How Do Classifiers Induce Agents To Invest Effort Strategically?
Kleinberg, Jon, Raghavan, Manish
One of the fundamental insights in the economics of information is the way in which assessing people (students, job applicants, employees) can serve two purposes simultaneously: it can identify the strongest performers, and it can also motivate people to invest effort in improving their performance [29]. This principle has only grown in importance with the rise in algorithmic methods for predicting individual performance across a wide range of domains, including education, employment, and finance. Akey challenge is that we do not generally have access to the true underlying properties that we need for an assessment; rather, they are encoded by an intermediate layer of features, so that the true properties determine the features, and the features then determine our assessment. Standardized testing in education is a canonical example, in which a test score serves as a proxy feature for a student's level of learning, mastery of material, and perhaps other properties we are seeking to evaluate as well. In this case, as in many others, the quantity we wish to measure is unobservable, or at the very least, difficult to accurately measure; the observed feature is a construct interposed between the decision rule and the intended quantity. This role that features play, as a kind of necessary interface between the underlying attributes and the decisions that depend on them, leads to a number of challenges. In particular, when an individual invests effort to perform better on a measure designed by an evaluator, there is a basic tension between effort invested to raise the true underlying attributes that the evaluator cares about, and effort that may serve to improve the proxy features without actually improving the underlying attributes. This tension appears in many contexts -- it is the problem of gaming the evaluation rule, and it underlies the formulation of Goodhart's Law, widely known in the economics literature, which states that once a proxy measure becomes a goal in itself, it is no longer a useful measure [17].
Dec-3-2018
- Country:
- North America > United States (0.14)
- Genre:
- Research Report (0.50)
- Industry:
- Education > Assessment & Standards > Student Performance (0.48)
- Technology: