The CURVE Sequence

Oct 10, 2023 by Bob Fischer

Causes and Uncertainty: Rethinking Value in Expectation

This is the home of Rethink Priorities' Worldview Investigations Team's CURVE Sequence. Its aim is to consider some alternatives to expected value maximization (EVM) for cause prioritization and, at the same time, to explore the practical implications of a commitment to expected value maximization.

We start by noting that some plausible moral theories reject EVM entirely. Our report on contractualism and resource allocation illustrates how we might set priorities if we were to measure effectiveness in something like “strength-adjusted moral claims addressed per dollar spent.”
Our report on risk and animal examines several ways of incorporating risk sensitivity into the comparisons between interventions to help numerous animals with a relatively low probability of sentience (such as insects) and less numerous animals of likely or all-but-certain sentience (such as chickens and humans). We show that while one kind of risk aversion makes us more inclined to help insects, two other kinds of risk aversion suggest the opposite.
We generalize this discussion of risk in our report on risk-aversion and cause prioritization. Here, we model the cost-effectiveness of different cause areas in light of several formal models of risk aversion, evaluating how various risk attitudes affect value comparisons and how risk attitudes interact with one another.
Our report on the common sense case for spending on x-risk mitigation consider a simple model for assessing x-risk mitigation efforts where the value is restricted to the next few generations. We show that, given plausible assumptions, x-risk may not be orders of magnitude better than our best funding opportunities in other causes, especially when evaluated under non-EVM risk attitudes.
We then explore a more complicated hypothesis about the future, the so-called “time of perils” (TOP) hypothesis, that is commonly used to claim that x-risk is robustly more valuable than other causes. We delineate a number of assumptions that go into the TOP-based case for focusing on x-risk and highlight some of the reasons to be uncertain about them.
We investigate the value of existential risk mitigation efforts under different risk scenarios, different lengths of time during which risk is reduced, and a range of population growth cases. This report shows that the value of x-risk work varies considerably depending on the scenario in question and that value is only astronomical under a select few assumptions.
In our report on uncertainty over time and Bayesian updating, we note the difficulty of comparing estimates from models with wildly different levels of uncertainty or ambiguity. We provide an empirical estimate of how uncertainty increases as time passes, showing how a Bayesian may put decreasing weight on longer-term estimates.
We present a cross-cause cost-effectiveness model (CCM) to assess the value of different kinds of interventions and research projects conditional on a wide range of assumptions. This resource allows users to specify distributions over possible values of parameters and see the corresponding distributions of results.
Finally, we consider how Rethink Priorities should make decisions.

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Causes and Uncertainty: Rethinking Value in Expectation

Bob Fischer·3y ago·18m read

Causes and Uncertainty: Rethinking Value in Expectation

We start by noting that some plausible moral theories reject EVM entirely. Our report on contractualism and resource allocation illustrates how we might set priorities if we were to measure effectiveness in something like “strength-adjusted moral claims addressed per dollar spent.”

Our report on risk and animal examines several ways of incorporating risk sensitivity into the comparisons between interventions to help numerous animals with a relatively low probability of sentience (such as insects) and less numerous animals of likely or all-but-certain sentience (such as chickens and humans). We show that while one kind of risk aversion makes us more inclined to help insects, two other kinds of risk aversion suggest the opposite.

We generalize this discussion of risk in our report on risk-aversion and cause prioritization. Here, we model the cost-effectiveness of different cause areas in light of several formal models of risk aversion, evaluating how various risk attitudes affect value comparisons and how risk attitudes interact with one another.

Our report on the common sense case for spending on x-risk mitigation consider a simple model for assessing x-risk mitigation efforts where the value is restricted to the next few generations. We show that, given plausible assumptions, x-risk may not be orders of magnitude better than our best funding opportunities in other causes, especially when evaluated under non-EVM risk attitudes.

We then explore a more complicated hypothesis about the future, the so-called “time of perils” (TOP) hypothesis, that is commonly used to claim that x-risk is robustly more valuable than other causes. We delineate a number of assumptions that go into the TOP-based case for focusing on x-risk and highlight some of the reasons to be uncertain about them.

We investigate the value of existential risk mitigation efforts under different risk scenarios, different lengths of time during which risk is reduced, and a range of population growth cases. This report shows that the value of x-risk work varies considerably depending on the scenario in question and that value is only astronomical under a select few assumptions.

In our report on uncertainty over time and Bayesian updating, we note the difficulty of comparing estimates from models with wildly different levels of uncertainty or ambiguity. We provide an empirical estimate of how uncertainty increases as time passes, showing how a Bayesian may put decreasing weight on longer-term estimates.

We present a cross-cause cost-effectiveness model (CCM) to assess the value of different kinds of interventions and research projects conditional on a wide range of assumptions. This resource allows users to specify distributions over possible values of parameters and see the corresponding distributions of results.

Finally, we consider how Rethink Priorities should make decisions.