Rethinking evidence-based policy

The 2008-09 financial crash and the 2020-21 Covid-19 pandemic expose the fragilities of our economic and social systems. Both involve systemic risks that exploit the underlying conditions, or co-morbidities, of the economy and society – including a misallocation of finance and a lack of domestic industrial capacity to produce critical medical supplies. The two crises probe the collective immune system and prey upon the weaknesses of private businesses and public institutions alike. They hold important lessons for public policy, including the overreliance on certain forms of probabilistic modelling, the under-investment in domestic production and public services as well as the contrasting fortunes of different models of governance and institutions. It is not just specific policies that are in question. The twin shocks over the past decade or so cast doubt on evidence-based policy-making in general and the use of behavioural or natural scientific approaches in determining policy decisions in particular, notably the assigning of probabilities to rival scenarios. Problems with probabilistic models and their misapplication are not new. A key factor in the 2008-09 global financial crisis was an excessive and uncritical use in financial services of models drawn from physics (Weatherall 2014; cf. Smolin 2006; Thorp and Kassouf 1967). In the past ten years we have come a long way in understanding the limits of modelling financial and economic processes based on human behaviour that is supposedly determined by general physical laws or individual psychological dispositions (Akerlof and Shiller 2009). Modelling economic and financial decision either ignored behavioural aspects altogether or reduced them to purely individual actions that can be ‘nudged’ (Thaler and Sunstein 2008). Missing from such models is the wider social embedding of individual and group choice, as well as a recognition of the intertwining of economic with social scarring in the event of severe shocks.