Daniel G. Goldstein

Increasing Saving Behavior Through Age-Progressed Renderings of the Future Self

Many people fail to save what they will need for retirement. Research on excessive discounting of the future suggests that removing the lure of immediate rewards by precommitting to decisions or elaborating the value of future rewards both can make decisions more future oriented. The authors explore a third and complementary route, one that deals not with present and future rewards but with present and future selves. In line with research that shows that people may fail, because of a lack of belief or imagination, to identify with their future selves, the authors propose that allowing people to interact with age-progressed renderings of themselves will cause them to allocate more resources to the future. In four studies, participants interacted with realistic computer renderings of their future selves using immersive virtual reality hardware and interactive decision aids. In all cases, those who interacted with their virtual future selves exhibited an increased tendency to accept later monetary rewards over immediate ones.

Predicting Individual Behavior with Social Networks

With the availability of social network data, it has become possible to relate the behavior of individuals to that of their acquaintances on a large scale. Although the similarity of connected individuals is well established, it is unclear whether behavioral predictions based on social data are more accurate than those arising from current marketing practices. We employ a communications network of over 100 million people to forecast highly diverse behaviors, from patronizing an off-line department store to responding to advertising to joining a recreational league. Across all domains, we find that social data are informative in identifying individuals who are most likely to undertake various actions, and moreover, such data improve on both demographic and behavioral models. There are, however, limits to the utility of social data.In particular, when rich transactional data were available, social data did little to improve prediction.

The Economic and Cognitive Costs of Annoying Display Advertisements

Some online display advertisements are annoying. Although publishers know the payment they receive to run annoying ads, little is known about the cost that such ads incur (e.g., causing website abandonment). Across three empirical studies, the authors address two primary questions: (1) What is the economic cost of annoying ads to publishers? and (2) What is the cognitive impact of annoying ads to users? First, the authors conduct a preliminary study to identify sets of more and less annoying ads. Second, in a field experiment, they calculate the compensating differential, that is, the amount of money a publisher would need to pay users to generate the same number of impressions in the presence of annoying ads as it would generate in their absence. Third, the authors conduct a mouse-tracking study to investigate how annoying ads affect reading processes. They conclude that in plausible scenarios, the practice of running annoying ads can cost more money than it earns.

The wisdom of smaller, smarter crowds

The wisdom of crowds refers to the phenomenon that aggregated predictions from a large group of people can rival or even beat the accuracy of experts. In domains with substantial stochastic elements, such as stock picking, crowd strategies (e.g. indexing) are difficult to beat. However, in domains in which some crowd members have demonstrably more skill than others, smart sub-crowds could possibly outperform the whole. The central question this work addresses is whether such smart subsets of a crowd can be identified a priori in a large-scale prediction contest that has substantial skill and luck components. We study this question with data obtained from fantasy soccer, a game in which millions of people choose professional players from the English Premier League to be on their fantasy soccer teams. The better the professional players do in real life games, the more points fantasy teams earn. Fantasy soccer is ideally suited to this investigation because it comprises millions of individual-level, within-subject predictions, past performance indicators, and the ability to test the effectiveness of arbitrary player-selection strategies. We find that smaller, smarter crowds can be identified in advance and that they beat the wisdom of the larger crowd. We also show that many players would do better by simply imitating the strategy of a player who has done well in the past. Finally, we provide a theoretical model that explains the results we see from our empirical analyses.

How do firms make money selling digital goods online

We review research on revenue models used by online firms who offer digital goods. Such goods are non-rival, have near zero marginal cost of production and distribution, low marginal cost of consumer search, and low transaction costs. Additionally, firms can easily observe and measure consumer behavior. We start by asking what consumers can offer in exchange for digital goods. We suggest that consumers can offer their money, personal information, or time. Firms, in turn, can generate revenue by selling digital content, brokering consumer information, or showing advertising. We discuss the firm’s trade-off in choosing between the different revenue streams, such as offering paid content or free content while relying on advertising revenues. We then turn to specific challenges firms face when choosing a revenue model based on either content, information, or advertising. Additionally, we discuss nascent revenue models that combine different revenue streams such as crowdfunding (content and information) or blogs (information and advertising). We conclude with a discussion of opportunities for future research including implications for firms’ revenue models from the increasing importance of the mobile Internet.

