Note: this is an unusually long and abstract post whose primary purpose is to help a particular subset of our audience understand our style of reasoning. It does not contain substantive updates on our research and recommendations.

GiveWell – both our traditional work and GiveWell Labs – is fundamentally about maximization: doing as much good as possible with each dollar you donate. This introduces some major conceptual challenges when making certain kinds of comparisons – for example, how does one compare the impact of distributing bednets in sub-Saharan Africa with the impact of funding research on potential high-risk responses to climate change, attempts to promote better collaboration in the scientific community or working against abuse of animals on factory farms?

Our approach to making such comparisons strikes some as highly counterintuitive, and noticeably different from that of other “prioritization” projects such as Copenhagen Consensus. Rather than focusing on a single metric that all “good accomplished” can be converted into (an approach that has obvious advantages when one’s goal is to maximize), we tend to rate options based on a variety of criteria using something somewhat closer to (while distinct from) a “1=poor, 5=excellent” scale, and prioritize options that score well on multiple criteria. (For example, see our most recent top charities comparison.)

We often take approaches that effectively limit the weight carried by any one criterion, even though, in theory, strong enough performance on an important enough dimension ought to be able to offset any amount of weakness on other dimensions. Relatedly, we look into a broad variety of causes, broader than can seemingly be justified by a consistent and stable set of values. Many others in the effective altruist community seem to have a strong and definite opinion on questions such as “how much animals suffer compared to humans,” such that they either prioritize animal welfare above all else or dismiss it entirely. (Similar patterns apply to views on the moral significance of the far future.) By contrast, we give simultaneous serious consideration to reducing animal suffering, reducing risks of global catastrophic events, reforming U.S. intellectual property regulation, global health and nutrition and more, and think it’s quite likely that we’ll recommend giving opportunities in several of these areas, while never resolving the fundamental questions that could (theoretically) establish one such cause as clearly superior to the others.

I believe our approach is justified, and in order to explain why – consistent with the project of laying out the basic worldview and epistemology behind our research – I find myself continually returning to the distinction between what I call “sequence thinking” and “cluster thinking.” Very briefly (more elaboration below),

• Sequence thinking involves making a decision based on a single model of the world: breaking down the decision into a set of key questions, taking one’s best guess on each question, and accepting the conclusion that is implied by the set of best guesses (an excellent example of this sort of thinking is Robin Hanson’s discussion of cryonics). It has the form: “A, and B, and C … and N; therefore X.” Sequence thinking has the advantage of making one’s assumptions and beliefs highly transparent, and as such it is often associated with finding ways to make counterintuitive comparisons.
• Cluster thinking – generally the more common kind of thinking – involves approaching a decision from multiple perspectives (which might also be called “mental models”), observing which decision would be implied by each perspective, and weighing the perspectives in order to arrive at a final decision. Cluster thinking has the form: “Perspective 1 implies X; perspective 2 implies not-X; perspective 3 implies X; … therefore, weighing these different perspectives and taking into account how much uncertainty I have about each, X.” Each perspective might represent a relatively crude or limited pattern-match (e.g., “This plan seems similar to other plans that have had bad results”), or a highly complex model; the different perspectives are combined by weighing their conclusions against each other, rather than by constructing a single unified model that tries to account for all available information.

A key difference with “sequence thinking” is the handling of certainty/robustness (by which I mean the opposite of Knightian uncertainty) associated with each perspective. Perspectives associated with high uncertainty are in some sense “sandboxed” in cluster thinking: they are stopped from carrying strong weight in the final decision, even when such perspectives involve extreme claims (e.g., a low-certainty argument that “animal welfare is 100,000x as promising a cause as global poverty” receives no more weight than if it were an argument that “animal welfare is 10x as promising a cause as global poverty”).

Finally, cluster thinking is often (though not necessarily) associated with what I call “regression to normality”: the stranger and more unusual the action-relevant implications of a perspective, the higher the bar for taking it seriously (“extraordinary claims require extraordinary evidence”).

I’ve tried to summarize the difference with the following diagram. Variation in shape size represents variation in the “certainty/robustness” associated with different perspectives, which matters a great deal when weighing different perspectives against each other for cluster thinking, but isn’t an inherent part of sequence thinking (it needs to be explicitly modeled by inserting beliefs such as “The expected value of this action needs to be discounted by 90%”).

I don’t believe that either style of thinking fully matches my best model of the “theoretically ideal” way to combine beliefs (more below); each can be seen as a more intellectually tractable approximation to this ideal.

