Any other policy is anti-social, destructive of confidence and incompatible with the working of the economic system”. 8 In the US, these sentiments were echoed in the immediate post-war era by Benjamin Graham, the original “value investor” and yesteryear investment guru to today’s investment guru, Warren Buffett: “A serious investor is not likely to believe that the day-to-day or even month-to-month fluctuations of the stock market make him richer or poorer”.9 And, famously, “in the short run, the market is a voting machine but in the long run, it is a weighing machine”. Whether an untutored savage, defective telescope or anti-social voting machine, something sounded amiss. Thus far, however, this evidence was largely anecdotal. It was not until the 1960s that the short-termism hypothesis was first tested empirically. This drew on survey evidence from investing firms. It found that investors typically expected full pay-back on an investment within 3 to 5 years. At the time, the average life of plant and equipment was often 10 times that.10 Firms played short even when they desired long. The first quantitative evidence that discount rates might be high began to appear in the early 1970s. For example, King (1972) examined investment in plant and machinery in the UK. Empirical estimates suggested the internal discount rate implied by firms’ corporate investment decisions may be up to 25%.11 This literature failed to catch fire. Starting in the  5  Jevons (1871). 6  Marshall (1890). 7  Pigou (1920). 8  Keynes (1938). 9  Graham (1949).
23 These measures appear to have contributed both to the rapid subsidence of SARS-related fears and uncertainties among the general public and to containing the spread of the disease. Since April 2004, there have been no reported cases of SARS. The global information infrastructure of GOARN is widely acknowledged as having helped nip the SARS crisis in the bud. There are important lessons here for the financial system. At present, risk measurement in financial systems is atomistic. Risks are evaluated node by node. In a network, this approach gives little sense of risks to the nodes, much less to the overall system. It risks leaving policymakers navigating in dense fog when assessing the dynamics of the financial system following failure. The market repercussions of Lehman’s failure were in part the result of such restricted visibility. What more might be done to prevent a repeat? Part of the answer lies in improved data, part in improved analysis of that data, and part in improved communication of the results. On data, in some real-world physical networks, data is collected on virtually all nodes and links. For example, in modelling the US electricity grid, data are collected on all major power stations (nodes) and power lines (links). 24 As these total 14,000 and 20,000 respectively, this is a large-dimension network. Data from physical networks such as the power grid are relatively easy to collect. For many other large-dimension networks, sampling techniques are typically required. These typically take one of three forms: node sampling; link sampling; and “snowball” sampling.