Lost in Transmission
with Shakked Noy and Chris Roth
[abstract]
[pdf]
[latest version: March 12, 2024]
How does word-of-mouth transmission distort economic information? We pay participants to listen to audio recordings containing economic forecasts and accurately transmit the information through voice messages. Other participants listen to an original or a transmitted recording before stating incentivized beliefs. Across various transmitter incentive schemes, a forecast's reliability is lost in transmission at a far higher rate than the forecast’s level. Reliable and unreliable information, once filtered through transmission, impact listener beliefs similarly. Mechanism experiments show that information about reliability is not perceived as less relevant or harder to transmit, but is less likely to come to mind during transmission.
Explanations
with Chris Roth and Constantin Schesch
[abstract]
[pdf]
[latest version: April 12, 2024]
When people exchange ideas, both truths and falsehoods can proliferate. We study the role of explanations for the spread of truths and falsehoods in 15 financial decision tasks. We pay participants to record the reasoning behind each of their answers, providing over 6,900 unique verbal explanations in total. A separate group of participants either only observe one orator’s choice or additionally listen to their explanation before making their own choice. Listening to explanations strongly improves aggregate accuracy. This effect is asymmetric: explanations enable the spread of truths, but do not curb the contagion of falsehoods. To study mechanisms, we extract every single argument provided in the explanations alongside a large collection of speech features, revealing the nature of financial reasoning on each topic. Explanations for truths exhibit a significantly richer message space and higher argument quality than explanations for falsehoods.
Complexity and Hyperbolic Discounting
with Benjamin Enke and Ryan Oprea
[abstract]
[pdf]
[latest version: Dec 14, 2023]
A large literature shows that people discount financial rewards hyperbolically instead of exponentially. While discounting of money has been questioned as a measure of time preferences, it continues to be highly relevant in empirical practice and predicts a wide range of real-world behaviors, creating a need to understand what generates the hyperbolic pattern. We provide evidence that hyperbolic discounting reflects mistakes that are driven by the complexity of evaluating delayed payoffs. In particular, we document that hyperbolicity (i) is strongly associated with choice inconsistency and cognitive uncertainty, (ii) increases in overt complexity manipulations and (iii) arises nearly identically in computationally similar tasks that involve no actual payoff delays. Our results suggest that even if people had exponential dis- count functions, complexity-driven mistakes would cause them to make hyperbolic choices. We examine which experimental techniques to estimate present bias are (not) confounded by complexity.
Stories, Statistics, and Memory
Revise & Resubmit at The Quarterly Journal of Economics
with Chris Roth and Florian Zimmermann
[abstract]
[pdf] [latest version: October 2, 2023]
For most decisions, we encounter relevant information over the course of days, months or years. We consume such information in various forms, including quantitative data about collections of observations – statistics – and qualitative content about individual instances – stories. This paper proposes that the information type – story versus statistic – shapes selective memory. In controlled experiments, we document a pronounced story-statistic gap in memory: the average impact of stories on beliefs fades by 33% over the course of a day, but by 73% for statistics. Guided by a model of selective memory, we disentangle different mechanisms underlying the story-statistic gap. The similarity between relevant information in memory and the prompt drives the gap. Irrelevant information that is similar to the prompt, on the other hand, impedes successful recall.
The Complexity of Economic Decisions
with Xavier Gabaix
[abstract]
[pdf] [latest version: July 10, 2023]
We propose a theory of the complexity of economic decisions. Leveraging a macroeconomic framework of production functions, we conceptualize the mind as a cognitive economy, where a task’s complexity is determined by its composition of cognitive operations. Complexity emerges as the inverse of the total factor productivity of thinking about a task. It increases in the number of importance-weighted components and decreases in the degree to which the effect of one or a few components on the optimal action dominates. Higher complexity generates larger decision errors and behavioral attenuation to variation in problem parameters. The model applies both to continuous and discrete choice. We develop a theory-guided experimental methodology for measuring subjective perceptions of complexity that is simple and portable. A series of experiments test and confirm the central predictions of our model for perceptions of complexity, behavioral attenuation, and decision errors. We apply our framework to core economic decision domains, including the complexity of static consumption choice with one or several interacting goods, consumption over time, the tax system, forecasting, and discrete choice between goods or lotteries. These applications demonstrate how our approach to complexity can be used in empirical and theoretical work.
