Sometimes we have already gone in the forest, and we can hear howling, and we have no idea what is going on, and we are posed with a choice of things to do, but no option is safe. We are stuck between a rock and a hard place. Under such deep uncertainty, how can we act if we refuse to reduce the complexity of the world? We can’t just play it safe, because every option fails a precautionary principle. What do we do in such cases?
I recognize that your questions may be rhetorical, but here are some answers:
1. prioritize, by type of harm, the harms to avoid. The classic approach to understanding harm is to rank death as the greatest harm, with disease and other harms less harmful than death. I don’t agree with this but that’s not relevant. Some explicit ranking of harms to avoid clarifies costs associated with different actions.
NOTE: The story of climate change is one of rich countries making most of the anthropogenic GHG’s, damaging ecosystems more, threatening carbon sinks more, etc. Proactive actions can avoid more extreme harms but have known and disliked consequences, particularly for the wealthier of two compromising to save both (for example, societies, countries, or interest groups).
2. recognize the root causes. If you cannot play it safe, then harms will occur no matter what. In that case, recognize root causes of your quandary so that civilization has an opportunity to not repeat the mistake that got you where you are. In the case of climate change, I perceive a root cause shows in the simple equation impacts = population * per capita consumption. You can get fancy with rates or renewable resources or pollution sinks, but basically: consume less or shrink the population.
TIP: The problem reduces to the population size of developed countries offering plentiful public goods while allowing citizens to accumulate private goods. I’ve seen the suggestion to increase public goods and reduce private consumption. Another idea is to offer consistent family planning emphasizing women’s health and economic opportunities as well as free birth control for all, such as free condoms and free vasectomies for men.
3. find the neglected differences between actual, believed, and claimed assertions. As the situation is evolving into an existential crisis, differences appear between public claims, believed information, and the actual truth. During the crisis, the difference between beliefs and the truth gets less attention. Truth-seeking is ignored or assumed complete. You can buck that trend.
EXAMPLE: Right now, the difference to correct could be between claims and beliefs (for example, politicians lying about climate change), but another difference that is more neglected is between truths and beliefs about the lifestyle implications of successfully mitigating climate change. That is where we are now, I believe. People in the developed world are afraid that mitigating climate change for the global population will wreck their modern lifestyle. In many cases, I suspect those fears are overblown.
CAUTION: In a future of real extremes, involving the plausible loss of 100′s of millions of lives, don’t (claim to) expect that obvious solutions like “let 100 million climate migrants into the US over 5 years” will be easily accepted. Instead, expect the gap between claims and beliefs to widen as hidden agendas are acted upon. Climate change issues of rights, fairness, justice, and ethics, not just economics or technology, have been consistently neglected. The endgame looks to be a harmful one.
4. close information gaps wherever you can: Earth science can be confusing. You can follow most of a discussion easily but then lose understanding at some key point because the researcher is being a geek and doesn’t know how to communicate their complicated information well. Sometimes there’s no way to make the presentation any simpler. Sometimes, there isn’t enough information or the information is aged out but not updated fast enough. Policy guidance appears to stick longer than real-time measurements of earth system changes allow. This is a point of frustration and a policy bottleneck that actually comes from the research side. Examples of such issues include:
physical modelling parameters of tipping elements (for example, Greenland melt) are missing from widely cited computer models predicting climate change impacts (for example, sea-level rise). The implications of measurement data wrt those tipping elements goes missing from policy recommendations based on the computer models.
loss of carbon sinks that are tipping elements are not factored into carbon budget calculations at rates reflective of current and short-term expected changes to those sinks. Neither are other forcings on tipping elements (for example, people clearing the Amazon for farming).
smaller scale features relevant to ocean current modeling or weather changes due to climate. These require a model “grid size” of about 1km in contrast to 100x larger grid sizes used for modeling climate. Or thereabouts, according to one discussion I followed. The gist for me that modeling climate change in the ocean or as it affects weather in real-time is not happening effectively yet.
correct interpretation of statistics, units, terminology or research purpose prevents confusion about limits, measurements, and tracking of changes in atmospheric heating, tipping element significance, and the significance of concepts like global average surface temperature (GAST). There are many examples, some of which baffled me, including:
the relationship between gigatons and petagrams
the difference between CO2 and CO2e
amounts referring to carbon (C) vs carbon dioxide (CO2)
the relationship between GAST increases and regional temperature increases
the difference between climate and weather
the rate of warming of the Arctic
the relationship between heating impact and decay rate of CH4 (methane)
the % contribution of land vs ocean carbon sinks to total carbon uptake
the hysteresis effect in tipping element models
the relationship between tipping elements, tipping points, and abrupt climate change.
