Many people, astronomers included, dismiss the need to observe newly-discovered NEOs for follow-up astrometry because the probability of a collision with the Earth is relatively low. Even though the probability of dying from an NEO impact has been calculated to be greater than the probability of death from a venomous bite, a tornado, or a lightning strike, and approximately that of dying in an airliner crash, we spend billions on the Federal Aviation Administration for airline safety and only a small fraction of that, about $4 million per year, on NEO surveys, with hardly any of that for NEO follow-up.
The problem is that most people, astronomers included, do not understand how to perform risk analysis properly. They confuse probability with risk. Most business schools teach this distinction and how to analyze and manage risk, at least at the introductory level, so that business executives can deal with risk analysis and risk management on a daily basis. They must recognize risks in their businesses, then decide whether to purchase insurance to mitigate the risk or to pursue some other strategy.
In basic terms, probability is simply the likelihood of the occurrence of an event. For an example, if you roll two dice 100 times, how many times will you roll a 7? Assuming some distribution of events, this can be computed in terms of a mean and standard deviation and a probability curve can be plotted. Along these same lines, the probability of a widespread fire in Southern California can be computed. However, that is only half the story.
Someone in the insurance business underwriting homes or businesses with fire insurance wants to know what risk they are assuming when they write an insurance policy, so they know what premium to charge their customers. That is, they need to know both what is the probability that an event will occur, and in addition, what the consequences are when that event occurs. When astronomers and others dismiss NEOs as low-probability events, they are failing to assess the consequences of NEO impacts, that is, they are not properly assessing the overall risk.
Much of the material in this section is quoted from or paraphrased from a risk analysis overview tutorial at URL http://www.solver.com/risk-analysis/#Quantitative_Risk_Analysis. Risk analysis is the systematic study of uncertainties and risks encountered in business, engineering, public policy, and other areas. Risk analysts seek to identify the risks faced by an institution or business unit, understand how and when they arise, and estimate the impact (financial or otherwise) of adverse outcomes. Risk managers start with risk analysis, then seek to take actions that will mitigate or hedge these risks.
Some institutions, such as banks, investment management firms, and insurance companies, are in the business of taking risks every day. Risk analysis and management is clearly crucial for these institutions. For these firms, the role of risk management is to quantify the risk involved in each investment, trade, or other business activity and to allocate a risk budget across all such activities. In particular, banks are required by their regulators to identify and quantify their risks, often computing measures such as Value at Risk, and ensure they have adequate capital to maintain solvency should the worst outcomes occur.
Quantitative risk analysis is the practice of creating a mathematical model of a project or process that explicitly includes uncertain parameters that we cannot control, and decision variables or parameters that we can control. A quantitative risk model calculates the impact of the uncertain parameters and the decisions we make on outcomes that we care about – such as profit and loss, investment returns, environmental consequences, and the like. Such a model can help business decision makers and public policy makers understand the impact of uncertainty and the consequences of different decisions. Note that such models include both the probability of an event’s occurrence and the cost or consequences of the event. If low probability events have a high cost or severe consequences, such models suggest considerable attention and aggressive action is appropriate.
Risk analysis depends heavily on modeling and simulation to avoid the expense and time of performing an experiment in the real world. Computer models permit one to introduce uncertainty into the experiment using devices such as coin tosses, dice rolls, or a simulated roulette wheel. By using multiple trials, one can collect statistics on the results that yield a significant result.
Another useful tool is Monte Carlo simulation, in which a model is run using randomly selected inputs to compute outcomes of interest. As stated previously, such models account for both the probability of an event’s occurring and the cost or destruction caused by the event, as well as the decisions made by humans to mitigate the event.
In assessing the overall risk of an NEO impact, one needs to know the probability of death from an NEO. This has been assessed differently at different times. Some time ago, Vernon Walters, former director of flight operations at NASA's Goddard Space Flight Center, assessed the risk of death from NEO's at 1 in 20,000 during one's lifetime. More recently, the probability has been pegged closer to 1 in 70,000 according Dr. Alan Harris.
This probability of death from an impact of any size NEO can be compared to the probability of death from another low-probability event, such as death from an airliner crash. The U.S. National Safety Council (NSC) estimates the probability of death from an airliner crash at 1 chance in 5000, but the U.S. National Transportation Safety Board (NTSB) places the same probability at 1 chance in 30,000. It is assumed that the NSC value is for frequent flyers, while the NTSB is for the average American.
A PDF copy of a paper by Christian Gritzner and Stefanos Fasoulas (both of the Dresden University of Technology Institute for Aerospace Engineering), two specialists in risk analysis in the aerospace industry, may be donwloaded by clicking here. They classify NEO impacts as a case of a “high consequence / low probability” event. The authors argue that such events pose a “moderate” risk that are not being adequately addressed by modern society, in terms of the resources devoted to them in comparison to other moderate risks to life and property.
Last modified: January 3, 2008.