Led by Robert Waller, PhD
Robert Waller currently wears several hats as the President and Senior Risk Analyst of Protect Heritage Corporation based in Ottawa, and his impressive career also includes working as a conservation administrator for the Canadian Museum of Nature as well as an author of numerous conservation publications. He taught this one-day workshop on risk assessment methodology with a sense of humor as well as a sense of purpose, and left me wanting to learn more. He kept us actively engaged and learning from one another. And his thorough handout gave us lots more information to clarify the concepts that we practiced working with during the day.
We started off introducing each other and doing a little group bonding at each table, which led to friendly competition for tantalizing prizes equitably awarded by our instructor. Our whole group was marvelously multi-lingual, multi-cultural, and multi-specialty, opening our minds to the challenges of different locations and types of collections. The bonding time paid off by the end of the day, when we had to work together to produce a real-life risk analysis of exhibits in the convention center, complete with insect infestation and earthquake risks.
One key takeaway for me after one of the mathematical exercises was that we don’t have to get too specific to estimate loss in value. The point is to identify the kind of value that the object is most prized for in the collection at this point in time, not every possible use/value that it could ever have. The spreadsheet would get too long, and we’d get bogged down with fine differences in opinion. Just using a few significant and vaguely measurable values allows us to screen and rank the risks so we can then prioritize the top ten, figure out how much mitigation would cost, and then take concrete, practical steps to get the most bang for the buck.
But first, we had to learn the method, which is both theoretical and practical. Brace yourselves for some math, or better yet, get some caffeine to help you through the next few paragraphs.
The method starts with identifying risks both by “agent of deterioration” (one of ten general causes of risk) and “type of risk” (a combination of frequency and severity). How often a risk might happen is the first significant filter for decision-making, so dividing risks into rare, sporadic, and continual is the place to start. These are then further modified by their severity, so a rare and catastrophic risk becomes type 1, sporadic and severe is type 2, and continual and mild is type 3. According to Bob, we can’t waste time worrying about either a continual/catastrophic risk (we’re all goners), or a mild/rare risk (just a drop in the bucket).
To assess a collection’s risks, we define the most likely agents of deterioration and types of risks, and then envision specific scenarios that illustrate the combination of the two. For example, the agent of deterioration might be pests, the type of risk might be type 2 (sporadic and severe), and the specific risk might be silverfish that enter the collection with a donation, and feast on paper-based library collections resulting in loss of information value. Approximately 50 such specific risks would be defined for a typical, comprehensive collection assessment, and a spreadsheet table created with a line for each risk.
The magnitude of each specific risk is then estimated by determining four ratios (each is given a number between 0 and 1) and multiplying them all together:
- fraction of the collection that is susceptible to each risk (.75 represents ¾ of the collection would be affected)
- loss in value that would occur if the risk occurred (and value is not just monetary…there are many notions of value, and this number is approximate based on minor/major/total loss, with 1 being total loss, .5 being half of the value lost, and .1 being 10% value lost)
- probability that the loss would actually occur within 100 years (for type 1 risks only; all others get a 1 because they will definitely happen within 100 years)
- extent (a concept that is hard for this novice to define, but is combination of the first two values modified by their likelihood of occurring within 100 years given current mitigation efforts, and is applied to type 2 and type 3 risks only).
We assume that each of those values is 1 unless there’s a reason to define it otherwise. And once we’ve calculated the magnitude for each specific risk, we have bottom-line numbers that can help prioritize the specific risks. By multiplying several variables that are <1, decimal places accumulate in the final product, so it becomes easier to see which are the most significant risks. The comparisons become logarithmic. Risks that are closer to 1 are more likely to cause significant loss, whereas risks that are .001 and lower are not such big threats. At the end of it all, if we have two risks with similar values, we use time as a tie-breaker, determining which risk is going to happen sooner and addressing that one first.
During the last exercise, each table was assigned a display window in a series of exhibits about the sister cities of Albuquerque. Our table was assigned Sasebo, Japan, which displayed ceramics on a glass shelf aamong other things. Narrowing down to what we arbitrarily judged to be the most significant risk, we assessed the risk of earthquake damage to the ceramics. Roughly 14 out of 40 objects were ceramics on the glass shelf (fraction susceptible is 14/40 or .35). We judged the loss in value to be .8, since the ceramics would very likely break but could be repaired to regain some of their display value. Probability for earthquakes in the region is estimated at 1 in 400 years, which gave us a ratio of .25 in 100 years. Multiplying .35 x .8 x .25 gave us a bottom line magnitude of risk of .07, which is smaller than we expected. Looking back on it, we might have gotten a higher magnitude of risk if we’d chosen to assess the impact of a dead moth lying on the bottom of the case on the silk kimono hanging above it.
By the end of the day, I came to appreciate what my actuary friend does all day long, and vowed to ask him more about it. Bob did a great job at helping us to put practical numbers onto concepts that previously seemed unmeasurable, and at providing a bottom-line mathematical method that can help us clarify the priorities for mitigating risks to our collections.