the precise definition of “famine” and “drought”
the nature of BECCS and DACCS solutions at this point in time
the intended meaning of “carbon budget” versus its commonly understood meaning of “carbon that is safe to produce”
the pragmatic meanings of “energy conservation” or “natural resources” or “carbon pollution”
the relationship between SDGs, SSPs, RCPs, SPAs, CMIP5 and 6 models, and radiative forcing (still confusing me)
Here’s a thought about the use of the word “ontology”. I actually chose that word myself for a criticism I submitted to the Red Team Contest this year. I think no one has read it. However, I suspect that its use by you, someone who gets noticed, could put EA’s off, since it is rarely used outside discussion of knowledge representation or philosophy. That said, I agree with your use of it. However, if you have doubts, other choices of words or phrases with similar meaning to “ontology” include:
model of the world
beliefs about the world
idea of reality
worldview
reality (as you understand it)
In a revision of my criticism (still in process), I introduce a table of alternatives:
EA terminology
probabilism-avoiding alternative
example of use
possible
plausible
it is plausible that the planet will reach 6C GAST by 2100.
impossible
implausible or false
It is false that the planet reached −1C GAST in 2020.
likely or probable
expected … if/assuming …
It is expected that solar power development will add to global energy production, rather than substitute for existing production, assuming business as usual continues.
unlikely
not expected
It is not expected that countries will stay under their carbon budgets between now and 2030.
risk
danger
The existential danger from climate destruction is apparently still controversial.
uncertain
unknown
The danger of human extinction from the proximate cause of climate change is unknown.
uncertainty
ignorance
At the moment, there is some unavoidable ignorance reflected by inferences of changes in weather from changes in climate.
chance that
opportunity for
There is an opportunity for the faith of techno-optimists to be vindicated.
usually
typically
Typically the corrupt politicians and selfish business barons work against the common good.
update
change or constrain
I constrained my beliefs about Greenland resilience against melting after learning that Greenland altitude losses could turn its snow to rain.
**EDIT:**Sorry I cannot get this table to render well
I’m not recommending those changes to your vocabulary, since you are dealing with foresight and forecasting while juggling models from Ord, Halstead, and other EAs. However, if you do intend to “take a break” from thinking probabilistically, consider some of the alternatives I offered here. It can also be helpful to make these changes when your audience needs to discuss scenarios as opposed to forecasts.
I have not spent much time studying geo-engineering, but I have formed the impression that climate scientists look at polar use of water vapor for marine cloud brightening with less fear than the use of aerosols like diamond dust elsewhere in the world. EDIT:Apparently Marine Cloud Brightening is a local effort with much shorter residence time, giving more time for gathering feedback, whereas aerosol dusts are generally longer-term and potentially global.
Also I recall a paint that is such a brilliant white that its reflectivity should match that of clean snow. If the world’s roofs were painted with that paint, could that cool the planet through the albedo effect, or would the cooling effect remain local? I need some clarity on the albedo effect, but I’ll leave the math to you for the moment, and best of success with your efforts!
Hi Gideon,
I recognize that your questions may be rhetorical, but here are some answers:
1. prioritize, by type of harm, the harms to avoid. The classic approach to understanding harm is to rank death as the greatest harm, with disease and other harms less harmful than death. I don’t agree with this but that’s not relevant. Some explicit ranking of harms to avoid clarifies costs associated with different actions.
NOTE: The story of climate change is one of rich countries making most of the anthropogenic GHG’s, damaging ecosystems more, threatening carbon sinks more, etc. Proactive actions can avoid more extreme harms but have known and disliked consequences, particularly for the wealthier of two compromising to save both (for example, societies, countries, or interest groups).
2. recognize the root causes. If you cannot play it safe, then harms will occur no matter what. In that case, recognize root causes of your quandary so that civilization has an opportunity to not repeat the mistake that got you where you are. In the case of climate change, I perceive a root cause shows in the simple equation impacts = population * per capita consumption. You can get fancy with rates or renewable resources or pollution sinks, but basically: consume less or shrink the population.
TIP: The problem reduces to the population size of developed countries offering plentiful public goods while allowing citizens to accumulate private goods. I’ve seen the suggestion to increase public goods and reduce private consumption. Another idea is to offer consistent family planning emphasizing women’s health and economic opportunities as well as free birth control for all, such as free condoms and free vasectomies for men.
3. find the neglected differences between actual, believed, and claimed assertions. As the situation is evolving into an existential crisis, differences appear between public claims, believed information, and the actual truth. During the crisis, the difference between beliefs and the truth gets less attention. Truth-seeking is ignored or assumed complete. You can buck that trend.