"De Gustibus" and Disputes about Reference Dependence
with Pol Campos-Mercade, Lorenz Goette, Alex Kellogg and Charles Sprenger
[abstract]
[pdf]
[latest version: November 10, 2023]
Existing tests of reference-dependent preferences assume universal loss aversion. This paper examines the implications of heterogeneity in gain-loss attitudes for such tests. In experiments on labor supply and exchange behavior we measure gain-loss attitudes and then study a canonical treatment effect that distinguishes different models of reference dependence. We document substantial heterogeneity in gain-loss attitudes and evidence against universal loss aversion. We then document heterogeneous treatment effects over gain-loss attitudes consistent with formulations of expectations-based reference points. Assuming homogeneous preferences would lead to different and possibly incorrect conclusions in these tests. Our findings provide foundational support for reference points derived from expectations, and reconcile inconsistencies across prior exercises.
Intertemporal Altruism
Forthcoming in The American Economic Journal: Microeconomics
with Felix Chopra and Armin Falk
[abstract]
[pdf] [latest version: August 13, 2022]
Most prosocial decisions involve intertemporal tradeoffs. Yet, the timing of prosocial utility flows is ambiguous and bypassed by most models of other-regarding preferences. We study the behavioral implications of the time structure of prosocial utility, leveraging a conceptual distinction between consequence-dated and choice-dated utility flows. We conduct a high-stakes donation experiment that comprehensively characterizes discounting behavior in self-other tradeoffs and allows us to identify different prosocial motives from their distinct time profiles. Our data can only be explained by a combination of choice- and consequence- dated prosocial utility. Both motives are pervasive and negatively correlated at the individual level.
Cognitive Uncertainty
with Benjamin Enke
(The Quarterly Journal of Economics, 2023, vol 138 (4), pp. 2021–2067
[abstract]
[pdf]
This article documents the economic relevance of measuring cognitive uncertainty: people’s subjective uncertainty over their ex ante utility-maximizing decision. In a series of experiments on choice under risk, the formation of beliefs, and forecasts of economic variables, we show that cognitive uncertainty predicts various systematic biases in economic decisions. When people are cognitively uncertain—either endogenously or because the problem is designed to be complex—their decisions are heavily attenuated functions of objective probabilities, which gives rise to average behavior that is regressive to an intermediate option. This insight ties together a wide range of empirical regularities in behavioral economics that are typically viewed as distinct phenomena or even as reflecting preferences, including the probability weighting function in choice under risk; base rate insensitivity, conservatism, and sample size effects in belief updating; and predictable overoptimism and -pessimism in forecasts of economic variables. Our results offer a blueprint for how a simple measurement of cognitive uncertainty generates novel insights about what people find complex and how they respond to it.
Confidence, Self-Selection and Bias in the Aggregate
with Benjamin Enke and Ryan Oprea
(The American Economic Review, 2023, vol. 113 (7), pp. 1933-1966)
[abstract]
[pdf]
The influence of behavioral biases on aggregate outcomes depends in part on self-selection: whether rational people opt more strongly into aggregate interactions than biased individuals. In betting market, auction and committee experiments, we document that some errors are strongly reduced through self-selection, while others are not affected at all or even amplified. A large part of this variation is explained by differences in the relationship between confidence and performance. In some tasks, they are positively correlated, such that self-selection attenuates errors. In other tasks, rational and biased people are equally confident, such that self-selection has no effects on aggregate quantities.
