1) Introduction to the Systemic Cascading Lens
Systems thinking and complexity science are largely overlooked and unexplored in the EA community – leading some EA thinkers to think highly linear causal chains or heavily simplified models can be used to validate/invalidate claims. Any system of thought or epistemic training that relies on “n-th order effects” may therefore be largely disregarded and not taken seriously in the community.
A common objection within the community goes: I’m uncomfortable with a volatile logical linkchain between A to B to C to D – these interlinkages can be tenuous and unlikely at best. Each additional Fermi estimate makes this whole claim more unlikely. Toby Ord wrote similarly that he preferred narrow over broad interventions because they can be targeted and thus most immediately effective without relying on too many casual steps. This mirrors the epistemic the EA community embraces – one of evidence-based logic and empirical models – which leans heavily on linear, quantifiable, direct effects.
I’d contend that conceptualizing certain complexity risks as a linear linkchain is a flawed visualization, as it only accounts for one possibility and no complexity effects; the correct one is to view our systems and societies as a graph with nodes and edges. A given systemic cascading risk shocks a subset of nodes – causing ripple effects through to other nodes and non-linear butterfly effects that cascade throughout the system. The damage (or recovery) done is subsequently proportional to the resiliency and adaptability of those systems.
My critique is that EA at large has failed to adequately connect complexity effects with longtermist cause area ranking & provide resolute, tractable solutions to such problems. As qualitative experience, development theories, as well as academic analyses show us – sole linear causation is highly unlikely, and overlooking higher-order effects (second-order consequences) have led to a multitude of failed forecasts and policies. Thus, in this post, I hypothesize that it will be effective for the community to consider and map systemic cascading risks, rather than focusing solely on direct existential risk mitigation.
In this forum post, I aim to reintroduce a phrase – systemic cascading risk – with the hope of turning it into common EA parlance, meant to easily articulate and disseminate complexity-based critiques and ideas in a concise, understandable manner. I believe the term systemic cascading risk acts as an applicable empirical framework to a) critique EA cause area prioritization and broader paradigms of thinking through a complexity lens and b) identify tractable ways for the EA community to address these criticisms.
I’ll start by building up the individual pieces – definitions & context for systemic cascading risks, showing how they cascade through sociopolitical and economic systems, the tractablity of targeted hedges against such risks – and end with my fundamental argument: how these risks are relevant to longtermist efforts & affect values lock-in. Afterwards, I have some further thoughts on systemic cascading risk in EA epistemics that are not fully formed but I think lend further credence to this idea and give you more reasons to think through this lens (that you may not be convinced by, but are worth investigation).
*To clarify, I do not intend to discuss systemic cascading GCRs, but rather the broader category of risks of which systemic cascading GCRs may act as a tail-end example of – with the goal of generally critiquing effective altruist cause prioritization and attitudes toward these risks.
2) Defining Systemic Cascading Risk
Cascading risks are characterized by significant n-th order effects that are often difficult to project with high certainty, yet significantly shape the equilibria and dynamics of social, economic, and political systems in potentially volatile manners. The focus throughout this essay will be on cascading risks that can still be forecasted with reasonable probability.
The systemic lens is a triple entendre:
- first, aiming to understand the social, economic, and institutional systems (e.g. supply chains) that compose our society and their vulnerabilities to build them to be more resilient;
- second, a financial risk-derived definition of systematic risk that is inherent to the whole economy, rather than one tied to the performance of an individual actor;
- third, a call to action to undergo meta-level societal changes and question more fundamental economic and social structures in society, rather than assuming those structures to be static and unchangeable.
A systemic cascading risk, therefore, is a trigger in a complex network – causing cascades of unpredictable or undesirable events.
3) Political and Economic Systems & Risks
Cascading Pandemic Risk: A Case Study in COVID
Beyond the estimated 3 million excess deaths attributed to COVID, the economic systemic effects due to COVID – the worst economic downturn since the Great Depression – revealed key vulnerabilities in supply chains as well as gaps in education, employment, housing, agriculture, and healthcare systems. Furthermore, the household economic stresses contributed to political extremism and the undermining of perceived institutional legitimacy.