I believe that each style of thinking has advantages relative to the other. I see sequence thinking as being highly useful for idea generation, brainstorming, reflection, and discussion, due to the way in which it makes assumptions explicit, allows extreme factors to carry extreme weight and generate surprising conclusions, and resists “regression to normality.” However, I see cluster thinking as superior in its tendency to reach good conclusions about which action (from a given set of options) should be taken. I have argued the latter point before, using a semi-formal framework that some have found convincing, some believe has flaws, and many have simply not engaged due to its high level of abstraction. In this post, I attempt a less formalized, more multidimensional, and hopefully more convincing (more “cluster-style”) defense. Following that, I lay out why I think sequence thinking is important and is probably more undersupplied on a global scale than cluster thinking, and discuss how I try to combine the two in my own decision-making. Separately from this post, I have also published a further attempt to formalize the underlying picture of an idealized reasoning process. 

By its nature, cluster thinking is hard to describe and model explicitly. With this post, I hope to reduce that problem by a small amount – to help people understand what is happening when I say things like “I see no problem with your reasoning, but I’m not placing much weight on it anyway” or “I think that factor could be a million times as important as the others, but I don’t want to give it 100x as much attention,” and what they can do to change my mind in such circumstances. (The general answer is to reduce the uncertainty associated with an argument, rather than simply demonstrating that no explicit flaws with the argument are apparent.)

In the remainder of this post, I:

• Elaborate on my definitions of sequence and cluster thinking. More
• Give a variety of arguments for why one should expect cluster thinking to result in superior decisions. More
• Briefly note and link to a new page (published alongside this post) that attempts to formalize, to some degree, the “idealized thought process” I’m envisioning and how it reproduces key properties of cluster thinking. More
• Lay out some reasons that I find sequence thinking valuable, even if one accepts that cluster thinking results in superior decisions, and defend the idea of switching between “sequence” and “cluster” styles for different purposes. I believe sequence thinking is superior not only for purposes of discussion and reflection (due to its transparency), but also for reaching the sort of deep understanding necessary for intellectual progress, and for generating novel insights that can become overwhelmingly important. More
• Briefly discuss why cluster thinking can be confusing and challenging to deal with in a discussion, and outline how one can model and respond to cluster-thinking-based arguments that are often perceived as “conversation stoppers.”More
• Close with a brief discussion of how I try to combine the two in my own thinking and actions. More

Before I continue, I wish to note that I make no claim to originality in the ideas advanced here. There is substantial overlap with the concepts of foxes and hedgehogs (discussed by Philip Tetlock); with the model and combination and adjustment idea described by Luke Muehlhauser; with former GiveWell employee Jonah Sinick’s concept of many weak arguments vs. one relatively strong argument (and his post on Knightian uncertainty from a Bayesian perspective); with former GiveWell employee Nick Beckstead’s concept of common sense as a prior; with Brian Tomasik’s thoughts on cost-effectiveness in an uncertain world; with Paul Christiano’s Beware Brittle Arguments post; and probably much more.

Sequence thinking might look something like:

Charity A spends $A per child vaccinated. Each vaccination reduces the odds of death by B%. (Both A and B can be grounded somewhat in further analysis.) That leaves an estimate of (B/A) lives saved per dollar. I will adjust this estimate down 50% to account for the fact that costs may be understated and evidence may be overstated. I will adjust it down another 50% to account for uncertainties about organizational competence.

Charity B spends $C per year. My best guess is that it improves the odds that space colonization eventually occurs by D%. I value this outcome as the equivalent of E lives saved, based on my views about when space colonization is likely to occur, how many human lives would be possible in these case, and how I value these lives. (C, D, and E can be grounded somewhat in further analysis.) That leaves an estimate of (D*E)/C) lives saved per dollar. I will adjust this estimate down 95% to account for my high uncertainty in these speculative calculations. I will adjust it down another 75% to account for uncertainties about organizational competence, which I think are greater for Charity B than Charity A; down another 80% to account for the fact that expert opinion seems to look more favorably on Charity A; and down another 95% to account for the fact that charities such as Charity A generally have a better track record as a class.

After all of these adjustments, Charity B comes out better, so I select that one.

Cluster thinking might look something like:

Explicit expected-value calculations [such as the above] imply quite a strikingly good cost-per-life-saved for Charity A, and I think the estimate isn’t terribly likely to be terribly mistaken. That’s a major point in favor of Charity A. Similar calculations imply good cost-per-life-saved for Charity B, but this is a much more uncertain estimate and I don’t put much weight on it. The fact that Charity B comes out ahead even after trying to adjust for other factors is a point in favor of Charity B. In addition, Charity A seems like a better organization than Charity B, and expert opinion seems to favor Charity A, and organizations such as Charity A generally have a better track record as a class, and all of these are signals I have a fair amount of confidence in. Therefore, Charity A has more certainty-weighted factors in its favor than Charity B.

