The 2008 financial crisis revealed failings in the way economists treated the financial system in relation to the real economy and the way regulators dealt with it. The crisis taught us three lessons: finance is central to macroeconomic outcomes; multiple equilibria can be all-important under stressful conditions; and the political economy of policy matters.

The crisis arose from the interaction of several characteristics. First, the extreme sophistication of financial instruments and the development of securitisation, the generalisation of derivative markets, the rapid growth of shadow banking, and the emergence of highly-leveraged institutions. Second, increased interconnectedness between all financial and non-financial institutions, enabled and encouraged by the advance of information technologies, giving rise to new, untested properties of global finance. At the same time, a sentiment of excessive tranquillity and confidence both in the public and private sectors due to sustained growth with low inflation. Third, belief in a “Great Moderation”, a permanent reduction in the volatility of business cycle fluctuations thanks to institutional and structural change. Fourth, consensus in the international community on the efficiency of markets in almost all circumstances, justifying large deregulation. The belief that the financial system could never be far away from a single optimal equilibrium. Fifth, generalised excess leverage was totally neglected by the international community. The explosion of leverage was boosted by the ‘shadow banking’ world of hedge funds, private equity firms, and other unregulated financial companies. When asset values turned, confidence and trust collapsed and leverage became destructive. Incentive structures encouraged traders to make unwarrantedly risky bets, but all traders have individual risk limits and banks’ managements set those limits.

The possibility of financial developments as drivers of economic performance was also largely ignored. In macroeconomic models, the role of the financial system was often reduced to the determination of a yield curve and stock prices. Fluctuations were seen as regular random shocks. In reality, financial crises are characterised by non-linearities and positive feedback whereby shocks are strongly amplified rather than dampened as they propagate. Financial markets, and economic behaviour generally, are a product of human evolution, and the basic principles of mutation, competition, and natural selection apply to the banking industry as much as to natural ecosystems. The key to these laws is adaptive behaviour in shifting environments.

One shift is that rather than returning to the status quo when the shock ends, financial crises are followed by long periods of depressed output. Another non-linearity comes from the interaction between public debt and the banking system, so-called “doom loops”. Higher public debt leads to worries about public debt restructuring, decreasing the value of the bonds held by financial institutions, leading in turn to a decrease in their capital, worries about their health, and the expectation that the state may have to bail them out and be itself in trouble as a result. A boom-bust process driven by private credit also fuels crises: excessive private debt and credit before crisis, negative credit during it (the annual change in private debt being negative rather than positive).

Financial networks therefore are not random, and are likely to have network properties that manifest a statistical signature of complex systems, namely, a top tier multi-hub of few agents who are highly connected among themselves and to other nodes that show few if any connections to others in the periphery. The clustered structure of a network implies short path lengths between a node and any other node in the system. This is efficient in terms of liquidity and informational flows in good times, but worsens fragility in bad times when so-called hub banks (‘super-spreaders’) fail or suffer illiquidity.

The analogy with policies designed to suppress natural disasters should be kept in mind, especially the trade-off between efficiency and resilience. Extinguishing every small fire in a forest may seem like a useful precaution, but it allows undergrowth to proliferate, adding potential fuel to future fires, just as dampening volatility in financial markets encourages risk-taking and increases the chances of a crisis.

Agent-based models (ABM) are a better way to understand the financial system than more traditional approaches. They use a dynamic system of interacting, autonomous agents to allow macroscopic behaviour to emerge from microscopic rules. Likewise, the agent-based approach recognises that individuals interact and thereby change the environment, leading to the next interaction. ABM operate without a representative consumer or investor who is always right. They allow for construction of a narrative, unique to the particular circumstances in the real world, in which the system may be derailed. Narratives are not just a way describing and seeking to understand what has happened. Stories that “go viral” evolve to actually affect outcomes, including crises, depressions, and recessions. The narrative basis of economic phenomena might be hard to see since narratives are not easy to measure, but by incorporating an understanding of popular narratives into their explanations, economists will become more sensitive to such influences and may produce better forecasts.

As models become more realistic, analytics often has to give way to numerical simulations. This is well‑accepted in physics, but many economists are still reluctant to recognise that numerical investigation of a model, although very far from theorem proving, is a valid way to do science. Numerical experiments allow one to quickly qualify an agent-based model (ABM) as potentially realistic or completely off the mark. What makes this expeditious diagnosis possible is the fact that for large systems details do not matter much – only a few microscopic features end up surviving at the macro scale.

Before the crisis, both monetary and prudential policies eased gradually over three decades. Afterwards, monetary easing continued but was accompanied with a justifiable degree of tightening in prudential policies. The big question was whether monetary policy could be normalised and what that ‘new normal’ would look like, in particular, if negative interest rates become a permanent feature of the new normal. The Covid-19 crisis suggests that this is the wrong question to ask. Underlying it is an implicit assumption that it is possible to get back to some theoretically “normal” state that was disrupted. The experiences of 2008 and 2020 suggest that constant flux and potential crises are the new normal, and the financial system should not be seen in isolation from the broader socio-economic and environmental system it is part of.