For instance, global food systems demonstrated a failure mode, with supply chain deficiencies resulting in average global cereal prices increasing 27.3% over a year. This resulted in soaring debt and instabilities that tested fragile states relying on imports – for example, in:
- Tunisia, where an associated 30-50% increase in fertilizer costs and the largest deficit in 40 years drove food prices to levels not witnessed since the Arab Spring, resulting in a new wave of anti-government protests emerging and a Presidential suspension of democratic rule of law was instituted based on a controversial interpretation of Article 80 in the constitution
- Lebanon, where 77% of households said they didn’t have enough food or enough money to buy food
- Bangladesh, where the proportion of rural households facing moderate or severe food insecurity rose from 15% in early 2020 to 45% in Jan. 2021;
- In households across Algeria and Morocco, where the price of soft wheat saw a 22% year-on-year increase
- Globally, a 70% increase in extreme hunger
A sudden loss of income also contributed to political polarization – for instance, in:
- Colombia as the number of people in extreme poverty grew by 3.6 million people, prompting protests and a violent government response
- Spain, where unemployment being pushed to 15% resulted in violent, widespread protests and Vox (far-right political party) was able to capitalize on polarization
- South Africa, where “Zuma riots,” fueled in a large part by economic inequality and distress, resulted in the worst violence in the country since the end of apartheid (354 deaths, 5500 arrests)
While COVID-19’s cascades were met with a recovery through fiscal stimulus through to the end of 2021, global debt levels have reached 320% of global GDP, which could trigger defaults around the world. The K-shaped recovery may also exacerbate existing inequality, contribute to political polarization, and undermine economic resiliency – especially when combined with the aftermath of the Russia-Ukraine war supply chain shock.
Cascading Conflict Risk: A Case Study in the Russia-Ukraine War
This year, the Russian invasion of Ukraine also served as a catalyst for food inflation, revealing multiple vulnerabilities in our global trade systems and demonstrating sector-dependent cascades. Given the magnitude of the shock – Russia and Ukraine together accounted for almost a third of the world's export of wheat & barley and 75% of sunflower oil exports – the World Bank warns of historically high food insecurity and inflation levels through the end of 2024, which has already hit certain import-dependent countries quite hard.
- Egypt (the world’s largest importer of wheat) received around 85% of their wheat imports and 73% of its sunflower oil imports from Russia and Ukraine; the war resulted in 44% increases in wheat prices and 32% increases in sunflower oil prices overnight. Food inflation continues at 24.8%, especially in grains and vegetable oils (June 2022).
- At the onset of the war, the cost of minimum food needs spiked to 351% in Lebanon, 300% in Somalia, 97% in Syria, 81% in Yemen.
- Kenyan prices for staple goods have risen (e.g. maize flour rose 15.3 percent) as locust infestation, climate change, inflation, and the Russia-Ukraine war coincide.
- West African inflation rates include 30% in Ghana, 22.4% in Sierra Leone, 18.6% in Nigeria, and 15.3% in Burkina Faso. The number of hungry people there has quadrupled since 2019.
- Moroccan poverty is projected to increase by 1.1-1.7% due to inflationary effects.
Certain government responses, aimed at ensuring domestic food stability, have worsened the crisis. Because of an international increase in oil prices, Indonesia implemented a temporary palm oil export ban, followed by a Malaysian export ban on chicken in June. A heatwave subsequently hit Indian crop yields, resulting in an Indian wheat export ban. Argentina, at a historic 69.5% inflation rate, restricted placed caps on exports of corn and wheat.
This coincided with rising gas prices resulting in widespread EU fertilizer plant shutdowns, further threatening food stability. As of June, global maize and wheat prices were 42% and 60% higher, respectively, compared with January of last year.
Under pressure from systemic issues and cascading food prices since the beginning of the COVID pandemic, Sri Lanka faced its worst economic crisis since its founding and depleted foreign currency reserves, leading to a historic debt default. The 2022 Sri Lankan protests continue on – as of the time of this article’s publishing, all 26 members of the cabinet have resigned, protestors occupied the President’s house and sacked the Temple Trees (PM’s residence), and President Gotabaya Rajapaksa was ousted. The new government has reacted by declaring a state of emergency, imposing a curfew, restricting social media access, and violently repressing protests.