Inattentive Inference
(Journal of the European Economic Association, 2023, vol. 21 (2), pp. 560–592)
[abstract]
[pdf]
This paper studies how people infer a state of the world from information structures that include additional, payoff-irrelevant states. For example, learning from a customer review about a product’s quality requires accounting for the reviewer’s otherwise-irrelevant taste. This creates an attribution problem common to all information structures with multiple causes. We report controlled experimental evidence for pervasive overinference about states that affect utility – a form of “omitted variable bias” in belief updating –, providing an explanation for various misattribution patterns. In studying why systematic misattribution arises, we consistently find that errors are not due to deliberate effort avoidance or a lack of cognitive capacity. Instead, people behave as if they form incomplete mental models of the information structure and fail to notice the need to account for alternative causes. These mental models are not stable but context-dependent: misattribution responds to a variety of attentional manipulations, but not to changes in the costs of inattention.
Measuring the Scientific Effectiveness of Contact Tracing: Evidence from a Natural Experiment
with Thiemo Fetzer
(Proceedings of the National Academy of Sciences, 2021, vol. 118 (33))
[abstract]
[pdf]
Contact tracing has for decades been a cornerstone of the public health approach to epidemics, including Ebola, severe acute respiratory syndrome, and now COVID-19. It has not yet been possible, however, to causally assess the method’s effectiveness using a randomized controlled trial of the sort familiar throughout other areas of science. This study provides evidence that comes close to that ideal. It exploits a large-scale natural experiment that occurred by accident in England in late September 2020. Because of a coding error involving spreadsheet data used by the health authorities, a total of 15,841 COVID-19 cases (around 20% of all cases) failed to have timely contact tracing. By chance, some areas of England were much more severely affected than others. This study finds that the random breakdown of contact tracing led to more illness and death. Conservative causal estimates imply that, relative to cases that were initially missed by the contact tracing system, cases subject to proper contact tracing were associated with a reduction in subsequent new infections of 63% and a reduction insubsequent COVID-19–related deaths of 66% across the 6 wk following the data glitch.
Bayesian signatures of confidence and central tendency in perceptual judgment
with Yang Xiang, Benjamin Enke and Samuel Gershman
(Attention, Perception & Psychophysics, 2021)
[abstract]
[pdf]
This paper theoretically and empirically investigates the role of Bayesian noisy cognition in perceptual judgment, focusing on the central tendency effect: the well-known empirical regularity that perceptual judgments are biased towards the center of the stimulus distribution. Based on a formal Bayesian framework, we show that measures of subjective confidence can be used to explain the central tendency effects and response variability through a Bayesian lens. Specifically, our model clarifies that lower subjective confidence as a measure of posterior uncertainty about a judgment should predict (i) a lower sensitivity of magnitude estimates to objective stimuli; (ii) a higher sensitivity to the mean of the stimulus distribution; (iii) a stronger central tendency effect at higher stimulus magnitudes; and (iv) higher response variability. To test these predictions, we collect a tailored large-scale experimental data set and additionally re-analyze perceptual judgment data from several previous experiments. Across data sets, subjective confidence is strongly predictive of the central tendency effect and response variability, both correlationally and when we exogenously manipulate the magnitude of sensory noise. Our results lend support to Bayesian explanations of both confidence and the central tendency effect.
Delayed Negative Effects of Prosocial Spending on Happiness
with Armin Falk
(Proceedings of the National Academy of Sciences, 2020, vol. 117 (12), pp. 6463-6468)
[abstract] [pdf]
Does prosocial behavior promote happiness? We test this longstanding hypothesis in a behavioral experiment that extends the
scope of previous research. In our Saving a Life paradigm, every
participant either saved one human life in expectation by triggering a targeted donation of 350 euros or received an amount of 100
euros. Using a choice paradigm between two binary lotteries with
different chances of saving a life, we observed subjects’ intentions at the same time as creating random variation in prosocial
outcomes. We repeatedly measured happiness at various delays.
Our data weakly replicate the positive effect identified in previous research but only for the very short run. One month later, the
sign of the effect reversed, and prosocial behavior led to significantly lower happiness than obtaining the money. Notably, even
those subjects who chose prosocially were ultimately happier if
they ended up getting the money for themselves. Our findings
revealed a more nuanced causal relationship than previously suggested, providing an explanation for the apparent absence of
universal prosocial behavior.
Assistant Professor, Harvard Business School