Note that this distinction is not the same as the distinction between explicit expected value and holistic-intuition-based decision-making. Both of the thought processes above involve expected-value calculations; the two thought processes consider all the same factors; but they take different approaches to weighing them against each other. Specifically:

• Sequence thinking considers each parameter independently and doesn’t do any form of “sandboxing.” So it is much easier for one very large number to dominate the entire calculation even after one makes adjustments for e.g. expert opinion and other “outside views” (such as the track record of the general class of organization). More generally, it seems easier to reach a conclusion that contradicts expert opinion and other outside views using this style. This style also seems more prone to zeroing in on a particular category of charity as most promising: for example, often one’s estimate of the value of space colonization will either be high enough to dominate other considerations or low enough to make all space-colonization-related considerations minor, even after many other adjustments are made.
• The two have very different approaches to what some call Knightian uncertainty (also sometimes called “model uncertainty” or “unknown unknowns”): the possibility that one’s model of the world is making fundamental mistakes and missing key parameters entirely. Cluster thinking uses several models of the world in parallel (e.g., “Expert opinion is correct”, “The track record of the general class of an organization predicts its success”, etc.) and limits the weight each can carry based on robustness (by which I mean the opposite of Knightian uncertainty: the feeling that a model is robust and unlikely to be missing key parameters); any chain of reasoning involving high uncertainty is essentially disallowed from making too much difference to the final decision, regardless of the magnitude of effect it points to. Sequence thinking involves the use of a single unified framework for decision analysis and by default it treats “50% probability that a coin comes up heads” and “50% probability that Charity B will fail for a reason I’m not anticipating” in fundamentally the same way. When it does account for uncertainty, it’s generally by adjusting particular parameters (for example, increasing “0.00001% chance of a problematic error” to “1% chance of a problematic error” based on the chance that one’s calculations are wrong); after such an adjustment, it uses the “highly uncertain probabilities adjusted for uncertainty” just as it would use “well-defined probabilities,” and does not disallow the final calculation from carrying a lot of weight.

Robustness and uncertainty

For the remainder of this piece, I will use the term robustness to refer to the “confidence/robustness” concept discussed immediately above (and “uncertainty” to refer to its opposite). I’m aware that I haven’t defined the term with much precision, and I think there is substantial room for sharpening its definition. One clarification I would like to make is that robustness is not the same as precision/quantifiability; instead, it is intended to capture something like “odds that my view would remain stable on this point if I were to gain more information, more perspectives, more intelligence, etc.” or “odds that the conclusion of this particular mental model would remain qualitatively similar if the model were improved.”

Regression to normality

A final important concept, which I believe is loosely though not necessarily related, is that of regression to normality: the stranger and more unusual the implications of an argument, the more “robustness” the supporting arguments need to have in order for it to be taken seriously. One way to model this concept is to consider “Conventional wisdom is correct and what seems normal is good” to be one of the “perspectives” or “mental models” weighed in parallel with others. This concept can potentially be modeled in sequence thinking as well, but in practice does not seem to be a common part of sequence thinking.

A couple more clarifications

Note that sequence thinking and cluster thinking converge in the case where one can do an expected-value calculation with sufficiently high robustness. “Outside view” arguments inherently involve a substantial degree of uncertainty (there are plenty of examples of expert opinion being wrong, of longstanding historical trends suddenly ending, etc.) so a robust enough expected-value calculation will carry the decision in both frameworks.

Note also that cluster thinking does not convert “uncertain, speculative probabilities” automatically into “very low probabilities.” Rather, it de-weights the conclusions of perspectives that overall contain a great deal of cumulative uncertainty, so that no matter what conclusion such perspectives reach, the conclusion is not allowed to have much influence on one’s actions.

Summary of properties of sequence thinking and cluster thinking

Below, I give several arguments for expecting cluster thinking to produce better decisions. It is important to note that I emphasize “better decisions” and not “correct beliefs”: it is often the case that one reaches a decision using cluster thinking without determining one’s beliefs about anything (other than what decision ought to be made). In the example given in the previous section, cluster thinking has not reached a defined conclusion on how likely space colonization is, how valuable space colonization would be, etc. and there are many possible combinations of these beliefs that could be consistent with its conclusion that supporting Charity A is superior. Cluster thinking often ends up placing high weight on “outside view” pattern-matching, and often leads to conclusions of the form “I think we should do X, but I can’t say exactly why, and some of the most likely positive outcomes of this action may be outcomes I haven’t explicitly thought of.”

The arguments I give below are, to some degree, made using different vocabularies and different styles. There is some conceptual overlap between the different arguments, and some of the arguments may be partly equivalent to each other. I have previously tried to use sequence-thinking-style arguments to defend something similar to cluster thinking (though there were shortcomings in the way I did so); here I use cluster-thinking-style arguments.