Pandemics and conflicts have the potential to cause great cascading socioeconomic effects, and it is unclear how the world would react to a similar systemic cascading risk – either in the form of another conflict/pandemic risk, or in the form of climate change’s short-term effects through to 2050.
Cascading Environmental Risk: A Case Study in Climate Change, through to 2050
Let’s observe climate change’s cascades on society.
A subset of the projected direct sociopolitical & economic impacts of climate change – within the next 30 years – are:
- Refugees: ~216 million climate refugees by 2050 (World Bank Groundswell Report) caused by droughts and desertification, sea-level rise, coastal flooding, heat stress, land loss, and disruptions to natural rainfall patterns
- Water crises: at least ~5 billion people total living in moderate water stressed areas & up to ~3 billion people under conditions where water requirements exceed managed surface water supply by 2050 (MIT IGSM-WRS)
- Sea level rise displacement: Sea level rise displacing ~150 million by 2050 (CoastalDEM)
- Food supply chain disruptions: Significant reduction of yields of essential crops in Africa and the Middle East
- Volatility in food yields – e.g. by 2030, maize crop yields may decline ~24% while wheat yield may increase ~17% (NASA).
- There will likely be enough aggregate food in theory, taking into account technological progress; it is a matter of whether our agriculture and trade systems will be able to adapt and deliver food to where it is required.
- A potential multi-breadbasket failure still poses significant risk to food stability internationally.
- Inflation: Increases in food, water, and real estate prices; supply chain disruptions due to natural disasters
- Poverty: ~130 million additional people by 2030 (World Bank)
These act as the relevant, first-order, modelable stressors – ones that are notably much larger than COVID or the Russia-Ukraine War. As a systemic cascading phenomenon, climate change is a butterfly effect that shocks a subset of nodes in our societal graph.
This can potentially cascade into increased political extremism, violent civil conflict, and social tension, as previous literature has established. Weather volatility drives increased civil conflict – and historically, when rainfall patterns are significantly below normal, the chance of a low-level conflict escalating to a civil war doubles the following year. A fragile peace that would have succeeded otherwise can be disrupted by shortages, nationalism, and political extremism.
In the future, it is estimated that 46 countries (~2.7 billion people) will face a high risk of violent conflict due to climate change, and a further 56 countries (~1.2 billion people) face potential politically destabilizing effects. To illustrate what these cascades look like, in the present day:
Notably, the Indian subcontinent, North Africa, and the Middle East have already faced water scarcity – and we can map their current geopolitical effects. Dam-building has been the source of escalating disputes in the Mekong River basin. The Nile Basin has been home to diverging interests between upstream and downstream countries – especially as upstream Egypt is projected to use more water than the Nile supplies. In Libya, the threat of cutting off water infrastructure is leveraged by violent militias against rivals. Turkey has historically and recently weaponized water as leverage against Syria and Iraq. Yemen’s water scarcity fuels its political insecurity and crisis. Furthermore, the cost of water is likely to increase – and in Pakistan and India, precursors to water mafias have already begun to spring up as organized crime groups trade, hoard, and steal water on the black market.
In a previous forum post, I expressed that beyond third-world civil conflicts, I was especially worried about far-right and politically extreme governments being elected in the first world – the Orban, Bolsonaro, Trump, or Le Pen-esque figures that can shake things up – because of economic, social (e.g. anti-refugee radicalism), and inflationary pressures from climate change. This volatility can be extraordinarily dangerous for international norms & politics.
Cascading Cybersecurity Risk
Theoretically, increased conflict and social tension can result in coordinated cyberattacks that create a cascading effect on societal systems – especially given interdependencies in computer-based critical infrastructure systems as well as the asymmetric nature of cyberwarfare.
Therefore, conflict or geopolitical tension itself is far from just a cascade; it can also serve as triggers for other systemic risks (e.g. fragility of network infrastructure) to cascade further.
