In California today, thinking about the high impact consequences of temperature increases, disappearing snowpack, and sea level rise could paralyze us; and that’s not the only unknown we’re facing. The impacts of seismic events on our imported water systems have both water supply and water quality consequences that are potential game stoppers; even the unknown timing of implementation of the BDCP and its ultimate costs represent enormous uncertainties (and that’s based on the assumption it proceeds). These severe uncertainties converge to make the definition and understanding of our information gaps (what we don’t know) more pressing than they have ever been.
In situations of extreme uncertainty, effective decision-making is fundamentally different from those cases where our future needs and objectives are known, our choices will produce predictable outcomes, and the likelihood of success is based on a statistical record sufficient to provide us with accurate estimates of probability — what might be defined as a deterministic world. Almost 15 years ago, Michael Schwarz described the characteristics of “extreme uncertainty” in these terms:
There are no stationary trends, no data points close to the relevant values of a variable and no theory to guide the forecast . . . an environment approximating an information vacuum. (Schwarz, 1999)
When it comes to planning, designing, and delivering traditional, large-scale water management infrastructure, we are often making decisions in “an environment approximating an information vacuum.”
This isn’t to say decisions can’t be made under these circumstances, only that unsatisfactory answers are likely to result from an overly deterministic view of the current state of knowledge and our ability to forecast future conditions — especially when it comes to the weather. This is well articulated in a very readable paper by the Society of Actuaries on decision-making under uncertain and risky situations.
Most people often make choices out of habit or tradition, without going through the decision-making process steps systematically. Decisions may be made under social pressure or time constraints that interfere with a careful consideration of the options and consequences.
Many of the decisions made regarding how we will meet our future water management needs are based almost entirely on both habit and tradition, often driven by both social and political pressure.
There are other approaches. Israeli professor Yakov Ben-Haim, in his book Info-Gap Decision Theory: Decisions Under Severe Uncertainty, offers an innovative approach that works without any reliance on probabilities. He describes the fundamental difference between classical statistical methods and his analytical techniques. Info-Gap theory is built around quantifying the extent and potential consequences of our ignorance regarding future events, rather than assigning probabilities to future events about which we know very little or nothing. To quote Ben-Haim:
The place to start our investigation of the difference between probability and info-gap uncertainty is with the question: can ignorance by modeled probabilistically? The answer is ‘no’. The ignorance which is important to the decision maker is a disparity between [what] is known and what needs to be known in order to make a responsible decision; ignorance is an [information] gap.
Ben-Haim goes on to define the “robustness” and “opportuneness” of decisions using an analytical approach assessing a decision’s level of “immunity” to both pernicious (bad) and propitious (good) outcomes based on the quantification of what we know and what we don’t know – never resorting to the ubiquitous assigning of probabilities to outcomes the underpins most multi-objective decisions.
Whatever other considerations may be pertinent, we know that sources of supply from ocean desalination and recycled water are not affected by extremely uncertain future hydrology; just as we know that a major seismic event will do significant damage to Delta levees and impact water quality sometime in the future – even though we cannot predict when it will occur. With this (and other) knowledge, we can make decisions that do not rely on assumed probability distributions regarding future conditions that are largely unknown.
Accepting our inability to probabilistically predict the future does not mean we must accept a passive or reticent approach to taking planned and proactive action. Doing nothing maybe the worst decision we can make in the context of such extreme change. There are other ways – and info-gap decision theory is one of them.
These questions should push us beyond the tools and materials in front of us, the proverbial tried-and-true approaches, towards examining fundamental ends, purposes and context. It’s a systems approach, a “whole water” approach that looks at the bigger picture and searches for more effective responses based on incremental changes, feedback, and adaptation. It employs new analytical tools like Ben-Haim’s info-gap decision theories and combines them with scenario planning, systems modeling and simulation, as well as classical methods to help us make robust decisions and increase our resilience to future surprises. “Keeping mistakes small and learning constant,” the saying goes.
Whatever we do in this new world of severe uncertainty, we are probably better off with solutions that are diversified, multi-purpose, smaller-scale, context sensitive, flexible, resilient and have low regret if they don’t perform as expected.
After 20 years of increasing our capacity to undertake integrated water resources planning using statistically based portfolio models taken from the power industry and the financial sector, I believe that we are at a point where it’s essential to re-evaluate our planning methodologies and tools to ensure that they are appropriate in a world of rapidly increasing vulnerability and uncertainty. Our historic confidence in the ability to predict future hydrology, future demands, and the useful life of facilities may be wholly unjustified in the world we live in. As Albert Einstein is credited with stating:
We cannot solve our problems with the same level of thinking that created them.
It is high time that we explore, discover, create, and invent new planning frameworks and tools that can help decision-makers manage the world we are headed towards – and be willing to let go of our overly deterministic problem-solving tools.
In 1989, Alan Kay (who was then an Apple Fellow) made an often quoted pronouncement that the “The best way to predict the future is to invent it.” But in that same address, Kay also commented:
In some sense our ability to open the future will depend not on how well we learn anymore — but how well we are able to unlearn.
Let’s be honest with ourselves regarding what we do know, what we don’t know, and what we could know in making decisions about future investments, and to be courageous enough to develop better approaches and tools for decision-making in severe uncertainty. This is not the time to be gambling on events where we don’t know the odds, and we don’t know the payout.
Photo credit: FFCUL, 2012