One of the ‘ten ways that economics gets it wrong' is the idea of ‘the connected economy,' says David Orrell in Economyths ( >www.landmarkonthenet.com ). Techies may like to begin with the section on the science of networks, where the author cites the banking and electrical systems, and transportation and telecommunication networks, as examples of technological networks. “Similar networks are ubiquitous in nature: biological systems are characterised by complex networks of interacting genes and proteins, ecosystems by predator-prey relationships. And sociologists use social networks to investigate the transmission of ideas and trends through society.”

Researchers in the field of network science view such systems in terms of nodes, which represent individuals or agents in the network, and links, which join the nodes and represent interactions of some kind, explains Orrell. Citing a June 2008 research paper of Domenico Delli Gatti et al from the Catholic University of Milan — that it is straightforward to think of agents as nodes and of debt contracts as links in a credit network… the default of one agent can bring about an avalanche of bankruptcies — Orrell adds that if the authors had delayed publication by a few months, they could have used Lehman as an example.

Based on the common features of networks — be they technological, biological, ecological, social, or economic — researchers have classified them into certain categories, one learns. The book mentions, for instance, ‘the small-world network,' where the connections between individual nodes are arranged in such a way that it takes only a small number of steps to link one node to another. The World Wide Web has this property, and search companies such as Google exploit it to derive their algorithms, the author notes.

Scale-free networks are another category, where there is no typical or expected number of connections for any node. Here, most nodes may have few connections to other nodes, but a small number of hubs are highly connected, as Orrell elaborates. “An example is the air traffic network: some airports such as Heathrow are global hubs, while smaller regional airports may fly to only a few destinations.”

Insights from natural systems

Stating that artificial networks with these and other properties can easily be produced and studied on the computer, the author informs that network modelling of the economy has become an active research area, in academia and institutions including the Bank of England. He highlights that a key question that engages network scientists and engineers is network robustness, which often depends strongly on the way in which the network is arranged; and, also instructs that much can be learned from natural systems, such as ecosystems or biological systems, simply because they have been around for a long time so have presumably learned a trick or two.

“Some design principles shared by robust networks — but not currently by our financial system — include modularity, redundancy, diversity, and a process for controlled shut-down. Together they provide clues on how we can reduce the chance of another disaster.” For example, introducing modularity in the financial system would mean separating speculative activities from ordinary commercial banking activities, or dividing large global banks into clearly defined national components, the author suggests. He cautions, however, that these measures can meet the protests of banks because of the reduction in short-term efficiency.

Apprentice engineer in the control room

An interesting question posed in the chapter is whether the credit crunch would ever have happened if politicians and risk experts at banks had been trained or educationally shaped in fields such as complexity and network theory rather than orthodox economics. A telling analogy of the administrative response to the Lehman fiasco that Orrell gives is of an untrained apprentice engineer wandering into the control room and unplugging the thick cable with the ‘Do Not Disconnect' sign above it, and causing nearly lights out for the economy!

He narrates that three days after Lehman's bankruptcy, on September 18, the Federal Reserve of the US had to intervene to stop an electronic bank run on US money market accounts. “As Representative Paul Kanjorski of Pennsylvania explained, they feared that if it were allowed to continue, ‘$5.5 trillion would have been drawn out of the money market system of the US, which would have collapsed the entire economy of the US, and within 24 hours the world economy would have collapsed. It would have been the end of our economic system and our political system as we know it.'”

Educate the ‘financial engineers'

In the author's view, a way to revive economics is to educate our cadre of highly-paid ‘financial engineers' in the principles and codes of real engineering, to ensure that firebreaks and safeguards can prevent systemic failure, and also to help develop diagnostic tools for the collection and analysis of network data. An apt quote given in the book is of Bank of England's Andrew Haldane, about how risk measurement in financial systems is at present 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.”

For starters, the atomic theory of the economy reached its point of highest glory in 1965 with the ‘efficient market hypothesis,' as the book traces. The reference is to a Ph.D thesis by Eugene Fama of the University of Chicago, which described the market as made up of large numbers of rational profit-maximisers who had access to all relevant information and were in active competition with one another. Given these assumptions, Fama argued, prices of any security would automatically adjust to reflect its ‘intrinsic value,' and that any deviations from that level would be small and random, Orrell recounts.

He feels that one reason for the enduring popularity of the hypothesis is that it does make one correct prediction, namely that the markets are unpredictable. “Even big institutions such as the International Monetary Fund (IMF) or the Organisation for Economic Cooperation and Development (OECD), which have access to large computer models and enormous quantities of data, turn out to be no more prescient in their predictions than the forecasters from Bloomberg…”

Tools from mathematical areas

The book's introduction, in fact, opens by reminiscing how according to forecasters polled by Bloomberg.com at the start of 2008, the year was going to be a prosperous one for the financial markets. “None foresaw a loss, and the average prediction was for a gain of 11 per cent. They were blissfully unaware that one of history's biggest financial earthquakes was already taking shape beneath their feet. By year-end the S&P 500 index was down 38 per cent, $29 trillion had slipped through the cracks appearing in global markets, and many of the foundations of the world economy lay in ruins.”

Assuring that the effects of the inevitable disasters and breakdowns can yet be minimised and procedures put in place to get the system up and running as quickly as possible, Orrell recommends the use of new ideas and tools from mathematical areas like network theory and complexity to help frame the problems, test and refine hypotheses, explore and communicate solutions, and motivate changes; for, models can have a large effect on the design of financial structures.

A book that can help you appreciate economics in action, and also help make it less of a voodoo science.

>dmurali@thehindu.co.in

Tailpiece

“We refined our workflow software to also light up the floor indicating the shortest route…”

“In case of emergencies?”

“No, whenever an employee headed towards the water-cooler, because we found the unguided routes to be invariably circuitous to the point of irrationality!”

comment COMMENT NOW