4) Countering Cascading Systemic Risk
Institutional resilience is the generalization of the solution to systemic cascading risks. Our system has rarely been stressed this much before, yet the 21st century is revealing how uniquely interconnected, interdependent, and vulnerable our supply chains are. There likely exist many other systemic cascading stressors outside of just climate, pandemics, conflict, and cyber risks (as less serious case studies, like the 1997 Asian or 2008 American financial crises, highlight). All of these can all be tractably mitigated in tandem by improving system fragility – e.g. by tracking and ensuring the necessities of the commodities to live.
Securing the Nexus of What Modern Societies Require for Survival
Food, water, energy, and infrastructure (where housing falls under infrastructure) form the nexus of what societies require for survival, giving us a comprehensive framework to target resiliency interventions toward.
In the absence of these necessities, political instability cascades in a far greater effect. Examples include Egyptian and Moroccan bread riots in 1977 and 1984, Jordanese protests in 1989, as well as the Arab Spring in 2011. Historically, volatile political risks often cascade through decreases to standard of living and household economic stresses.
However, currently – as an example – most private and public climate capital goes toward prevention, not toward adaptation & resiliency measures (which only make up ~5% of all climate finance). Even most EA paradigms for addressing climate change fall under the former (including the 80,000 Hours page and Founder’s Pledge’s Climate Change Fund). This leaves a (relatively) neglected gap in the climate ecosystem – one focused on institutional resilience and mitigating climate change’s systemic cascading impacts through food, water, energy, and infrastructure-focused interventions.
There is potential for great entrepreneurial & non-profit-focused efforts focused around securing this nexus – including resilient & emergency foods, drought monitoring and resilience, climate vulnerability analyses on supply chains, and building cost-effective quickly-deployable refugee shelters. In the private sector, providing tailored climate data to agriculture companies, supply chain managers, governments, and beta investor activists can facilitate greater resiliency investment and preparedness. In addition to resilience, increasing substitutes means more options to hedge against inflationary forces – analogous to having multiple nodes for cascades to disperse through to distribute the momentum of a stressor (redundancy).
This can prove a very tractable field to work on. For example, the World Bank’s Groundswell report finds that adaptation development, when developed alongside other prevention efforts, can reduce the scale of climate migration by up to 80% – potentially greatly increasing global stability.
Furthermore, there is a great synergistic effect to mitigating cascading systemic risks: the ability to work on them all in tandem. Resilient foods, scenario forecasting models, and fast shelter construction techniques can be recycled for conflict, pandemic, and climate resilience alike – because these risks are fundamentally three sides of the same coin.
Modeling & Understanding Global Supply Chains Related to This Nexus
Crucial to informing effective resiliency efforts is building a solid understanding of global supply chains, industry sectors, key interdependencies, political & economic systems, and historical cascades - to create a model of the nodes & edges at play.
Scenario analysis around pandemics, conflicts, and climate risks (as well as any other plausible systemic cascading risks I’ve left out) would also greatly assist resiliency efforts, enabling us to understand realistic cascading effects and target interventions to hedge against cascading risks that may happen at a reasonable probability.
Accurate quantitative models are necessary to inform that understanding – however, mapping supply chains can prove quite a difficult task due to its required comprehensiveness and complexity in datasets and modeling. Perfectly modeling all factors affecting wheat flows, for instance, wouldn’t just entail calculating domestic production, consumption, imports, and exports; it’d require tracking the turtles all the way down: water scarcity, drought risk, land usage, fertilizers, seeds, and chemicals – which in turn are affected by energy prices, phosphate production, and so on.
However, there are already datasets and models available to do some of this work, including public repositories from UN Comtrade, UN FAOSTAT, IMF Macroeconomic & Financial Data, World Bank DataBank, and CEPII BACI that can inform cascading economic impacts. Furthermore, global climate and trade models – such as ALLFED’s Integrated Model, built off UN FAOSTAT data – can be interlinked with other models, allowing for accurate projection of systemic cascading effects through networks.
It is unclear to me whether a model that is too comprehensive in mapping trade flows may pose an info hazard by enabling malicious, targeted attacks. This seems plausible (even with publicly available data) and worth consideration.
