Indeed, in these systems the very notion of identification or causation becomes blurred. (b) Heuristic Behaviour  Mainstream models in macro-economics and finance tend to have a fairly sophisticated treatment of risk. Provided the distribution of possible outcomes is reasonably well-understood, this risk can be priced and hedged in financial markets. Saving and investment behaviour can then be analysed under the assumption agents optimise their risk-adjusted decision-making (Haldane (2012)). 22  All speeches are available online at www.bankofengland.co.uk/publications/Pages/speeches/default.aspx  22  A world of radical uncertainty, the like of which is arises in a complex system, changes that perspective fundamentally. Uncertainty means it may sometimes be impossible to compute future outcomes. In the language of computer science, behavioural decisions are no longer “Turing computable” (Beinhocker (2006), Velupillai (2000)). The relevant Euler conditions, familiar from mainstream macro-models, may not even exist. Faced with that uncertainty, it is often rational for agents to rely on simpler decision rules (Gigerenzer (2015)). These are often called “rules of thumb” or heuristics. Use of such rules is sometimes thought to be arbitrary, sub-optimal or irrational. Yet in a world of uncertainty, that is far from clear. Heuristics may be the most robust means of making decisions in a world of uncertainty. Rather than solving a complex inter-temporal optimisation, many consumers appear to follow simple rules of thumb when deciding their spending (Allen and Carroll (2001)). Rather than solving a complex mean-variance optimisation, many investors appear to invest passively or to equally-weight assets in their portfolios (Gigerenzer (2014)).

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.