Chrysi Malouchou Kanellopoulou reviews The Random Universe: How Models and Probability Help us Make Sense of the Cosmos by Andrew H Jaffe
All scientific inquiry involves some kind of uncertainty, but the uncertainties in cosmology are particularly daunting. As well as being complex and difficult to describe, the systems cosmologists investigate are almost by definition out of reach. In these conditions of uncertainty, how confident can we be in the models we’ve built? How can we come to know how galaxies form, how the universe began or how much dark matter it contains?
In The Random Universe: How Models and Probability Help us Make Sense of the Cosmos, cosmologist and astrophysicist Andrew Jaffe offers a masterful and refreshing take on these questions. As the director of the Centre for Inference and Cosmology at Imperial College London, UK, and a past co-investigator on ESA’s Planck satellite that provided us with so much new information about the cosmos, Jaffe is well-equipped to walk readers through the tools that allow us to learn about the world despite its uncertainties. Anchoring his insights in beautifully narrated episodes from the history of physics, astrophysics and cosmology, he sheds light on how our theories of the universe evolved into our current best cosmological model – which nonetheless still has limitations.
Jaffe is particularly good at recounting how we refined, revised and even replaced some of our assumptions in light of incoming evidence such as the first measurements of stellar distance; the discovery of galaxies beyond our own; and precise measurements of cosmological parameters. Throughout this journey, he engages thoughtfully with a rich philosophical tradition that runs from David Hume in the 18th century through Willard Van Orman Quine, Imre Lakatos and Paul Feyerabend in the 20th.
Bayes’ theorem is important because it tells us how to update our degree of belief in light of new evidence
The book’s main protagonist, though, is the mathematician Thomas Bayes. For Jaffe, Bayesian probability is key to dealing with uncertainty, and even to solving Hume’s notorious problem of induction: how do we justify inferences from a finite set of observations to generalizations that go beyond them? Bayes’ theorem is important, Jaffe argues, because it tells us how to update our degree of belief in light of new evidence. It shows us why and how, even in an uncertain world, “the more observations we make, the surer we become”.
Of course, before we can use probability to deal with uncertainty, we first need a model. As Jaffe puts it, “Absent a model, I can’t assign a probability, and absent a probability, I can’t quantify my uncertainty about the world.” So, what exactly is a model? I found Jaffe’s answer to be one of the most captivating elements of the book. As the title of a subsection of chapter five puts it, “a model is a story about the world”. Without models, Jaffe tells us, our world would be an unexplained series of unrelated events. Models are also what allow us to take a finite set of data and use it to create something new. Without this leap, Jaffe suggests, we cannot learn anything about the world, because “you can’t make sense of an experimental result unless you have some model to interpret it”.
Of course, if the way we interpret data depends on our model, then different people may well obtain different interpretations depending on which model and which prior probability they choose. This charge of subjectivity is a longstanding accusation against Bayesians, but Jaffe offers at least a partial solution: “If you and I agree on the model, we should assign the same probability.” This, Jaffe argues, is where objectivity emerges, writing that “we come to the same conclusions if we start from the same premises”.
Models allow us to understand the world by bridging subjectivity with objectivity, and the personal with the collective
Many contemporary philosophers are at odds with the idealized view of science as an objective and linear enterprise that produces theories from a God’s-eye view. Instead, they strive to portray it as something that is social and human, but reliable nonetheless. Jaffe’s own views seem to align with this endeavour; as he puts it, “the scientific coupling of models and observations is a living, breathing, subjective endeavour, simultaneously collective and personal”. Through their interplay with data, he argues, models allow us to understand the world by bridging subjectivity with objectivity, and the personal with the collective.
Not everyone will agree with Jaffe that Bayesian probability offers a solution to Hume’s problem of induction. Personally, I (or perhaps the hardcore rationalist in me) was not convinced. Nevertheless, I found the book’s thesis – that uncertainty is not a hindrance to knowledge, but an opportunity to learn (and learn more) about the world – simultaneously liberating, humbling and empowering. I recommend The Random Universe to scientists, philosophers and anyone interested in better understanding the role of probability in science; the philosophical problems with how we handle uncertainty; and how our standard cosmological model came to be. I also hope it will encourage more collaborations between philosophers and scientists. There are many questions we can explore further if we work together.
- 2025 Yale University Press, £25hb, 288pp