From a governmental perspective, I see three immediately obvious actions:
- Facilitating investment in resilient supply chains
- Policies to reward/incentivize private actors toward redundancy – e.g. just-in-time production arose out of profit incentives that favored efficiency over resiliency
- Improving coordination between actors to respond to crises
Governments are in a unique position to leverage existing resources and modify system structures. Incentivizing systemic cascading risk paradigms & long-term thinking through governance can also help facilitate resilience investment: i.e. policies that require accurate models of climate risk from central banks to inform risk analysis, tailoring private shareholder incentives to care more about long-term firm viability, etc.
Why not use the phrase “improving institutional decision-making”?
I believe the current closest EA catchphrase, “improving institutional decision-making,” is too vague of a phrase to use as a substitute to identify tractable supply chain resiliency solutions. This is because in its current form, this cause area is so general it encompasses many meta-level decision-making problems – from democratic reform to forecasting elections to reducing biases.
“Systemic cascading risk” is specific, relevant, and clear. The stability of systems rests on institutions, supply chains, and the necessities to life. Poverty, food insecurity, unemployment, & weak governance drive instability and cascading risks. A systemic cascading lens hedges against possible cascades – limiting crises to localized events rather than international political catastrophes.
5) Significance to Longtermism
Current Epistemic Prioritization of Tail Risk
A lot of the risks I pose above are viewed as direct existential risks, but not as cascading stressors – leading to the prevailing internal view of the EA community being that the tail end of these problems is most significant.
For example, climate change may not be a direct existential risk. Only recently, however, has there been a shift in thinking of climate change within the EA community – from an unlikely, unimportant tail-end direct risk to a possible existential risk multiplier, viewing climate change as leading to stressors which increase instability on fragile global systems.
Currently, in the community, pandemic and conflict risks are often framed the same way – e.g. whether a conflict could cause nuclear war, rather than whether a conflict could cascade across systems and make them more fragile and susceptible to other compounding risks.
Overreliance on linear models and ways of thinking prioritizes tail risk – resulting in an epistemic attitude may undervalue the importance of systemic cascading effects.
Cascades on Technology Development
Longtermist cause areas tend to be united in a focus on technology development – and a worry of emerging biotechnology or artificial general intelligence being misaligned or misused in some way. Therefore, longtermism tends to promote the most direct interventions: technology safety research and technology governance interventions.
However, on a larger scale, it makes sense to ensure a stable political environment as these technologies mature. Climate sociopolitics, pandemic sociopolitics, conflict sociopolitics, governmental norms, and sociotechnical values can act as multiplying forces, clearly and irreversibly affecting the development and regulation of technologies that pose direct existential risks.
It only requires a few further cascades for sociopolitical risks to reach the node in our graph labeled AGI development or bioweapons development or intergovernmental trust. Here are three concrete examples of how political environments can drive existential risk:
- AGI governance – Lowering international tensions is vital for AGI governance.
- Sociopolitical tension, the election of politically extreme governments, and the violation of international norms can pose a significant barrier to international cooperation in AGI regulation. Notably, any long-term solution involving AGI governance would likely involve the U.S. and China.
- Climate-driven nationalist sentiment, counterinsurgency campaigns, refugee politics, or proxy wars may drive mutual distrust.
- Domestic regulation of AI is limited by game theory-esque dynamics of “not letting the other side get ahead in the AI race,” and is therefore (at least somewhat) tied to the fate of international regulation.
- Driving forward military-based AI capabilities – and arms race dynamics, misalignment, and misuse.
- In an increasingly politically tumultuous time (e.g. terrorism, refugee crises, political extremism, assassination/coup risk), fear and uncertainty begets military spending and thus weaponry development.
- Given trends in advances in AI capabilities, armed drone development and applied AI in military intel & decision-making will be favored in developed countries due to cost and effectiveness.
- The U.S. DoD is already preparing counterinsurgency efforts in response to climate terrorism – so we know our drones are likely to be used somewhere in the future.
- Political volatility can result in arms race dynamics being multiplied between countries.
- Human-in-the-loop systems and standard procedures are usually required for (safer) autonomous weapons. However, there is an increasingly strong incentive for the losing side of a conflict to give their autonomous weapons more autonomy than normal.
- Nuclear weapons & bioweapons – a multiplying effect on risk
- To the extent you think nuclear weapons or bioengineered pandemics pose significant existential risks, those risk factors get multiplied by climate sociopolitics (and other potential systemic cascading risks) as drivers of international tension.
