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Decision Frameworks

Actuarial Method

Actuarial methods, which rely on statistical algorithms, often outperform clinical judgment in predicting human behavior, but they have limitations and require careful application, periodic reevaluation, and quality controls.

THE IDEA

APPLICATION OF ACTUARIAL METHODS: LIMITS, BENEFITS AND IMPLICATIONS

The research reviewed in this article indicates that a properly developed and applied actuarial method is likely to help in diagnosing and predicting human behavior as well or better than the clinical method even when the clinical judge has access to equal or greater amounts of information. Research demonstrating the general superiority of actuarial approaches, however, should be tempered by an awareness of limitations and needed quality controls.

First, although surpassing clinical methods, actuarial procedures are far from infallible, sometimes achieving only modest results. Second, even a specific procedure that proves successful in one setting should be periodically reevaluated within that setting and should not be applied to new settings mindlessly. Although theory and research suggest that the choice of predictive variables is often more important than their weighting, statistical techniques can be used to yield weights that optimize a procedure’s accuracy when it is applied to new cases drawn from the same population. Moreover, accuracy can be easily monitored as predictions are made, and methods modified or improved to meet changes in settings and populations. Finally, efforts can be made to test whether new variables enhance accuracy.

When developed and used appropriately, actuarial procedures can provide various benefits. Even when actuarial methods merely equal the accuracy of clinical methods, they may save considerable time and expense. For example, each year millions of dollars and many hours of clinicians’ valuable time are spent attempting to predict violent behavior. Actuarial prediction of violence is far less expensive and would free time for more productive activities, such as meeting unfulfilled therapeutic needs. When actuarial methods are not used as the sole basis for decisions, they can still serve to screen out candidates or options that would never be chosen after more prolonged consideration.

When actuarial methods prove more accurate than clinical judgment the benefits to individuals and society are apparent. Much would be gained, for example, by increased accuracy in the prediction of violent behavior and parole violation, the diagnosis of disorder and the identification of effective treatment. Additionally, more objective determination of limits in knowledge or predictive power can prevent inadvertent harm. Should a confident but incorrect clinical diagnosis of Alzheimer’s disease be replaced by a far more cautious statement, or even better by the correct conclusion, we would avoid much unnecessary human misery.

Actuarial methods are explicit, in contrast to clinical judgment, which rests on mental processes that are often difficult to specify. Explicit procedures facilitate informed criticism and are freely available to other members of the scientific community who might wish to replicate or extend research.

Finally, actuarial methods – at least within the domains discussed in this article – reveal the upper bounds in our current capacities to predict human behavior. An awareness of the modest results that are often achieved by even the best available methods can help to counter unrealistic faith in our predictive powers and our understanding of human behavior. It may well be worth exchanging inflated beliefs for an unsettling sobriety, if the result is an openness to new approaches and variables that ultimately increase our explanatory and predictive powers.

The argument that actuarial procedures are not available for many important clinical decisions does not explain failure to use existent methods and overlooks the ease with which such procedures can be developed for use in special settings. Even lacking any outcome information, it is possible to construct models of judges that will likely surpass their accuracy (Dawes & Corrigan, 1974; Goldberg, 1970). What is needed is the development of actuarial methods and a measurement assurance program that maintains control over both judgment strategies so that their operating characteristics in the field are known and an informed choice of procedure is possible. Dismissing the scientific evidence or lamenting the lack of available methods will prove much less productive than taking on the needed work.

When to use it

  • Predicting parole violations in criminal justice to inform release decisions

  • Diagnosing mental disorders in clinical settings to improve treatment outcomes

  • Identifying effective treatment interventions by analyzing data from past cases

  • Screening candidates for high-stakes decisions, such as hiring or admission, to reduce bias and improve accuracy

FROM THEORY TO EVERYDAY LIFE

In practice

01

Predicting Violent Behavior

The authors note the money and clinical time devoted to predicting violence and propose that actuarial methods could reduce that burden. Comparable accuracy at lower cost could free resources for therapeutic work; the excerpt does not quantify realized savings.

02

Diagnosing Alzheimer's Disease

Replacing a confident but incorrect clinical diagnosis of Alzheimer's with a cautious actuarial statement or the correct conclusion can avoid unnecessary human misery.

✳

A MOMENT TO REFLECT

Put it to work.

  1. When making predictions about human behavior, do you rely on intuitive judgment or statistical models? Why?

  2. How can actuarial methods be adapted or improved for a specific setting you are familiar with?

  3. What ethical considerations arise from using actuarial methods in high-stakes decisions like parole or diagnosis?

Often confused with

References

  1. Dawes & Corrigan (1974) — Dawes, R. M. & Corrigan, B. (1974)
  2. Goldberg (1970) — Goldberg, L. R. (1970)

CONTINUE THE CONVERSATION

Source texts

  • Heuristics and Biases The Psychology of Intuitive Judgment (Thomas Gilovich, Dale Griffin, Daniel Kahneman)
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