Sequence thinking is prone to reaching badly wrong conclusions based on a single missing, or poorly estimated, parameter

Sequence-style reasoning often involves a long chain of propositions that all need to be reasonable for the conclusion to hold. As an example, Robin Hanson lays out 10 propositions that cumulatively imply a decision to sign up for cryonics, and believes each to have probability 50-80%. However, if even a single one ought to have been assigned a much lower probability (e.g., 10^-5) – or if he’s simply failed to think of a missing condition that has low probability – the calculation is completely off.

In general, missing parameters and overestimated probabilities will lead to overestimating the likelihood that actions play out as hoped, and thus overestimating the desirability of deviating from “tried and true” behavior and behavior backed by outside views. Correcting for missed parameters and overestimated probabilities will be more likely to cause “regression to normality” (and to the predictions of other “outside views”) than the reverse.

Cluster thinking is more similar to empirically effective prediction methods

Sequence thinking presumes a particular framework for thinking about the consequences of one’s actions. It may incorporate many considerations, but all are translated into a single language, a single mental model, and in some sense a single “formula.” I believe this is at odds with how successful prediction systems operate, whether in finance, software, or domains such as political forecasting; such systems generally combine the predictions of multiple models in ways that purposefully avoid letting any one model (especially a low-certainty one) carry too much weight when it contradicts the others. On this point, I find Nate Silver’s discussion of his own system and the relationship to the work of Philip Tetlock (and the related concept of foxes vs. hedgehogs) germane:

Even though foxes, myself included, aren’t really a conformist lot, we get worried anytime our forecasts differ radically from those being produced by our competitors.

Quite a lot of evidence suggests that aggregate or group forecasts are more accurate than individual ones … “Foxes often manage to do inside their heads what you’d do with a whole group of hedgehogs,” Tetlock told me. What he means is that foxes have developed an ability to emulate this consensus process. Instead of asking question of a whole group of experts, they are constantly asking questions of themselves. Often this implies that they will aggregate different types of information together – as a group of people with different ideas about the world naturally would – instead of treating any one piece of evidence as though it is the Holy Grail. The Signal and the Noise, pg 66

In sequence thinking, a single large enough number can dominate the entire calculation. In consensus decision making, a person claiming radically larger significance for a particular piece of the picture would likely be dismissed rather than given special weight; in a quantitative prediction system, a component whose conclusion differed from others’ by a factor of 10^10 would be likely to be the result of a coding error, rather than a consideration that was actually 10^10 times as important as the others. This comes back to the points made by the above two sections: cluster thinking can be superior for its tendency to sandbox or down-weight, rather than linearly up-weight, the models with the most extreme and deviant conclusions.

A cluster-thinking-style “regression to normality” seems to prevent some obviously problematic behavior relating to knowably impaired judgment

One thought experiment that I think illustrates some of the advantages of cluster thinking, and especially cluster thinking that incorporates regression to normality, is imagining that one is clearly and knowably impaired at the moment (for example, drunk), and contemplating a chain of reasoning that suggests high expected value for some unusual and extreme action (such as jumping from a height). A similar case is that of a young child contemplating such a chain of reasoning. In both cases, it seems that the person in question should recognize their own elevated fallibility and take special precautions to avoid deviating from “normal” behavior, in a way that cluster thinking seems much more easily able to accommodate (by setting an absolute limit to the weight carried by an uncertain argument, such that regression to normality can override it no matter what its content) than sequence thinking (in which any “adjustments” are guessed at using the same fallible thought process).

The higher one’s opinion of one’s own rationality relative to other people, the less appropriate the above analogy becomes. But it can be easy to overestimate one’s own rationality relative to other people (particularly when one’s evidence comes from analyzing people’s statements rather than e.g. their success at achieving their goals), and some component of “If I’m contemplating a strange and potentially highly consequential action, I should be wary and seek robustness (not just magnitude) in my justification” seems appropriate for nearly everyone.

Sequence thinking seems to tend toward excessive comfort with “ends justify the means” type thinking

Various historical cases of violent fanaticism seem somewhat fairly modeled as sequence thinking gone awry: letting one’s decisions become dominated by a single overriding concern, which then justifies actions that strongly violate many other principles. (For example, justifying extremely damaging activities based on Marxist reasoning.) Cluster thinking is far from a complete defense against such things: the robustness of a perspective (e.g., a Marxist perspective) can itself be overestimated, and furthermore a “regression to normality” can encourage conformism with highly problematic beliefs. However, the basic structure of cluster thinking does set up more hurdles for arguments about “the ends” (large-magnitude but speculative down-the-line outcomes) to justify “the means” (actions whose consequences are nearer and clearer).