Risks that threaten the political stability of our societies threaten our ability to develop new technologies safely, competently, and cooperatively. Studying these dynamics requires grappling with some unquantifiable uncertainty by necessity, but incorporating complexity principles in risk calculations is necessary to produce accurate models of reality.
6) Significance to Values Lock-In & Path Dependency
What values do we encode into powerful technologies?
William McAskill (2022) observes that institutions and technologies alike tend to go through an earlier period of plasticity (where the basic worldview and norms of that institution are being formed) and then a later period of rigidity (where momentum primarily carries institutional norms forward).
In the next 10-30 years, many novel technologies are being formed – it’s a key time.
In the next 10-30 years, many sociopolitical crises can also cascade – it’s a key time.
Thus, in this key time, we encounter a highly path-dependent precipice with new technologies. If we start going on a particular path and these technologies lock-in those values, it makes it more difficult to access other alternative paths we could have had – making the impact significant, persistent, and contingent.
By locking certain values into powerful technologies, one encodes the sociotechnical nature of a very particular time and place. Similar analogies have happened to historical technologies encoded with the values of their time – e.g. racist architectural exclusion and car-centric cul-de-sacs and interstate highway systems in the U.S.
Thus, who and why someone creates technology – and their core values – matter.
Systemic cascades & value norms
Value-locking can be applied to analyze the longterm risks of institutional failure and societal tension from climate change and other associated cascading risks. There may be more likely paths (path dependency) where the society we become post-crisis will likely miss certain values.
There is a sort of “trauma” one is likely putting humanity through by allowing pervasive climate- and crisis-driven scarcity & tension to be a possibility – and this will likely be reflected in the values encoded in political institutions. Political crisis and fear-based extremism tends to historically lean toward heavily anti-democratic, authoritarian, and violent social values, while abundance tends to beget altruism and peace.
This locks in certain values in societal norms. Due to international lack of resiliency and cooperation, the overall set of values that are practically available to society after climate catastrophe are likely on average significantly worse and less likely to provide large utility to a large group of people.
These crises can affect the sociotechnical values locked into emerging existential technologies – resulting in a highly significant, persistent, and contingent effect. These include serious risks associated with AGI – AGI might have a particular set of encoded values, and in the worst case, these values are misaligned with humanity in general (e.g. authoritarianism) and are values we are permanently stuck with.
Given optimistic and pessimistic AI futures, how is AI being used - right now?
Which values are currently being encoded into commercial AI systems, labs producing cutting-edge AI capabilities, autonomous weapons systems, and surveillance technologies?
What sort of values will be further encoded into the industry as capabilities research expands, and what cascading effects will this have on eventual AGI development?
By enabling possible values lock-in during a volatile time of systemic cascading risks, will we encode our best values, or is there a strong possibility of a value misalignment?
7) The Strongest Argument Against This Post
I believe the strongest argument against this post goes as follows:
Systemic cascading risks should be considered, but all-in-all, pandemic/conflict/climate cascades and supply chain risk are generally not neglected compared to a field like AI alignment where there are only ~300 estimated technical researchers. If EA can make an outsized impact through this space, all the better – perhaps there is an argument to be made through sheer importance and tractability alone.
A systemic cascading lens could also be an applicable framework to inform and critique effective altruist community epistemics. Here are some low-confidence for-fun thoughts that, in my opinion, are further thoughts to investigate.
As an example, naive consequentialism and burnout can be logically countered through a consequentialist systemic cascading lens. There are many risks that come from an overoptimized life focused on a singular goal, and a systemic cascading risk is one where the actor does not take into account the interdependence of systems and how a change in one system can lead to cascading changes in other systems. In the context of work, a systemic cascading risk is the risk of overworking yourself to the point of burnout. Resiliency would include actions that protect you from burnout, improve the positive feedback loops that keep you engaged and active, help you explore new horizons and apply you natural creativity and curiosity, and identify errors which you can make adjustments to.
Similarly, community building, diversity of ideas, and epistemic infrastructure have powerful systemic cascading effects (and benefits).
