As a counter-example, in 2008 Google launched a predictive model of flu outbreaks based on searches for phrases such as “indications of flu”. 10  This did a terrific job of tracking flu outbreaks in the US in 2009-10. 11  But that model’s predictions broke down spectacularly in the following years. That empirical fragility arose, I  would suggest, from an over-reliance on empirical regularities and an over-adherence to inductive methods. Or, put differently, from too little focus being placed on the deep medical causes of past flu outbreaks. In the first case, empirical fragility arose from too tight a set of axioms and restrictions, from placing too great an emphasis on theory over real-world correlations and historical experience. In the second, empirical 5  Haldane (2016). Popper (1934) and Popper (1959). Bacon (1620). 8 Anderson (2008). 9 For example, Stiglitz (2018). 10 Ginsberg et al (2009). 11 Lazer et al (2014). 6 7  3  All speeches are available online at www.bankofengland.co.uk/speeches  3  fragility arose from too loose a set of axioms and restrictions, from observed empirical correlations being given too great a role relative to theory and causality. In both cases, these mistakes might have been reduced had inductive and deductive approaches been used in a complementary, or iterative, fashion. This iterative approach has a strong pedigree in other disciplines.
The history of advance in many scientific disciplines has involved a two-way process of learning between theory and empirics, with theory motivating measurement at some times, and measurement motivating 12  theory at others, in a continuous feedback loop. One example of this approach, discussed by Governor Carney at the time the Bank’s own data analytics 13  programme was launched, concerns the dynamics of planetary motion. It was Sir Isaac Newton (a fellow  money-printer, as former Master of the Royal Mint) who developed the physical theory of celestial motion. But this theory was built on the empirical shoulders of another scientific giant, Johannes Kepler. When it comes to planetary motion, empirics first led theory, the inductive led the deductive. The same has been true, at times, when understanding the motion of economies and financial markets. Keynesian and Monetarist theory were both built on empirical experience during the Great Depression. The Phillips curve began life as a Kepler-esque empirical regularity, which was only subsequently given a Newtonian theoretical basis. Many puzzles in finance, which have taxed theorists for decades, began as 14  empirical anomalies in asset markets. In each case, empirics led theory, the inductive led the deductive. My lesson from all of this is clear. If that iterative learning process between empirics and theory is to continue to bear fruit in economics, then deductive and inductive approaches may need to share a broadly equal billing.