And rather than solve a complex inter-temporal trade-off, monetary policy in practice seems to mimic simple rules of thumb (Taylor (2016)). Some would interpret these simple decision rules as irrational, in the sense of being inconsistent with the Euler condition from standard macro-models. But even the concept of rationally needs careful reconsideration in an environment of radical uncertainty. Rationality can only be defined in relation to the environment in which decisions are made – what some have called ecological rationality (Gigerenzer (2014)). Heuristics can be the ecologically-rational response to radical uncertainty. In ABMs, the behaviour of agents is characterised, not by Euler conditions, but by behavioural rules of thumb. These systems also exhibit radical uncertainty. That means there is a degree of model-consistency in ABMs – heuristics and uncertainty are mutually consistent. In that sense, the behavioural rules embedded in ABMs are neither as irrational, nor as prone to the Lucas critique, as some critics might imply. (c) Non-Normal Behaviour  In many standard models, the equilibrium of the system is both singular and stationary. There is a natural and unique state of rest towards which the model converges following a disturbance. Wicksell’s rocking horse is not a perpetual motion machine and nor does it turn somersaults. While many models of multiple equilibria exist in economics and finance, they tend to occupy the suburbs rather than the city-centre of the profession. In ABMs, the equilibria which emerge are often non-stationary or multiple, sometimes both.
Notes: What happens after a shock to the expected loss rate; a sudden change in firms' expected loss rate on bonds causes both short-term fluctuations in yield, and a new, higher long-term yield. Results presented are the median of 100 individual simulations runs, individual model runs exhibit a range of outcomes. Source: Braun-Munzinger et al (2016). The outcomes for median yield over 100 trading days after a 0.36% loss rate shock. Percentiles indicate the distribution of results taken from 250 simulation runs. How can this model help in understanding the dynamics of real-world financial markets and the appropriate setting of policy in these markets? Chart 13 compares the actual distribution of corporate bond prices changes with the distribution which emerges from ABM simulations. There is a reasonable correspondence between the two, with fatter than normal tails. The model can be used counter-factually – for example, to assess the impact of a rise in the number of passive or momentum traders relative to value investors. This makes for larger and longer-lived oscillations. So too does a reduction in the market-making capacity – for example, lower market-maker inventories – as this amplifies the impact on prices of any shock to fund demand. One topical policy issue is whether constraints might be imposed on some funds to forestall investor redemptions in the face of falling prices and performance. For example, US money market mutual funds experienced such redemption runs during the course of the financial crisis.