There is always a three-body problem in trying to rank various interventions. Day-to-day, effective altruism biases towards ways of knowing which interventions are economically viable, measurable, and quantifiable. Systemic cascading effects are a powerful way to think about the full range of impacts – both linear and non-linear – of our actions. By taking them into account, we can consider the fuller picture and avoid doing more harm than good.
Many of the statistics are drawn from Mar 2022. ↩︎
EA examples: ALLFED, Open Phil's grant (May 2020) to Penn State for Research on Emergency Food Resilience. While most of their ag resilience work focuses on global catastrophic risk (e.g. nuclear war), I believe their work is quite applicable to general resiliency efforts as well. ↩︎
Seems very tractable & neglected – 54% of WMO members have lacking or inadequate drought warning systems (as of 2021). ↩︎
Beta investor activists are typically institutional asset owners (e.g. pension funds) that focus on the long-term performance of the market as a whole, rather than just the short-term performance of individual companies (e.g. most PE, hedgefunds, & VC firms). They theorize that because they are “universal owners” of an economy, effectively holding a slice of the overall market and diversified away from most individual firm risks, they can best improve their long term financial performance by acting in such a way as to encourage sustainable & healthy economies and markets. This makes their incentives aligned with preventing systemic cascading risks, encouraging resiliency, and long-term risk planning. I've been particularly inspired by the ideas written by Ellen Quigley (CSER) on this front. Phil Chen also did a more detailed forum post on leveraging finance to improve resilience. ↩︎
https://github.com/allfed/allfed-integrated-model/tree/main/data Big thanks to Morgan Rivers for showing me around this thing! ↩︎
This is when uncertainty, speculation, and unpredictability in outcomes occurs, as n-th order social dynamics are extremely difficult to predict. I only present plausible pathways and do not make strong claims on probability of occurrence. ↩︎
I highly recommend the book Tropic of Chaos to gain an investigative journalist’s perspective on how extreme weather events are cascading into state failure and counterinsurgency. ↩︎
This was inspired by conversations with Neil Thompson at EAG SF. ↩︎
(And current democratic institutions have not necessarily proven themselves better at handling these crises than techno-authoritarian governments.) ↩︎
A lot more studying needs to be done on how these value lock-ins occurred and how to prevent them – especially from a historical perspective. ↩︎
Freedom House’s 2021 Democracy Under Siege report seems particularly relevant here. ↩︎
The section on values lock-in and path dependency was significantly inspired by conversations with Clem Von Stengel & Archana Ahlawat. I highly recommend reading Archana’s forum post, which further explores a path dependency framework and its impact on long-term outcomes. ↩︎
I personally pretty much agree with this argument, and still think institutional resilience is important and tractable enough to bring up as a criticism of EA. For example, “improving institutional decision-making” (broadly) or “preventing pandemics” aren’t neglected either, but I still imagine an additional person on the margin can produce solid work that has positive cascading effects, especially if the interventions themselves focus on targeted and neglected subfields of a broader field. (This has yet to be rigorously proven for institutional resilience interventions and remains nothing more than an intuition of mine.) ↩︎
"Refugees: ~216 million climate refugees by 2050 (World Bank Groundswell Report) caused by droughts and desertification, sea-level rise, coastal flooding, heat stress, land loss, and disruptions to natural rainfall patterns"
The groundswell report is about voluntary internal migration, so it is not about refugees, who are typically defined as involuntarily displaced people crossing national borders.
I didn't realize the phrase "climate refugees" implied involuntary cross-border migration and mistaked it for a blanket term for climate migration. Thanks for the catch!
For the sake of fairness for the EA criticism contest, I won't edit the mistake now but maybe after the competition winners have been announced. If I were to edit & rephrase it, it'd look something like:
I'm curious what you (and anyone else) thinks are differences between:
Quick thoughts: People might be a lot more sympathetic to migrants (or refugees) who are of similar cultural backgrounds to them, prompting less social tension and political extremism.
As a notable example, the political effects of Arab vs Ukrainian refugees on Europe are markedly different.
Thanks! What about in the case that the number of refugees or internal migrants rises a lot? So rather than ten thousand, a million?
