New Directions for Understanding Systemic Risk A Report on a Conference Cosponsored by the Federal Reserve Bank of New York and the National Academy of Sciences (2007) / Chapter Skim
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3 Systemic Risk in Ecology and Engineering
3 Systemic Risk in Ecology and Engineering
Pages 29-54
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From page 29... ...
. Similarly, epidemiologists and public health experts worry about disease outbreaks and spread, which occasionally reach systemic levels, and they have learned lessons in risk management by studying past epidemics.
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From page 30... ...
Complex systems of any sort are characterized by nonlinearities, multiple stable states, hysteresis, contagion, and synchrony, all of which have relevance to the problem of systemic risk. Nonlinear relationships are a key characteristic of virtually any complex system.
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From page 31... ...
In ecosystems, contagion is an important part of ecological and epidemiological dynamics, as exemplified by the mechanisms that spread forest fires and disease. In the financial sector, contagion manifests itself as cascading losses and increased risk aversion, with the latter leading to herd behavior, funding withdrawals, and a contraction of liquidity.
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Behavioral ecologists have developed some understanding of the principles of collective decision making among animals. Complex adaptive systems consist of heterogeneous collections of individual units that interact with one another and thereby influence how the whole system evolves.
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Levin pointed out that, unlike systems designed for robustness, complex adaptive systems are systems in which whatever robustness exists has to emerge from the collective properties of the individual units that make up the system; there is no planner or manager whose decisions completely control the system. Therefore, there are no guarantees that things will work well.
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From page 34... ...
In various literatures, the terms robustness, resilience, rigidity, and resistance are often used to mean the same thing, although they really describe different components of the system's capacity to function in the presence of internal or external disturbances. What leads to robustness in complex adaptive systems?
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Whether diversity increases or decreases stability is an argument over the definition of stability, and it is still being debated. The lesson that might be inferred is that understanding the behavior of complex adaptive systems requires more than just qualitative analysis and more than just theory. Ecologists have applied alternative mathematical frameworks (for example, interacting particle systems or systems of differential equations)
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Levin indicated that transferring the techniques from these models to the study of financial systems would not be difficult, both because the parallels were strong and because researchers in the financial sector would be comfortable with the mathematical techniques. The rich literature of epidemic theory, both mathematical and computational, might then be applicable to understanding runs on banks, as long as this approach was properly augmented with knowledge of human behaviors that contribute
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From page 37... ...
These attributes could then be examined as candidate characteristics for lessening systemic risk in other contexts, such as the financial sector. Because experimental stress testing is not feasible in the financial sector, examining such common structural properties of ecosystems should be of interest, and it might help guide policy.
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He emphasized that the last subtopic must be included in any study of risk because many of the factors that contribute to risk, or follow from extreme events, are organizational problems and human problems. Risk analysis must consider such matters as how well lines of communication function, how much trust exists within a system, and who can share information in a timely and effective way.
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From page 39... ...
For instance, several teams identified software and staff training as key risks, but only the policymaker team identified organizational decision making as a potential risk, and only the operators/owners team identified the quality of electrical infrastructure as a potential risk. This exercise underscores the value of incorporating multiple views and perspectives in efforts to identify sources of risks in complex systems.
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From page 40... ...
By studying the heterogeneous effects of such an event, Haimes explicitly avoids the spatial and sector smoothing that is implicit in some analyses of risk, and draws attention to the varied and localized nature of the economy's vulnerabilities. In this particular case, it was determined that the major impacts sustained by some sectors would nevertheless have a minor effect on the economy per se, and so would not lead to systemic problems.
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with major, potentially regime-shifting consequences -- and in the more common risks, which dominate the expected value. Haimes explained how he uses the partitioned multiobjective risk method (PMRM)
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Each of the policy options A through D has an associated cost for risk management and a corresponding loss of functionality. For instance, option A consists of investing significant resources in risk management in order to reduce the likelihood of extreme events.
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Of course, the technologies for recognizing the incipient problem and tailoring a solution are far from obvious. Engineered systems such as the electric power grid or a telecommunications network often include advanced control systems that enable recovery.
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Creating such a control capability for the electric grid requires a mixture of tools from dynamical systems, statistical physics, and information and communication science, as well as research to reduce the computational complexity of the algorithms so they can be scaled up to the large size of the system being controlled. The electric grid poses a multiscale challenge: troublesome signals must be detected within milliseconds, with certain compensatory actions taken automatically; some load balancing and frequency control on the grid is handled on a timescale of seconds; and control functions such as load forecasting and management or generation scheduling take place on a timescale of hours or days. Identifying at the atomic level what is amiss in a system and then responding on a macro-scale requires multiresolution modeling in both space and time.
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Another key insight relayed by Amin was drawn from the analysis of forest fires. Researchers in one of the six EPRI-funded consortia found these fires to have "failure-cascade" behavior similar to that of electric power grids.
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From page 46... ...
If the barrier does not function properly or is an insufficient impediment, the failure bypasses it and continues cascading across the system. These findings suggest risk management approaches in which the natural barriers in power grids may be made more robust by simple design changes, or in which small failures might be contained by active smokejumper-like controllers before the failures grow into large problems.
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From page 47... ...
First, he noted that the consequences of events in the financial sector are likely nonlinear. Therefore, in designing and enforcing laws and regulations, the goal should not be to minimize the probability of every adverse event, but to guard against those that have more severe consequences: In other words, the risk probabilities have to be weighted by some measure of the welfare gain that would arise from the prevention of each serious adverse event.
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From page 48... ...
Levin observed that, in contrast to management of the electric power grid, there are only coarse or indirect options for control of the financial system. The tools available to policymakers -- such as those used by central banks -- are designed to modify individual incentives and individual behaviors in ways that will support the collective good.
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From page 49... ...
Since no one can really capture all of the relevant perspectives, systemic risks must be assessed through consultations with multiple players, which ultimately converge on a picture of the most important risks. David Levermore of the University of Maryland pointed out that large-scale, complex simulations as exemplified by the work of Haimes and Amin are only part of the process of analyzing systemic risk.
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Simple models can provide considerable insight and also produce very useful predictions. The ultimate test of an assumption is its predictive power.
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But the overall model behavior was essentially simple logistic growth: much of the apparent complexity did not add real insight. While the ecosystem models provide a note of caution, it is nevertheless the case that complex models can be built well.
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To model the electric grid, for example, researchers have parametrized some of the component models so as to provide input to the next level of modeling, using Bayesian analysis. Sensitivity analysis is used to validate the resulting models.
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These models help us understand how individual behaviors become synchronized or integrated with one another and how they spread through the financial sector. Of course, there are many unknowns about these rules, and the gamesmanship and proactive moves probably figure more importantly in the financial sector than in ecology or engineered systems.
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From page 54... ...
1984. "The Partitioned Multiobjective Risk Method." Large Scale Systems 6, no.
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