I believe that invoking “the ends justify the means” (justifying near and clear harms by pointing to their further-out effects) is sometimes the right thing to do, and is sometimes not. Specifically, I think that the worse the “means,” the more robust (and not just large in claimed magnitude) one’s case for “the ends” ought to be. Cluster thinking seems to accommodate this view more naturally than sequence thinking.

(Related piece by Phil Goetz: Reason as memetic immune disorder)

When uncertainty is high, “unknown unknowns” can dominate the impacts of our actions, and cluster thinking may be better suited to optimizing “unknown unknown” impacts.

Sequence thinking seems, by its nature, to rely on listing the possible outcomes of an action and evaluating the action according to its probability of achieving these outcomes. I find sequence thinking especially problematic when I specifically expect the unexpected, i.e., when I expect the outcome of an action to depend primarily on factors that haven’t occurred to me. And I believe that the sort of outside views that tend to get more weight in cluster thinking are often good predictors of “unknown unknowns.” For example, obeying common-sense morality (“ends don’t justify the means”) heuristics seems often to lead to unexpected good outcomes, and contradicting such morality seems often to lead to unexpected bad outcomes. As another example, expert opinion often seems a strong predictor of “which way the arguments I haven’t thought of yet will point.”

It’s hard to formalize “expecting unknown unknowns to be the main impact of one’s action” in a helpful way within sequence thinking, but it’s a fairly common situation. In particular, when it comes to donations and other altruistic actions, I expect the bulk of the impact to come from unknown unknown factors including flow-through effects.

Broad market efficiency

Another way of thinking about the case for cluster thinking is to consider the dynamics of broad market efficiency. As I stated in that post:

the more efficient a particular market is, the higher the level of intensity and intelligence around finding good opportunities, and therefore the more intelligent and dedicated one will need to be in order to consistently “beat the market.” The most efficient markets can be consistently beaten only by the most talented/dedicated players, while the least efficient ones can be beaten with fairly little in the way of talent and dedication.

When one is considering a topic or action that one knows little about, one should consider the broad market to be highly efficient; therefore, any deviations from the status quo that one’s reasoning calls for are unlikely to be good ideas, regardless of the magnitude of benefit that one’s reasoning ascribes to them. (An amateur stock trader should generally assume his or her opinions about stocks to be ill-founded and to have zero expected value, regardless of how strong the “inside view” argument seems.) By contrast, when one is considering a topic or action that one is relatively well-informed and intelligent about, contradicting “market pricing” is not as much of a concern.

This is a special case of “as robustness falls, the potential weight carried by an argument diminishes – no matter what magnitudes it claims – and regression to normality becomes the stronger consideration.”

Sequence thinking seems to over-encourage “exploiting” as opposed to “exploring” one’s best guesses

I expect this argument to be least compelling to most people, largely because it is difficult for me to draw convincing causality lines and give convincing examples, but to me it is a real argument in favor of cluster thinking. It seems to me that people who rely heavily on sequence thinking have a tendency to arrive at a “best guess” as to what cause/charity/etc. ought to be prioritized, and to focus on taking the actions that are implied by their best guess (“exploiting”) rather than on actions likely to lead to rethinking their best guess (“exploring”). I would guess that this is because:

• To the extent that sequence thinking highlights opportunities for learning, it tends to focus on a small number of parameters that dominate the model, and these parameters are often the least tractable in terms of learning more (for example, the value of space colonization). It thus seems often to encourage continued debate on largely intractable topics. Cluster thinking highlights many consequential areas of uncertainty and promises returns to clearing up any of them, leading to more traction on learning and more reduction in “unknown unknowns” over time.
• Sequence thinking has a tendency to make different options seem to differ more in value, while cluster thinking tends to make it appear as though any high-uncertainty decision is a “close one” that can be modified with more learning. I believe the latter tends to be a more helpful picture.
• Cluster thinking tends to have heavier penalties for uncertainty, due to its feature of not allowing the magnitude of a model parameter to overwhelm adjustments for uncertainty. When people are promoting speculative arguments, having to contend with and persuade “cluster thinkers” seems to cause them to do more investigation, do more improving of their arguments, and generally do more to increase the robustness of their claims.

In the domains GiveWell focuses on, it seems that learning more over time is paramount. We feel that much of the effective altruist community tends to be quicker than we are to dismiss large areas as unworthy of exploration and to focus in on a few areas.