That depends. For example, global food supply depends on a system of stocks and flows that emphasizes flows and concentrated sources. This is a problem with our global system of manufacturing and trade and the tendency of corporations to optimize throughput while lowering costs. The whole system is not designed to handle something like:
A mapped and monitored system for the entire food supply chain (from fertilizers to seed sources to ship berths), production and delivery system will show problems at sources, pathways, chokepoints, and sinks. It won't solve the likely problems though.
In the case of a multi-breadbasket failure, sources decline, temporarily or permanently, depending on the causes. Flows collapse, and we turn to stocks. A monitoring system is worthless unless:
We need the type of system you're talking about, but we also need resiliency built into the system now. We need:
We need to spread out food sources and reduce overall productivity proactively, pulling back from producing in areas that we will lose to climate change. We also need larger stocks and a change in how we produce foods.
The type of modeling work you're describing, if intended to produce resiliency, would quickly reveal:
in order to increase resiliency of product supply to whatever region or small population. I say "whatever" because I am not too sure where they will be in 20-30 years.
There are other constraints on supply shortages of various products to do with politics and greed, governments and money (for example, with food: terminator seeds, agricultural run-off, govt subsidy incentives, big food lobbying)
We understand the supply-chain essentials that interfere with resiliency:
Where do we go from here?
I agree with the following statement:
My low-confidence rationale for including a section on modeling, scenario analysis, & its helpfulness to building resiliency is twofold:
1. Targeting & informing on-the-ground efforts: Overlaying accurate climate agriculture projections on top of food trading systems can help us determine which trade flows will be most relied on in the future and target interventions where they would be most effective and neglected - e.g. select between various agriculture interventions in different regions, lobbying for select policies or local food stocks, and tailoring food resilience research/engineering efforts towards countries and situations that will be projected to need it most.
2. Influencing risk-sensitive actors: Having accurate trade flow models can also help determine & project dangerous economic second-order consequences, creating more accurate risk analyses and thus further incentivizing governments and risk-sensitive organizations toward a coordinated systemic reform/response.
Open to have this opinion change.
Yes, so gather information about what's happening and tell those who could be effected by changes later on.
I proposed a reform to enhance food system resiliency for smaller regions and populations. What do you think of it?
Constantly expanding list of mistakes I made / things I would change in this post (am not editing at the moment because this is an EA criticism contest submission):
I misinterpreted what Toby Ord was saying in The Precipice (page 268). He specifically claimed he preferred narrow/targeted over broad interventions because they can be targeted toward technological risks directly & thus can be expected to accomplish much more, compared to previous centuries. (He also made a neglectedness-based argument for targeted interventions.) I believe it was other people or other things I read (likely where the confusion comes from) that made claims about casual steps using the targeted vs broad framework.
I'm also not arguing for broad interventions, necessarily. As commonly used, the narrow vs broad framework doesn't fully capture my argument for the importance of systemic cascading risk for multiple reasons:
For all those reasons, I'd probably remove this quote.
I didn't realize the phrase "climate refugees" implied involuntary cross-border migration and mistaked it for a blanket term for climate migration. Thanks to John Halstead for pointing this one out; through this quote, I unintentionally misrepresented the weight of the evidence.
If I were to edit & rephrase it, it'd look something like: "~216 million internally displaced climate migrants by 2050 (World Bank Groundswell Report), which can give a rough order of magnitude estimate for total cross-border climate migrants and refugees (figures which are much harder to quantify)".
Nice article! There's a lot of things to unpack here, as it goes over quite a lot, but I wanted to focus on something that caught my attention in section 4.
It appeared to me that you discuss two types of resiliency without - to my mind - making much of a distinction between them. The first is that of institutional resiliency, and the second that of resilient interventions. In my mind, this latter one comes across as object-level interventions for for specific problems - drought-tracking, etc., and the former as meta-level organisational design/interventions for ensuring that our current institutions can build operate under (future, probable) high organisational stress conditions.
Is this a conceptual divide you would endorse, or do you more see the institutional resiliency as another object-level area of resiliency interventions on line with others, and which would be upgraded/updated in tandem with other object-level interventions? (e.g. As we get better drought-tracking capabilities, it is designed so that institutional resiliency in the usage of this systemis a built-in feature package.)