Overconfidence vs. Market Efficiency in the National Football League

A question of increasing interest to researchers in a variety of fields is whether the incentives and experience present in many "real world" settings mitigate judgment and decision-making biases. To investigate this question, we analyze the decision making of National Football League teams during their annual player draft. This is a domain in which incentives are exceedingly high and the opportunities for learning rich. It is also a domain in which multiple psychological factors suggest teams may overvalue the "right to choose" in the draft -- non-regressive predictions, overconfidence, the winner's curse and false consensus all suggest a bias in this direction. Using archival data on draft-day trades, player performance and compensation, we compare the market value of draft picks with the historical value of drafted players. We find that top draft picks are overvalued in a manner that is inconsistent with rational expectations and efficient markets and consistent with psychological research.

Small Cues Change Savings Choices

In randomized field experiments, we embedded one- to two-sentence anchoring, goal-setting, or savings threshold cues in emails to employees about their 401(k) savings plan. We find that anchors increase or decrease 401(k) contribution rates by up to 1.9% of income. A high savings goal example raises contribution rates by up to 2.2% of income. Highlighting a higher savings threshold in the match incentive structure raises contributions by up to 1.5% of income relative to highlighting the lower threshold. Highlighting the maximum possible contribution rate raises contribution rates by up to 2.9% of income among low savers.

The Loser\u27s Curse: Decision Making and Market Efficiency in the National Football League Draft

A question of increasing interest to researchers in a variety of fields is whether the biases found in judgment and decision-making research remain present in contexts in which experienced participants face strong economic incentives. To investigate this question, we analyze the decision making of National Football League teams during their annual player draft. This is a domain in which monetary stakes are exceedingly high and the opportunities for learning are rich. It is also a domain in which multiple psychological factors suggest that teams may overvalue the chance to pick early in the draft. Using archival data on draft-day trades, player performance, and compensation, we compare the market value of draft picks with the surplus value to teams provided by the drafted players. We find that top draft picks are significantly overvalued in a manner that is inconsistent with rational expectations and efficient markets, and consistent with psychological research

Overcoming Algorithm Aversion: People will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them

Although evidence-based algorithms consistently outperform human forecasters, people often fail to use them after learning that they are imperfect, a phenomenon known as algorithm aversion. In this paper, we present three studies investigating how to reduce algorithm aversion. In incentivized forecasting tasks, participants chose between using their own forecasts or those of an algorithm that was built by experts. Participants were considerably more likely to choose to use an imperfect algorithm when they could modify its forecasts, and they performed better as a result. Notably, the preference for modifiable algorithms held even when participants were severely restricted in the modifications they could make (Stuides 1-3). In fact, our results suggest that participants\u27 preference for modifiable algorithms was indicative of a desire for some control over the forecasting outcome, and not for a desire for greater control over the forecasting outcome, as participants\u27 preference for modifiable was relatively insensitive to the magnitude of the modifications they were able to make (Study 2). Additionally, we found that giving participants the freedom to modify an imperfect algorithm made them feel more satisfied with the forecasting process, more likely to believe that the algorithm was superior, and more likely to choose to use an algorithm to make subsequent forecasts (Study 3). This research suggests that one can reduce algorithm aversion by giving people some control—even a slight amount—over an imperfect algorithm\u27s forecast

Prescribed Optimism: Is it Right to be Wrong About the Future?

We test the assumption that people desire to be accurate when making predictions about their own future. Results revealed that, across four different scenarios and three manipulated variables (commitment to a decision, agency over the decision, and control over outcomes), participants thought it was better to make optimistically biased predictions than accurate or pessimistically biased predictions. Additionally, participants thought that they and others would be optimistic in the scenarios they read, but insufficiently so. We argue that prescriptions can serve as one standard by which the quality of predictions can be judged, and that this particular standard strongly endorses optimism

Hope Over Experience Desirability and the Persistence of Optimism

Many important decisions hinge on expectations of future outcomes. Decisions about health, investments, and relationships all depend on predictions of the future. These expectations are often optimistic: People frequently believe that their preferred outcomes are more likely than is merited. Yet it is unclear whether optimism persists with experience and, surprisingly, whether optimism is truly caused by desire. These are important questions because life’s most consequential decisions often feature both strong preferences and the opportunity to learn. We investigated these questions by collecting football predictions from National Football League fans during each week of the 2008 season. Despite accuracy incentives and extensive feedback, predictions about preferred teams remained optimistically biased through the entire season. Optimism was as strong after 4 months as it was after 4 weeks. We exploited variation in preferences and matchups to show that desirability fueled this optimistic bias

Algorithm Aversion: People Erroneously Avoid Algorithms after Seeing Them Err

Research shows that evidence-based algorithms more accurately predict the future than do human forecasters. Yet when forecasters are deciding whether to use a human forecaster or a statistical algorithm, they often choose the human forecaster. This phenomenon, which we call algorithm aversion, is costly, and it is important to understand its causes. We show that people are especially averse to algorithmic forecasters after seeing them perform, even when they see them outperform a human forecaster. This is because people more quickly lose confidence in algorithmic than human forecasters after seeing them make the same mistake. In 5 studies, participants either saw an algorithm make forecasts, a human make forecasts, both, or neither. They then decided whether to tie their incentives to the future predictions of the algorithm or the human. Participants who saw the algorithm perform were less confident in it, and less likely to choose it over an inferior human forecaster. This was true even among those who saw the algorithm outperform the human

The Importance of Being an Optimist: Evidence from Labor Markets

Dispositional optimism is a personality trait associated with individuals who believe, either rightly or wrongly, that in general good things tend to happen to them more often than bad things. Using a novel longitudinal data set that tracks the job search performance of MBA students, we show that dispositional optimists experience significantly better job search outcomes than pessimists with similar skills. During the job search process, they spend less effort searching and are offered jobs more quickly. They are choosier and are more likely to be promoted than others. Although we find optimists are more charismatic and are perceived by others to be more likely to succeed, these factors alone do not explain away the findings. Most of the effect of optimism on economic outcomes stems from the part that is not readily observed by one's peers.