What beats intuition when it comes to doing good? (with Marcus Davis)

Mar 27, 2026 Episode Page ↗
Overview

Marcus A. Davis, co-founder and CEO of Rethink Priorities, discusses comparing diverse charitable outcomes, addressing moral uncertainty, and applying rigorous modeling to philanthropic decisions to maximize positive impact.

At a Glance
10 Insights
1h 16m Duration
13 Topics
11 Concepts

Deep Dive Analysis

Comparing Different Charitable Outcomes

Cost-Effectiveness in Rich vs. Poor Countries

Arguments Against Comparing Charities

Plausibility of Moral Theories

Dealing with Moral Uncertainty

Aggregating Across Diverse Moral Views

Applying Explicit Models to Big Decisions

Common Pitfalls of Explicit Models

Balancing Simple vs. Complex Models

Modeling Personal Life Decisions

Motivation for Effective Altruism

Inefficiencies and Opportunities in Charity

Final Advice on Improving the World

Commensurability of Outcomes

The ability to compare and evaluate different types of charitable or moral outcomes against each other, even if they appear vastly different (e.g., saving lives vs. funding art).

Diminishing Marginal Returns

The principle that as an individual or entity acquires more of a good (like money), the additional utility or benefit gained from each subsequent unit of that good tends to decrease.

Value Systems

The set of moral or ethical principles and beliefs that guide an individual's or group's judgments about what is good, right, or important.

Reflective Equilibrium

A state of coherence in moral philosophy where one's general moral principles and specific moral judgments are brought into alignment through a process of mutual adjustment and revision.

Moral Uncertainty

The state of not knowing which moral theory or set of values is correct, leading to difficulty in determining the 'right' course of action.

Aggregating Moral Views

The philosophical and practical challenge of combining different moral theories or perspectives, each with varying degrees of credence, to arrive at a unified decision or allocation of resources.

Empirical Uncertainty

Uncertainty related to factual outcomes and the real-world effects of interventions, such as whether a charity's program actually achieves its stated goals.

Normative Uncertainty

Uncertainty about what values truly matter and how much they matter, such as the relative importance of saving a life versus providing a positive experience.

Metanoronative Uncertainty

Uncertainty about how to make decisions when faced with both empirical and normative uncertainty, specifically how to aggregate different moral views or handle risk.

Explicit Models

Decision-making tools, often quantitative (like spreadsheets), that clearly lay out assumptions, inputs, and calculations, making the decision process transparent and auditable.

Implicit Models

Decision-making processes that rely on intuition, gut feelings, or non-transparent mental calculations, where the underlying assumptions and logic are not explicitly stated or examined.

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Can radically different forms of good be compared?

Yes, even if two charitable outcomes seem vastly different, pushing extreme examples often reveals that people can agree on which outcome is more valuable, making comparison possible.

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Why is it cheaper to help poorer people with the same problem?

Services and labor cost significantly less in low-income countries, and governments in richer countries often address problems like lead exposure, meaning charitable interventions can achieve greater impact per dollar in poorer regions.

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How should one deal with the argument that comparing charities leads to a world without art?

This argument assumes morality is maximally demanding, which most moral theories do not claim; instead, they suggest dedicating a portion of resources to doing good, not eliminating all other valuable activities.

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How do you assess the plausibility of moral theories?

It's a complex process involving investigating logical arguments, premises, and implications, balancing argument strength with intuitive feelings, but ultimately, humility is key as definitive conclusions are hard to reach.

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Does the idea of objective moral truth matter for effective giving?

While the existence of objective moral truth is a contested philosophical question, the speaker suggests that given the weak evidence in philosophy, one should not be overly certain and instead consider theories that emphasize the importance of doing good.

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How can one make decisions when uncertain about what "the good" is?

One approach is to aggregate across different moral views by assigning credences to each theory and combining their recommendations, acknowledging that different aggregation methods can lead to different outcomes.

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How should individuals approach charitable giving given these complexities?

For individual donations, especially smaller amounts, it's often best to use rigorous charity evaluators like GiveWell, as they identify highly efficient and evidence-based interventions.

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Should one use explicit models for big decisions or rely on intuition?

For big, complex decisions, explicit models are generally preferred because they make assumptions transparent and allow for systematic analysis, which is often more reliable than intuition in complex scenarios.

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How to avoid over-trusting explicit models?

Be mindful of common failure points such as treating point estimates as certain, ignoring model uncertainty, and not questioning assumptions; instead, use distributions for uncertain inputs and continuously challenge the model's premises.

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Should models start simple or complex?