Formal framework reproducing key qualities of cluster thinking

Cluster thinking, despite its seeming inelegance, is in some ways a closer match to what I see as the “idealized” thought process than sequence thinking is. On a separate page, I have attempted to provide a formal framework describing this “idealized” thought process as I see it, and how this framework deals with extreme uncertainty of the kind we often encounter in making decisions about where to give.

According to this framework, formally combining different mental models of the world has a tendency to cap the decision-relevance of highly uncertain lines of reasoning – the same tendency that distinguishes cluster thinking from sequence thinking. For more, see my full writeup on this framework, which I have confined to another page because it is long and highly abstract.

Writeup on modeling extreme model uncertainty

To be clear, in this section when I say “engaging in sequence thinking” I mean “working on generating and improving chains of reasoning along the lines of explicit expected-value calculations,” or more generally, “Trying to capture as many relevant considerations as possible in a single unified model of the world.” Cluster thinking includes giving some consideration and weight to the outcomes of such exercises, but does not include generating them. Many of the advantages I name have to do with the tendency of sequence thinking to underweight, or ignore, “outside views” and crude pattern-matches such as historical patterns and expert opinion, as well as “regression to normality”; sequence thinking can make adjustments for such things, but I generally find its method for doing so unsatisfactory, and feel that its greatest strengths come when it does not involve such adjustments.

Sequence thinking can generate robust conclusions that then inform cluster thinking

There are times when a long chain of reasoning can be constructed that has relatively little uncertainty involved (it may involve many probabilistic calculations, but these probabilities are well-understood and the overall model is robust).

The extreme case of this is in some science and engineering applications, when sequence thinking is all that is needed to reach the right conclusion (I might say cluster thinking “reduces to” sequence thinking in these cases, since the sequence-thinking perspective is so much more robust than all other available perspectives).

A less extreme case is when someone simply puts a great deal of work into doing as much reflection and investigation as they can of the parameters in their model, to the point where they can reasonably be assumed to have relatively little left to learn in the short to medium term. People who have reached such status have, in my opinion, good reason to assign much less uncertainty to their sequence-thinking-generated views and to place much more weight on their conclusions. (Still, even these people should often assign a substantial amount of uncertainty to their views.)

There are many times when I have underestimated the weight I ought to place on a sequence-thinking argument because I underestimated how much work had gone into investigating and reflecting on its parameters. I have been initially resistant to many ideas that I now regard as extremely important, such as the greater cost-effectiveness of developing-world as opposed to developed-world aid, the potential gains to labor mobility, and views of “long-term future” effective altruists on the most worrying global catastrophic risks, all of which appeared to me at first to be based on naïve chains of logic but which I now believe to have been more thoroughly researched – and to have less uncertainty around key parameters – than I had thought.

Sequence thinking is more favorable to generating creative, unconventional, and nonconformist ideas

I often feel that people in the effective altruist community do too little regression to normality, but I believe that most people in the world do far too much. Any thinking style that provides a “regression to normality”-independent way of reaching hypotheses has major advantages.

Sequence thinking provides a way of seeing where a chain of reasoning goes when historical observations, conventional wisdom, expert opinion and other “outside views” are suspended. As such, it can generate the kind of ideas that challenge long-held assumptions and move knowledge forward (the cases I list in the immediately previous section are some smaller-scale examples; many scientific breakthroughs seem to fit in this category as well). Sequence thinking is also generally an important component in the formation of expert opinion (more below), which is usually a major input into cluster thinking.

Sequence thinking is better-suited to transparency, discussion and reflection

I generally find it very hard to formalize and explain what “outside views” I am bringing to a decision, how I am weighing them against each other, and why I have the level of certainty I do in each view. Many of my outside views consist of heuristics (i.e., “actions fitting pattern X don’t turn out well”) that come partly from personal experiences and observations that are difficult to introspect on, and even more difficult to share in ways that others would be able to comprehend and informedly critique them.

Sequence thinking tends to consist of breaking a decision down along lines that are well-suited to communication, often in terms of a chain of causality (e.g., “This action will lead to A, which will lead to B, which will lead to outcome-of-interest C if D and E are also true”). This approach can be clumsy at accommodating certain outside views that don’t necessarily apply to a particular sub-prediction (for example, many heuristics are of the form “actions fitting pattern X don’t turn out well for reasons that are hard to visualize in advance”). However, sequence thinking usually results in a chain of reasoning that can be explicitly laid out, reflected on, and discussed.

Consistent with this, I think the cost-effectiveness analysis we’ve done of top charities has probably added more value in terms of “causing us to reflect on our views, clarify our views and debate our views, thereby highlighting new key questions” than in terms of “marking some top charities as more cost-effective than others.” I have often been pushed, by people who heavily favor sequence thinking, to put more work into clarifying my own views, and I’ve rarely regretted doing so.