Improving Comprehension of Numbers in the News

How many guns are there in the USA? What is the incidence of breast cancer? Is a billion dollar budget cut large or small? Advocates of scientific and civic literacy are concerned with improving how people estimate and comprehend risks, measurements, and frequencies, but relatively little progress has been made in this direction. In this article we describe and test a framework to help people comprehend numerical measurements in everyday settings through simple sentences, termed perspectives, that employ ratios, ranks, and unit changes to make them easier to understand. We use a crowdsourced system to generate perspectives for a wide range of numbers taken from online news articles. We then test the effectiveness of these perspectives in three randomized, online experiments involving over 3,200 participants. We find that perspective clauses substantially improve peoples ability to recall measurements they have read, estimate ones they have not, and detect errors in manipulated measurements. We see this as the first of many steps in leveraging digital platforms to improve numeracy among online readers.

To Put That in Perspective: Generating Analogies that Make Numbers Easier to Understand

Laypeople are frequently exposed to unfamiliar numbers published by journalists, social media users, and algorithms. These figures can be difficult for readers to comprehend, especially when they are extreme in magnitude or contain unfamiliar units. Prior work has shown that adding perspective sentences that employ ratios, ranks, and unit changes to such measurements can improve peoples ability to understand unfamiliar numbers (e.g., 695,000 square kilometers is about the size of Texas). However, there are many ways to provide context for a measurement. In this paper we systematically test what factors influence the quality of perspective sentences through randomized experiments involving over 1,000 participants. We develop a statistical model for generating perspectives and test it against several alternatives, finding beneficial effects of perspectives on comprehension that persist for six weeks. We conclude by discussing future work in deploying and testing perspectives at scale.

Learning in the Repeated Secretary Problem

In the classical secretary problem, one attempts to find the maximum of an unknown and unlearnable distribution through sequential search. In many real-world searches, however, distributions are not entirely unknown and can be learned through experience. To investigate learning in such a repeated secretary problem we conduct a large-scale behavioral experiment in which people search repeatedly from fixed distributions. In contrast to prior investigations that find no evidence for learning in the classical scenario, in the repeated setting we observe substantial learning resulting in near-optimal stopping behavior. We conduct a Bayesian comparison of multiple behavioral models which shows that participants behavior is best described by a class of threshold-based models that contains the theoretically optimal strategy. In fact, fitting such a threshold-based model to data reveals players estimated thresholds to be surprisingly close to the optimal thresholds after only a small number of games.

VoxPL: Programming with the Wisdom of the Crowd

Having a crowd estimate a numeric value is the original inspiration for the notion of the wisdom of the crowd. Quality control for such estimated values is challenging because prior, consensus-based approaches for quality control in labeling tasks are not applicable in estimation tasks. We present VoxPL, a high-level programming framework that automatically obtains high-quality crowdsourced estimates of values. The VoxPL domain-specific language lets programmers concisely specify complex estimation tasks with a desired level of confidence and budget. VoxPLs runtime system implements a novel quality control algorithm that automatically computes sample sizes and obtains high quality estimates from the crowd at low cost. To evaluate VoxPL, we implement four estimation applications, ranging from facial feature recognition to calorie counting. The resulting programs are concise---under 200 lines of code---and obtain high quality estimates from the crowd quickly and inexpensively.

Erratum to: The role of subjective beliefs in preferences for redistribution

We investigate whether beliefs about the income distribution are associated with political positions for or against redistribution. Using a novel elicitation method, we assess individuals’ beliefs about the shape of the income distribution in the United States. We find that beliefs about inequality, measured in terms of income dispersion, play only a marginal role in political positions as well as prospects of future wealth. Political preferences, however, are predicted by beliefs about the level of income of the poorest members of society (consistent with quasi-maximin utility functions), and a belief in an open society with equal opportunities for all.