EXAMPLE: Right now, the difference to correct could be between claims and beliefs (for example, politicians lying about climate change), but another difference that is more neglected is between truths and beliefs about the lifestyle implications of successfully mitigating climate change. That is where we are now, I believe. People in the developed world are afraid that mitigating climate change for the global population will wreck their modern lifestyle. In many cases, I suspect those fears are overblown.
CAUTION: In a future of real extremes, involving the plausible loss of 100′s of millions of lives, don’t (claim to) expect that obvious solutions like “let 100 million climate migrants into the US over 5 years” will be easily accepted. Instead, expect the gap between claims and beliefs to widen as hidden agendas are acted upon. Climate change issues of rights, fairness, justice, and ethics, not just economics or technology, have been consistently neglected. The endgame looks to be a harmful one.
4. close information gaps wherever you can: Earth science can be confusing. You can follow most of a discussion easily but then lose understanding at some key point because the researcher is being a geek and doesn’t know how to communicate their complicated information well. Sometimes there’s no way to make the presentation any simpler. Sometimes, there isn’t enough information or the information is aged out but not updated fast enough. Policy guidance appears to stick longer than real-time measurements of earth system changes allow. This is a point of frustration and a policy bottleneck that actually comes from the research side. Examples of such issues include:
physical modelling parameters of tipping elements (for example, Greenland melt) are missing from widely cited computer models predicting climate change impacts (for example, sea-level rise). The implications of measurement data wrt those tipping elements goes missing from policy recommendations based on the computer models.
loss of carbon sinks that are tipping elements are not factored into carbon budget calculations at rates reflective of current and short-term expected changes to those sinks. Neither are other forcings on tipping elements (for example, people clearing the Amazon for farming).
smaller scale features relevant to ocean current modeling or weather changes due to climate. These require a model “grid size” of about 1km in contrast to 100x larger grid sizes used for modeling climate. Or thereabouts, according to one discussion I followed. The gist for me that modeling climate change in the ocean or as it affects weather in real-time is not happening effectively yet.
correct interpretation of statistics, units, terminology or research purpose prevents confusion about limits, measurements, and tracking of changes in atmospheric heating, tipping element significance, and the significance of concepts like global average surface temperature (GAST). There are many examples, some of which baffled me, including:
the relationship between gigatons and petagrams
the difference between CO2 and CO2e
amounts referring to carbon (C) vs carbon dioxide (CO2)
the relationship between GAST increases and regional temperature increases
the difference between climate and weather
the rate of warming of the Arctic
the relationship between heating impact and decay rate of CH4 (methane)
the % contribution of land vs ocean carbon sinks to total carbon uptake
the hysteresis effect in tipping element models
the relationship between tipping elements, tipping points, and abrupt climate change.
the precise definition of “famine” and “drought”
the nature of BECCS and DACCS solutions at this point in time
the intended meaning of “carbon budget” versus its commonly understood meaning of “carbon that is safe to produce”
the pragmatic meanings of “energy conservation” or “natural resources” or “carbon pollution”
the relationship between SDGs, SSPs, RCPs, SPAs, CMIP5 and 6 models, and radiative forcing (still confusing me)
Here’s a thought about the use of the word “ontology”. I actually chose that word myself for a criticism I submitted to the Red Team Contest this year. I think no one has read it. However, I suspect that its use by you, someone who gets noticed, could put EA’s off, since it is rarely used outside discussion of knowledge representation or philosophy. That said, I agree with your use of it. However, if you have doubts, other choices of words or phrases with similar meaning to “ontology” include:
model of the world
beliefs about the world
idea of reality
worldview
reality (as you understand it)
In a revision of my criticism (still in process), I introduce a table of alternatives:
**EDIT:**Sorry I cannot get this table to render well
I’m not recommending those changes to your vocabulary, since you are dealing with foresight and forecasting while juggling models from Ord, Halstead, and other EAs. However, if you do intend to “take a break” from thinking probabilistically, consider some of the alternatives I offered here. It can also be helpful to make these changes when your audience needs to discuss scenarios as opposed to forecasts.
I have not spent much time studying geo-engineering, but I have formed the impression that climate scientists look at polar use of water vapor for marine cloud brightening with less fear than the use of aerosols like diamond dust elsewhere in the world. EDIT:Apparently Marine Cloud Brightening is a local effort with much shorter residence time, giving more time for gathering feedback, whereas aerosol dusts are generally longer-term and potentially global.
Also I recall a paint that is such a brilliant white that its reflectivity should match that of clean snow. If the world’s roofs were painted with that paint, could that cool the planet through the albedo effect, or would the cooling effect remain local? I need some clarity on the albedo effect, but I’ll leave the math to you for the moment, and best of success with your efforts!