Sequence thinking can lead to deeper understanding

Partly because it is better-suited to explicit discussion and reflection, and partly because it tends to focus on chains of causality without deep integration of poorly-understood but empirically observed “outside view” patterns, sequence thinking often seems necessary in order to understand a particular issue very deeply. Understanding an issue deeply, to me, includes (a) being able to make good predictions in radically unfamiliar contexts (thus, not relying on “outside views” that are based on patterns from familiar contexts); (b) matching and surpassing the knowledge of other people, to the point where “broad market efficiency” can be more readily dismissed.

In my view, people who rely heavily on sequence thinking often seem to have inferior understanding of subjects they aren’t familiar with, and to ask naive questions, but as their familiarity increases they eventually reach greater depth of understanding; by contrast, cluster-thinking-reliant people often have reasonable beliefs even when knowing little about a topic, but don’t improve nearly as much with more study. At GiveWell, we often use a great deal of sequence thinking when exploring a topic (less so when coming to a final recommendation), and often feel the need to apologize in advance to the people we interview for asking naïve-seeming questions.

In order to reap this benefit of sequence thinking, one must do a good job stress-testing and challenging one’s understanding, rather than being content with it as it is. This is where the “incentives to investigate” provided by cluster thinking can be crucial, and this is why (as discussed below) my ideal is to switch between the two modes.

Other considerations

Sequence thinking can be a good antidote to scope insensitivity, since it translates different factors into a single framework in which they can be weighed against each other. I do not believe scope insensitivity is the only, or most important, danger in making giving decisions, but I do find sequence thinking extremely valuable in correcting for it.

Many seem to believe that sequence thinking is less prone to various other cognitive biases, and in general that it represents an antidote to the risks of using “intuition” or “system 1.” I am unsure of how legitimate this view is. When making decisions with high levels of uncertainty involved, sequence thinking is (like cluster thinking) dominated by intuition. Many of the most important parameters in one’s model or expected-value calculation must be guessed at, and it often seems possible to reach whatever conclusion one wishes. Sequence thinking often encourages one to implicitly trust one’s intuitions about difficult-to-intuit parameters (e.g., “value of space colonization”) rather than trusting one’s more holistic intuitions about the choice being made – not necessarily an improvement, in my view.

I’ve sometimes observed an intelligent cluster thinker, when asked why s/he believes something, give a single rather unconvincing “outside view” related reason. I’ve suspected, in some such cases, that the person is actually processing a large number of different “outside views” in a way that is difficult to introspect on, and being unable to cite the full set of perspectives with weights, returns a single perspective with relatively (but not absolutely) high weight. I believe this dynamic sometimes leads sequence thinkers to underestimate cluster thinkers.

One of my hopes for this piece is to help people better understand cluster thinking, and in particular, how one can continue to make progress in a discussion even after a seemingly argument-stopping comment like “I see no problem with your reasoning, but I’m not placing much weight on it anyway” is made.

In such a situation, it is important to ask not just whether there are explicit problems with one’s argument, but how much uncertainty there is in one’s argument (even if such uncertainty doesn’t clearly skew the calculation in one direction or another) and whether other arguments, using substantially different mental models, give the same conclusion. When engaging with cluster thinking, improving one’s justification of a probability or other parameter – even if it has already been agreed to by both parties as a “best guess” – has value; citing unrelated heuristics and patterns has value as well.

To give an example, many people are aware of the basic argument that donations can do more good when targeting the developing-world poor rather than the developed-world poor: the developing-world poor have substantially worse incomes and living conditions, and the interventions charities carry out are commonly claimed to be substantially cheaper on per-person or per-life-saved basis. However, many (including myself) take these arguments more seriously on learning things like “people I respect mostly agree with this conclusion”; “developing-world charities’ activities are generally more robustly evidence-supported, in addition to cheaper”; “thorough, skeptical versions of ‘cost per life saved’ estimates are worse than the figures touted by charities, but still impressive”; “differences in wealth are so pronounced that “hunger” is defined completely differently for the U.S. vs. developing countries“; “aid agencies were behind undisputed major achievements such as the eradication of smallpox”; etc. The function of such findings isn’t necessarily to address specific objections to the basic argument, but rather to put its claims on more solid footing – to improve the robustness of the argument.

However, there are also times in which I let sequence thinking dominate my decisions (not just my investigations), for the following reasons.

