Escape from Model Land
How Mathematical Models Can Lead Us Astray and What We Can Do About It
-
- 39,00 kr
-
- 39,00 kr
Utgivarens beskrivning
'A brilliant account of how models are so often abused and of how they should be used' John Kay
How do mathematical models shape our world - and how can we harness their power for good?
Models are at the centre of everything we do. Whether we use them or are simply affected by them, they act as metaphors that help us better understand the increasingly complex problems facing us in the modern world. Without models, we couldn't begin to tackle three of the major challenges facing modern society: regulation of the economy, climate change and the COVID-19 pandemic. Yet in recent years, the validity of the models we use has been hotly debated and there has been renewed awareness of the disastrous consequences when the makers and interpreters of models get things wrong.
Drawing on contemporary examples from finance, climate and health policy, Erica Thompson explores what models are, why we need them, how they work and what happens when they go wrong. This is not a book that argues we should do away with models, but rather, that we need to properly understand how they are constructed - and how some of the assumptions that underlie the models we use can have significant unintended consequences. Unexpectedly humorous, thought-provoking and passionate, this is essential reading for everyone.
PUBLISHERS WEEKLY
Thompson, a senior policy fellow at the London School of Economics, debuts with an eye-opening account of the limits and uses of mathematical models. Thompson explains that models are metaphors for the real world, and that it's crucial to avoid taking them too literally. "Force equals mass times acceleration is the ‘correct model' to use to solve the question" of when a truck would reach 60 mph, for example, but real-world conditions contain variables that the model can't account for. Thompson offers a host of lessons, among them that every model depends upon value judgments to determine what's included in them, that models should be understood as "not an objective mathematical reality, but a social idea," and that models contain the biases of those who make them, so increased diversity among modelers is essential for "greater insight, improved decision-making capacities and better outcomes." Thompson wraps up with a list of principles for "responsible modelling," including deciding "to what purpose(s)" models should be applied, and if "decisions informed by this model will influence other people or communities" who weren't considered or consulted in the making of the model. The result is a thoughtful, convincing look at how data works.