Learning Through Noticing: Theory and Evidence from a Field Experiment

Rema  Hanna; Sendhil  Mullainathan; Joshua  Schwartzstein

Article | Quarterly Journal of Economics

Learning Through Noticing: Theory and Evidence from a Field Experiment

by Rema Hanna, Sendhil Mullainathan and Joshua Schwartzstein

Abstract

We consider a model of technological learning under which people "learn through noticing": they choose which input dimensions to attend to and subsequently learn about from available data. Using this model, we show how people with a great deal of experience may persistently be off the production frontier because they fail to notice important features of the data they possess. We also develop predictions on when these learning failures are likely to occur, as well as on the types of interventions that can help people learn. We test the model's predictions in a field experiment with seaweed farmers. The survey data reveal that these farmers do not attend to pod size, a particular input dimension. Experimental trials suggest that farmers are particularly far from optimizing this dimension. Furthermore, consistent with the model, we find that simply having access to the experimental data does not induce learning. Instead, behavioral changes occur only after the farmers are presented with summaries that highlight previously unattended-to relationships in the data.

Keywords: Perception; Behavior; Learning;

Format: Print

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Citation:

Hanna, Rema, Sendhil Mullainathan, and Joshua Schwartzstein. "Learning Through Noticing: Theory and Evidence from a Field Experiment." Quarterly Journal of Economics 129, no. 3 (August 2014): 1311–1353. (Online Appendix.)

We present a framework for analyzing “model persuasion,” where persuaders influence people’s beliefs by proposing models (likelihood functions) that suggest how to organize past data (e.g., on investment performance) to make predictions (e.g., about future returns). Receivers are assumed to find models more compelling when they better explain the past. A key tradeoff persuaders face is that models that better fit the data induce less movement in receivers’ beliefs. Model persuasion sometimes makes the receiver worse off than if he interprets data through the lens of a default model. The receiver is most misled by persuasion when there is a lot of publicly available data that is open to interpretation and exhibits randomness, as this gives the persuader “wiggle room” to highlight false patterns. Even when the receiver is exposed to the true model, the wrong model often wins because it better fits the past. Competition more broadly pushes persuaders towards overfitting available data and as a result tends to neutralize the data by leading receivers to view it as unsurprising. With multiple receivers, a persuader is more effective when receivers share similar priors and default interpretations. We illustrate with examples from finance, politics, and law.

Chapter | Handbook of Behavioral Economics | 2019

Behavioral Economics and Health-Care Markets

Amitabh Chandra, Benjamin Handel and Joshua Schwartzstein

This chapter summarizes research in behavioral health economics, focusing on insurance markets and product markets in health care. We argue that the prevalence of choice difficulties and biases leading to mistakes in these markets establish a special place for them in economic analysis. In addition, we argue that while the behavioral health-economics literature has done a better job documenting consumer-choice mistakes in insurance and treatment choices than explaining why those mistakes occur, it is clear that we should not ignore these mistakes in our analyses. We document evidence showing that consumers leave lots of money on the table in their insurance-plan choices, sometimes thousands of dollars. This is true both when consumers make active choices (e.g., they do not have a default plan) and when they make passive choices (e.g., they have a default plan). We discuss the implications of this body of work for the design and regulation of insurance markets, including the interaction between consumer choice difficulties or biases and adverse selection. We then document evidence on consumer mistakes in health-care utilization and treatment choices, especially in response to changes in prices such as copayments and deductibles. We show how choice difficulties or biases may lead patients to respond to such increases in patient cost-sharing by reducing demand for high-value care, muddying the traditional argument that the price elasticity of demand for medical care meaningfully captures the degree of moral hazard. We conclude with directions for future research.

Keywords: Behavioral economics; Consumer Behavior; Economics; Health Care and Treatment; Insurance; Markets;

Citation:

Chandra, Amitabh, Benjamin Handel, and Joshua Schwartzstein. "Behavioral Economics and Health-Care Markets." In Handbook of Behavioral Economics: Foundations and Applications 2, edited by B. Douglas Bernheim, Stefano DellaVigna, and David Laibson. Amsterdam: Elsevier/North-Holland, 2019. View Details

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A common critique of models of mistaken beliefs is that people should recognize their error after observations they thought were unlikely. This paper develops a framework for assessing when a given error is likely to be discovered, in the sense that the error-maker will deem her mistaken theory implausible. The central premise of our approach is that people channel their attention through the lens of their mistaken theory, meaning a person may ignore or discard information her mistaken theory leads her to consider unimportant. We propose solution concepts embedding such channeled attention that predict when a mistaken theory will persist in the long run even with negligible costs of attention, and we use this framework to study the “attentional stability” of common errors and psychological biases. While many costly errors are prone to persist, in some situations a person will recognize her mistakes via “incidental learning”: when the data she values, given her mistaken theory, happen to also tell her how unlikely her theory is. We investigate which combinations of errors, situations, and preferences tend to induce such incidental learning vs. factors that render erroneous beliefs stable. We show, for example, that a person may never realize her self-control problem even when it leads to damaging behavior and may never notice the correlation in others’ advice even when that failure leads her to follow repetitive advice too much. More generally, we show that for every error there exists an environment where the error persists and is costly. Uncertainty about the optimal action paves the way for incidental learning, while being dogmatic creates a barrier.

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Learning Through Noticing: Theory and Evidence from a Field Experiment

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