16  In the light of the crisis, interest in ABM methods has blossomed, albeit from a low base. They have been used to study the effects of fiscal and monetary policies (Dosi et al (2015)), systemic risk (Geanakoplos et al  14 15 16  Epstein and Axtell (1996). Geanakoplos et al. (2012). Alfi, Cristelli, Pietronero and Zaccaria (2009). 19  All speeches are available online at www.bankofengland.co.uk/publications/Pages/speeches/default.aspx  19  (2012)) and financial market liquidity (Bookstaber (2015)). ABMs of entire economies have also begun to be developed. One example here is the Complexity Research Initiative for Systemic Instabilities (CRISIS), an open source collaboration between academics, firms, and policymakers (Klimek (2015)). Another is EURACE, a large micro-founded macroeconomic model with regional heterogeneity (Dawid (2012)). A third is the Complex Adaptive System model, which incorporates bounded rationality and heterogeneity to reproduce business cycles. 17  A fourth is the MINSKY model. 18  Figure 14: Segregation  Figure 15: Financial markets  Source: Esther Bubley (public domain). Racial segregation in  Source: Katrina Tuliao (CC-BY-2.0). Philippine stock market board  Georgia (image available here). (image available here). The Costs and Benefits of ABM  If ABMs have found relatively limited application in economics and finance, despite widespread application elsewhere, does that matter? That depends on the potential benefits, and associated costs, of ABM techniques in understanding economic phenomena. Starting with costs, ABM technology has been transformed over the past decade, for two reasons. First, the cost and speed of running these models has been revolutionised.

10  All speeches are available online at www.bankofengland.co.uk/publications/Pages/speeches/default.aspx  10  Yet in economics, ABMs have been used relatively less (Battiston et al (2016)). Chart 5 shows the number of citations that the most-cited article on agent-based modelling has in different fields. Economics has been a late and slow-adopter of ABM techniques. In other words, ABMs may be another example of the lack of cross-disciplinary perspective in economics. To be clear, ABMs are no panacea for the modelling ills of economics. In discussing them here, the implication is not that they should replace DSGE models, lock, stock and barrel. Rather, their value comes from providing a different, complementary, lens through which to make sense of our dappled economic and financial world, a lens which other disciplines have found useful when understanding their worlds or when devising policies to improve it. (a) Early Origins  ABMs grew out of some of the earliest applications of computing at Los Alamos National Laboratory in the United States (Metropolis (1987)). In 1945, it was this lab that developed the first-ever nuclear weapon – the fission bomb. The chain-reactions used in these weapons are based on the fissioning of large atoms by neutrons. Neutron transport involves high-dimension, discrete collisions, with each step probabilistic. This quickly gives rise to a vast probability tree of possible outcomes. Enrico Fermi had experimented with a method to solve these sorts of high-dimensional problems in the 1930s, using a small mechanical adding machine.