Ethics, Science and Projection of WAIS Collapse
Some believe that general discussions about the overall uncertainties associated with abrupt sea level rise, SLR, projections are a waste of time and that the only path forward is to allow the "deductive" process-based approach (characterized by the IPCC process) to continue working only with extant model projections (and their associated model spreads), and then to develop isolated-incremental improvements to the nature and/or input to those existing models. If you are one of these individuals then you may not be interested in reading any further, as the following focuses on ways to make better use of "induction" (together with "deduction") for helping decision makers to deal with the inherent fat-tailed risks of the potential the West Antarctic Ice Sheet, WAIS.
The philosopher C. D. Broad once said that: "Induction is the glory of science and the scandal of philosophy." One interpretation of this statement is that induction only works well when one is honestly seeking the truth, and does not work well when bias/deceit is not actively rooted out of the process; and note that the "Scientific Method" inherently makes extensive use of induction, as indicated in the attached image. However, neither deductive, nor inductive, reasoning is infallible since in inductive reasoning premises of an argument may support a conclusion, but does not necessarily ensure it; and, in deductive reasoning an argument is dependent on the truth of its premises. That is, a false premise may lead to a false result and an inconclusive premise will yield an inconclusive conclusion.
The basic problem with using either deductive, or inductive, reasoning when examining the potential collapse of the WAIS, is the high uncertainty when it comes to rare events, as there are limited past samples and such cases therefore require strong extrapolating theories; accordingly events depend more and more on theories when their probability is small. In the case of the potential collapse of the WAIS, knowledge is both uncertain and the consequences are large, thus requiring more robust policies founded on solid research focused on the fat-tail of the collapse PDF.
Taleb argues that the proposition "we know", in many cases, is an illusion, albeit a necessary one; the human mind tends to think it knows, but it does not always have a solid basis for this delusion of "I know". Similarly, to those who might argue that the advancement of science has rendered the world well-known, Taleb argues that while science added knowledge, we always run the risk of experiencing the improbable, rare, and novel. We can be shocked by this knowledge/experience or we can be open to it. As with the dictum of Socrates, "the only thing I know is that I do not know", is as true as ever. Of course both the natural human physiological propensity and the cultural phenomenon are somewhat a necessary precondition to learning, since complete openness to every event would be inefficient. Bertrand Russell observed, "An open mind is an empty mind." So we cannot be completely open, but we must guard against being completely closed as well. It would be most efficacious if we could find a balance between the known and unknown and for the limits/uncertainties of our knowledge and experience.
In order to find such a balance:
(1) We need to fill our models (mental or numerical) with good experience/data, and actively weed-out misleading/deceptive input. This includes making better input forcing functions for WAIS ice mass loss (e.g. note that Rignot et al 2014 merely linearly projected the forcing from the past decade (or so) into the future without even trying to quantify future: atmospheric, oceanic, geothermal, etc. forcing's), and better modeling of uncertain boundary conditions for WAIS models (e.g. note that Pfeffer et al 2008's assumed kinematic constraints on the WAIS have been found to be incorrect and too limiting).
(2) We need to continuously update, refine and strengthen our models/theories. We need to use a "Big Data" approach to cross disciplines (including: glacio, paleo, bio, geo, etc) in order to better identify likelihood of collapse scenarios for our models (note Rignot is currently working on just such a "Big Data" approach).
(3) We need to look for insightful, and ethical, solutions from our models/theories, that can help society at large by seeking to minimize systemic suffering. Currently, neither scientists, nor economists, are trained to prepare their information/outputs in formats conducive for decision makers to focus on the "greater good".
Unfortunately, process-based SLR projections are crafted for decision makers that are part of the current global capitalist-style economic system which is based on a mindset of survival of the fittest, SF, rather than true natural selection, NS. The distinction between SF and NS may not be apparent to many, but Darwin believed that natural selection resulted in mankind's better qualities such as empathy and cooperation; much as mathematician John Nash demonstrated that optimal systemic solutions must evaluate group cooperation rather than only individual benefit. In a SF capitalist-style world economy captains of industry think of themselves as warriors and study works such as Sun Tzu's "The Art of War", and follow advise such as: "All warfare is based on deception", which they translate into all competitive business practices are based on deception, with the goal of establishing repetitive cycles of temporary monopoly power and increasingly of temporary Ponzi schemes. In such a non-cooperative socio-economic system uncertainty is used as a device to provide "plausible deniability" for those who want to maintain their monopoly power and/or Ponzi schemes; thus never achieving an optimized Nash/Darwin – type sustainable solution that considers the good of the whole group in order to provide a balanced approach to the Tyranny of the Commons, TC, issues rife in climate change challenges, including the potential collapse of the WAIS.
NS has typically taken millennia to select cooperative biological systems to handle TC issues; while mankind has evolved elaborate mental and social constructs that can be used to reduce the time required to select more optimized sustainable solutions to TC problems, provided these mental and social constructs do not use uncertainty to provide plausible deniability/deception to the dis-benefit of the whole. However, built into the very nature of our mental constructs/models is the human propensity to extend existing knowledge and experience to future events and experiences; which is an inherently hazardous situation for evaluating the non-stationary conditions of the WAIS. To exacerbate this natural propensity much of our cultural education both formal and otherwise is built upon historical knowledge directed by others. Better incorporation of the likely effects of unexpected events (such as the collapse of the WAIS) in to our mental/numerical constructs is fundamental to finding a balanced approach to addressing the common good. Thus, addressing the rare and unexpected is far more significant to our formation of knowledge than people often imagine, or incorporate into our decision making processes (note that the AR5 report explicitly [in writing] excluded the potential collapse of the WAIS this century from their report to policy makers). Currently, too many decision makers seek to "keep their eye on the ball" by chopping-off the long-tail of PDFs; which may be a practicable approach for a "thin-tailed" PDF; but can be a disastrous approach for a "fat-tailed" PDF, such as that associated with the consequences of the collapse of the WAIS this century.
While it may seem esoteric, "Quantum Contextuality", QC, means that the measurement result of a quantum observable depends on the physical arrangement prepared to measure it, and recent research indicates that addressing QC is critical to gaining the full benefits of future quantum computing. Furthermore, we all need to realize that our perception of reality is a result of our continuous chain of QC measurements of the world's non-stationary quantum function. So in this sense if we make QC measurements with a SF mindset then we will see SF observables, while if we make QC measurements with a NS mindset then we see NS observables of reality. So to quote Sun Tzu (the Art of War) again: "If you know the enemy and know yourself, you need not fear the result of a hundred battles. If you know yourself but not the enemy, for every victory gained you will also suffer a defeat. If you know neither the enemy nor yourself, you will succumb in every battle.”
In this sense in order to make better use of inductive reasoning, we need to treat fat-tailed uncertainty as the enemy otherwise we will fall victim to Pogo's (aka cartoonist Walt Kelly) truism that: "We have met the enemy and he is us"; as we will use our mental constructs and numerical models to get SF answers rather than NS answers.
To many these statements will seem like hokum, and for these people this is hokum; but for others it can provide better insights on how best to use inductive tools like: "Robust Decision Making", "Bottom up" adaptive planning, and "Big Data" analyses.