

So, there is a significant amount of expert judgement in building models, but do people know that and which expert do we trust?

“Trust is a social process and expertise is socially determined. […]
You must follow the science is saying you must agree with my value judgements.[…]

A decision can never be science based.”

Thus, science is never value free.

Finally we talk about regulation in complex systems and how those relate to models, the long and short term perspectives and what skin in the game means. Is Niall Ferguson right when he says:

“Surely, once we have written a regulation for every possible misdeed, then good behaviour will ensue. This is just an amazing illustration of our ability as human beeings to keep doing the wrong thing in the face of all experience. […] the big players are actually protected by complex regulation. […]

Regulation is the disease of which it pretends to be the cure.”

Then, how should we regulate complex systems? Should every politician be a scientist in the Platonic sense?

»Ultimately the definition of an expert is somebody who's judegements you are willing to accept as your own.«

References

Other Episodes

  • Episode 68: Modelle und Realität, ein Gespräch mit Dr. Andreas Windisch
  • Episode 67: Wissenschaft, Hype und Realität — ein Gespräch mit Stephan Schleim
  • Episode 53: Data Science und Machine Learning, Hype und Realität — Teil 1
  • Episode 54: Data Science und Machine Learning, Hype und Realität — Teil 2
  • Episode 39: Follow the Science?
  • Episode 37: Probleme und Lösungen
  • Episode 2: Was wissen wir?
Dr. Erica Thompson

Other References

  continue reading

91 bölüm

Artwork
iconPaylaş
 
Manage episode 376703383 series 2604595
İçerik Alexander Schatten tarafından sağlanmıştır. Bölümler, grafikler ve podcast açıklamaları dahil tüm podcast içeriği doğrudan Alexander Schatten veya podcast platform ortağı tarafından yüklenir ve sağlanır. Birinin telif hakkıyla korunan çalışmanızı izniniz olmadan kullandığını düşünüyorsanız burada https://tr.player.fm/legal özetlenen süreci takip edebilirsiniz.

Todays guest is Dr. Erica Thompson who wrote the excellent book "Escape from Model Land", which I strongly recommend for reading.

Dr. Thompson is Associate Professor of Modelling for Decision Making at UCL’s Department of Science, Technology, Engineering and Public Policy. She is also a Fellow of the London Mathematical Laboratory, where she leads the research programme on Inference from Models, and a Visiting Senior Fellow at the LSE Data Science Institute.

She is working on the appropriate application of mathematical modelling in supporting real-world decisions, including ethical and methodological questions. For instance, what is the best use of models in climate change, public health and economics.

Making and using models in the real world is — as it turns out — quite a tricky business and in our conversation we go deep into the question: what constitutes model land and how can we escape model land to achieve good results for our society from what we learned in model land.

I covered similar topics in other podcast episodes, because this question can be tackled from a number of different perspectives.

The first question I ask Dr. Thompson is the obvious one: What is model land?

“Nobody actually cares at all about what happens in your model. […] unless you make a claim that what happens within that model land has some relationship to what happens in the real world. So, how to transfer your judgement about the model to the judgement about the real world, is the key question?”

What does Steven Wolfram mean with irreducibility of nature? Why do we have to treat different types of models differently?

What is the difference between interpolation and extrapolation, and why is this crucially important? Many models of complex systems incorporate significant amounts of expert judgement, especially when models are extrapolating. How should we deal with such models?

“All of these decisions about model construction imply value judgements about what we think to be important.”

Value judgements per se are not the problem — but are they shared by the people affected by the model? How did you get to those judegements? Are the transparent enough? Do the decision makers know and agree with these judgements?

Under what conditions can we assess the reliability of a model? In which category do models that are discussed in public fall, for instance climate models? What are the butterfly and hawkmoth effect? What is the difference between data driven vs. “expert driven” models and what role does data quality play in practice?

Most models also are partial models. What is incorporated in a model? What is left out? What conclusions are we allowed to draw from complex models? Do they highly successful data driven models distort our expections in the more assumption driven ones?

“The model is then very much part of the story. It is not just a prediction engine.”

There are models that influence the world and the world feeds back opposed to models that “just” describe the world, and performative models that actually create the reality they describe and counter-performative models. Why is it important to distinguish among these different types?

“Those [counter-performative models] were not made with the aim to be accurate models and correctly predicting the future. They were made with the aim of showing what could happen if we didn't action which would then avoid these worst case scenarios.”

What is the difference between a (conditional or unconditional) prediction and a scenario?

Models are tools and cannot replace judgement. But did we use these tools accordingly? Or did models in the recent past (e.g. Covid) inflict more harm than good on our society?

“This is exactly what models are for—to serve as working hypotheses for further research.”, Ludwig von Bertalanffy

and

“Build a society that is resistant to model errors”, Nassim Taleb

Is this true?

Models as narrative generating devices and communication tools and collective thinking — do we want that? Under what conditions — like flatten the curve? And, how to avoid group think and be captured by models?

“Plans are worthless but planning is everything”, Dwight D. Eisenhower
“Kein Plan überlebt die erste Feindberührung”, Helmuth Graf von Moltke

So, there is a significant amount of expert judgement in building models, but do people know that and which expert do we trust?

“Trust is a social process and expertise is socially determined. […]
You must follow the science is saying you must agree with my value judgements.[…]

A decision can never be science based.”

Thus, science is never value free.

Finally we talk about regulation in complex systems and how those relate to models, the long and short term perspectives and what skin in the game means. Is Niall Ferguson right when he says:

“Surely, once we have written a regulation for every possible misdeed, then good behaviour will ensue. This is just an amazing illustration of our ability as human beeings to keep doing the wrong thing in the face of all experience. […] the big players are actually protected by complex regulation. […]

Regulation is the disease of which it pretends to be the cure.”

Then, how should we regulate complex systems? Should every politician be a scientist in the Platonic sense?

»Ultimately the definition of an expert is somebody who's judegements you are willing to accept as your own.«

References

Other Episodes

  • Episode 68: Modelle und Realität, ein Gespräch mit Dr. Andreas Windisch
  • Episode 67: Wissenschaft, Hype und Realität — ein Gespräch mit Stephan Schleim
  • Episode 53: Data Science und Machine Learning, Hype und Realität — Teil 1
  • Episode 54: Data Science und Machine Learning, Hype und Realität — Teil 2
  • Episode 39: Follow the Science?
  • Episode 37: Probleme und Lösungen
  • Episode 2: Was wissen wir?
Dr. Erica Thompson

Other References

  continue reading

91 bölüm

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