One of the great strengths of sequence thinking is its ability to generate ideas that contradict conventional wisdom and easily observable patterns, yet have some compelling logic of their own. For brevity, I will call these “novel ideas” (though a key aspect of such ideas is that they are not just “different” but also “promising”). I believe that novel ideas are usually flawed, but often contain some important insight. Because the value of new ideas is high, promoting novel ideas – in a way that is likely to lead to stress-testing them, refining them, and ultimately bringing about more widespread recognition of their positive aspects – has significant positive expected value. At the same time, a given novel idea is unlikely to be valid in its current form, and quietly acting on it (when not connected to “promoting” it in the marketplace of ideas, leading to its refinement and/or widespread adoption) may have negative expected value.

One example of this “novel ideas” dynamic is the charities recommended by GiveWell in 2006 or 2007: GiveWell at that time had a philosophy and methodology with important advantages over other resources, but it was also in a relatively primitive form and needed a great deal of work. Supporting GiveWell’s recommendations of that time – in a way that could be attributed to GiveWell – led to increasing attention and influence for GiveWell, which was evolving quickly and becoming a more sophisticated and influential resource. However, if not for GiveWell’s ongoing evolution, supporting its recommended charities would not have had the sort of expected value that it naively appeared to (according to our over-optimistic “cost per life saved” figures of the time). (Note that this paragraph is intended to give an example of the “novel ideas” dynamic I described, but does not fit the themes of the post otherwise. Our recommendations weren’t purely a product of sequence thinking but rather of a combination of sequence and cluster thinking.)

For me, a basic rule of thumb is that it’s worth making some degree of bet on novel ideas, even when the ideas are likely flawed, when it’s the kind of bet that (a) facilitates the stress-testing, refinement, and growing influence of these ideas; (b) does not interfere with other, more promising bets on other novel ideas. So it makes sense to start, run, or support an organization based on a promising but (because dependent on sequence thinking, and in tension with various outside views) likely flawed idea … if (a) the organization is well-suited to learning, refining, and stress-testing its ideas and growing its influence over time; (b) starting or supporting the organization does not interfere with one’s support of other, more promising novel ideas. It makes sense to do so even when cluster thinking suggests that the novel idea’s conclusions are incorrect, to the extent that quite literal endorsement of the novel idea would be “wrong.”

When we started GiveWell, I believed that we were likely wrong about many of the things that seemed to us from an inside, sequence-thinking view to be true, but that it was worth acting on these things anyway, because of the above dynamic. (I am referring more to our theories about how we could influence donors and have impact than to our theories about which charities were best, which we tried to make as robust as we could, while realizing that they were still quite uncertain.) We believed we were onto some underappreciated truth, but that we didn’t yet know what it was, and were “provisionally accepting” our own novel ideas because we could afford to do so without jeopardizing our overall careers and because they seemed to be the novel ideas most worth making this sort of bet on. We expected our ideas to evolve, and rather than taking them as true we tried to stress-test them by examining as many different angles as we could (for example, visiting a recommended charity’s work in the field even though we couldn’t say in advance which aspect of our views this would affect). There were other novel ideas that we found interesting as well, but incorporating them too deeply into our work (or personal lives) would have interfered with our ability to participate in this dynamic.

The above line of argument justifies behavior that can seem otherwise strange and self-contradictory. For example, it can justify advocating and acting to some degree on a novel idea, while not living one’s life fully consistently with this idea (e.g., working to promote Peter Singer’s ideas about the case for giving more generously, while not actually giving as much as his ideas would literally imply one should). When considering possible actions including “avoiding factory-farmed meat,” “giving to the most apparently cost-effective charity,” etc., I am always asking not only “Does this idea seem valid to me?” but “Am I acting on this idea in a way that promotes it and facilitates its evolution, and does not interfere with my promotion of other more promising ideas?” As such, I tend to change my own behavior enough to reap a good portion of the benefits of supporting/promoting an idea but not as much as literal acceptance of the idea would imply. I have a baseline level of stability and conservatism in the way I live my life, which my bets on novel ideas are layered on top of in a way that fits well within my risk tolerance.

Promoting a sequence-thinking-based idea in a cluster-thinking-based world leads to examining the idea from many angles, looking for many unrelated (or minimally related) arguments in its favor, and generally working toward positive evolution of the idea. The ideal, from my perspective, is to use cluster thinking to evaluate the ultimate likely validity of ideas, while retaining one’s ability to (without undue risk) promote and get excited about sequence-thinking-generated ideas that may eventually change the world.

For one with few resources for idea promotion and exploration, this may mean picking a very small number of bets. For one who expects to influence substantial resources – as GiveWell currently does – it is rational to simultaneously support/promote work in multiple different causes, each of which could be promising under certain assumptions and parameters (regarding how much value we should estimate in the far future, how much suffering we should ascribe to animals, etc.), even if the assumptions and parameters that would support one cause contradict those that would support another. When choosing between causes to support, cluster thinking – rather than choosing one’s best-guess for each parameter and going from there – is called for.