It's best to start with the simplest model that captures the core phenomenon and then gradually add complexity as needed, as simple models are easier to introspect on and identify issues.

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Should complex models be used for everyday life decisions?

While models can be useful for some personal decisions (like buying a car or renting an apartment), for highly subjective or dynamic personal choices (like having a child), they may not be as applicable or straightforward.

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Why devote one's life to effective altruism?

The motivation stems from a belief that it's possible to make the world better, a sense of owing it to past generations who improved the world, and the personal satisfaction of working on impactful problems with smart, caring people.

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Is helping the world effectively really difficult?

While it's harder than pursuing immediate personal happiness and involves navigating empirical and philosophical uncertainties, it's not incomprehensibly complex, and significant progress can be made through rigorous analysis.

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Are there more opportunities or inefficiencies in charity work compared to for-profit?

Yes, due to fewer financial incentives in the charitable sector, there's less competition, allowing individuals and organizations who apply rigor and analysis to make a disproportionately large positive difference.

1. Prioritize Location for Impact

When addressing a specific problem like lead poisoning, focus on low-income countries where interventions are significantly cheaper and more cost-effective, yielding greater impact per dollar.

2. Don’t Avoid Comparing Good

Reject the notion that radically different forms of good (e.g., art performance vs. saving lives) are incomparable; pushing extreme examples often reveals that most people agree some outcomes are clearly more valuable.

3. Be Humble About Moral Theories

Recognize that philosophical evidence for specific moral theories is weak and contested, so avoid being overly certain about one particular view.

4. Aggregate Across Moral Views

For large-scale giving (e.g., foundations), combine different moral theories (e.g., utilitarianism, deontology, virtue ethics) using weighted averages or proportional splitting to account for moral uncertainty.

5. Use Explicit Models for Big Decisions

For complex, high-stakes decisions, build explicit models (like spreadsheets) to make assumptions transparent and track interactions, rather than relying solely on vague intuition.

6. Model Uncertainty, Not Point Estimates

In explicit models, represent uncertain inputs as distributions (e.g., using Monte Carlo simulations) rather than single point estimates to avoid underestimating the true range of possible outcomes.

7. Start Simple, Then Refine Models

Begin with the simplest model that captures the core phenomenon, then gradually add complexity and additional factors as the stakes increase or as you identify critical areas for deeper analysis.

8. Integrate Judgment with Models

Do not blindly follow model outputs; instead, use models to clarify thinking, identify disagreements, and inform judgment, always considering what might be missing or where assumptions could be flawed.

9. Leverage Charity Evaluators for Donations

For individual donations, especially smaller amounts, use highly rigorous charity evaluators like GiveWell to identify evidence-based, efficient charities that maximize impact.

10. Differentiate Personal vs. Altruistic Decisions

Recognize that the moral constraints and decision-making approaches for personal life choices (e.g., hobbies, family) may differ from those for large-scale altruistic giving.

not all value systems are equally plausible.

Marcus A. Davis

Once you admit there's some case it doesn't work, then we're arguing about like, why is that the case?

Marcus A. Davis

just because someone can like say, yeah, that's just my belief that, uh, these things are incomparable. That doesn't mean like I have to like take that seriously.

Marcus A. Davis

I don't think you should be that certain one way or another about this question.

Marcus A. Davis

all models are wrong, some are useful.

Marcus A. Davis

Progress is possible. Even when hard questions in charity, in thinking about how to make the world better, you can do better than your intuition.

Marcus A. Davis
80th to 95th percentile
US income percentile Average income in the United States compared to the global income distribution.
$3 a day
Income for global poor Approximate daily income for people in the global poor.
a couple of thousand dollars
Cost to save a life Estimated cost to save a life through highly effective interventions like insecticide-treated bed nets.
2% to 10%
Recommended donation percentage Suggested portion of income to dedicate to charitable giving.
30, 30, 30
Philosophical disagreement on moral theories Approximate percentage split among philosophers for virtue ethics, deontology, and consequentialism.
around the year 2000
Start of moral aggregation field The approximate time when the field of aggregating across different moral views in philosophy began.
10X or 100X
Charity efficiency difference The potential difference in efficiency between charities, even those working on the same problem.
more than a decade
GiveWell's bed net recommendation duration The length of time GiveWell has been recommending bed nets as a highly effective intervention.
several hundred thousand dollars
Value of small improvement for large foundation The potential value of a small improvement in decision-making for a foundation giving away $10 million a year.
tens of millions, possibly hundreds of millions of dollars
Impact of Rethink Priorities' work The amount of grants that Rethink Priorities' work has directly led to improving.