Jack B. Soll

Mental Budgeting and Consumer Decisions

Consumers often set budgets for categories of expenses (e.g., entertainment) and track expenses against their budget. Because budgets cannot perfectly anticipate consumption opportunities, people may earmark too much or too little money for a particular category. This leads them to overconsume or underconsume goods in that category. The results of three studies suggest that consumers do indeed set budgets and that budgeting may lead to underconsumption. To show that consumers track expenses, the studies demonstrate that budgeting effects are larger for purchases that are highly typical of their category. Such purchases reduce the amount people spend in a category and block the purchase of other typical items. The studies control for satiation and income effects; thus, budgeting adds predictive power to standard economic consumer theory. Copyright 1996 by the University of Chicago.

Strategies for Revising Judgment: How (and How Well) People Use Others' Opinions.

A basic issue in social influence is how best to change ones judgment in response to learning the opinions of others. This article examines the strategies that people use to revise their quantitative estimates on the basis of the estimates of another person. The authors note that people tend to use 2 basic strategies when revising estimates: choosing between the 2 estimates and averaging them. The authors developed the probability, accuracy, redundancy (PAR) model to examine the relative effectiveness of these two strategies across judgment environments. A surprising result was that averaging was the more effective strategy across a wide range of commonly encountered environments. The authors observed that despite this finding, people tend to favor the choosing strategy. Most participants in these studies would have achieved greater accuracy had they always averaged. The identification of intuitive strategies, along with a formal analysis of when they are accurate, provides a basis for examining how effectively people use the judgments of others. Although a portfolio of strategies that includes averaging and choosing can be highly effective, the authors argue that people are not generally well adapted to the environment in terms of strategy selection.

Intuitive theories of information: beliefs about the value of redundancy.

In many situations, quantity estimates from multiple experts or diagnostic instruments must be collected and combined. Normatively, and all else equal, one should value information sources that are nonredundant, in the sense that correlation in forecast errors should be minimized. Past research on the preference for redundancy has been inconclusive. While some studies have suggested that people correctly place higher value on uncorrelated inputs when collecting estimates, others have shown that people either ignore correlation or, in some cases, even prefer it. The present experiments show that the preference for redundancy depends on ones intuitive theory of information. The most common intuitive theory identified is the Error Tradeoff Model (ETM), which explicitly distinguishes between measurement error and bias. According to ETM, measurement error can only be averaged out by consulting the same source multiple times (normatively false), and bias can only be averaged out by consulting different sources (normatively true). As a result, ETM leads people to prefer redundant estimates when the ratio of measurement error to bias is relatively high. Other participants favored different theories. Some adopted the normative model, while others were reluctant to mathematically average estimates from different sources in any circumstance. In a post hoc analysis, science majors were more likely than others to subscribe to the normative model. While tentative, this result lends insight into how intuitive theories might develop and also has potential ramifications for how statistical concepts such as correlation might best be learned and internalized.

Consumer Misunderstanding of Credit Card Use, Payments, and Debt: Causes and Solutions

The authors identify several judgmental biases related to paying off credit card debt. Participants with stronger numerical skills made fewer errors, as did those who used the new statement format mandated by Congress in the CARD Act of 2009. Study 1 shows that people underestimate how long it takes to eliminate a debt when payments barely cover interest owed. Study 2 shows that less numerate people tend to underestimate the monthly payment required to pay off a debt in three years, whereas more numerate people tend to overestimate the payment. The newly revised statement required by the CARD Act substantially reduced these biases. However, even with the new statement, many people still underestimate required payments when still using the credit card. Study 3 identifies ambiguities in the revised statement that can lead to misjudgments about how much to pay on monthly bills. The authors recommend additional public policy actions to help cardholders understand the relationship between payments and debt elimination.

The wisdom of select crowds.

Social psychologists have long recognized the power of statisticized groups. When individual judgments about some fact (e.g., the unemployment rate for next quarter) are averaged together, the average opinion is typically more accurate than most of the individual estimates, a pattern often referred to as the wisdom of crowds. The accuracy of averaging also often exceeds that of the individual perceived as most knowledgeable in the group. However, neither averaging nor relying on a single judge is a robust strategy; each performs well in some settings and poorly in others. As an alternative, we introduce the select-crowd strategy, which ranks judges based on a cue to ability (e.g., the accuracy of several recent judgments) and averages the opinions of the top judges, such as the top 5. Through both simulation and an analysis of 90 archival data sets, we show that select crowds of 5 knowledgeable judges yield very accurate judgments across a wide range of possible settings-the strategy is both accurate and robust. Following this, we examine how people prefer to use information from a crowd. Previous research suggests that people are distrustful of crowds and of mechanical processes such as averaging. We show in 3 experiments that, as expected, people are drawn to experts and dislike crowd averages-but, critically, they view the select-crowd strategy favorably and are willing to use it. The select-crowd strategy is thus accurate, robust, and appealing as a mechanism for helping individuals tap collective wisdom.

Designing better energy metrics for consumers

Consumers are often poorly informed about the energy consumed by different technologies and products. Traditionally, consumers have been provided with limited and flawed energy metrics, such as miles per gallon, to quantify energy use. We propose four principles for designing better energy metrics. Better measurements would describe the amount of energy consumed by a device or activity, not its energy efficiency; relate that information to important objectives, such as reducing costs or environmental impacts; use relative comparisons to put energy consumption in context; and provide information on expanded scales. We review insights from psychology underlying the recommendations and the empirical evidence supporting their effectiveness. These interventions should be attractive to a broad political spectrum because they are low cost and designed to improve consumer decisionmaking.

POWERFUL AND UNPERSUADED: THE IMPLICATIONS OF POWER FOR CONFIDENCE, ADVICE TAKING, AND ACCURACY.

The article discusses how internal psychological states or mindsets that are triggered by the social environment may influence the taking of advice. A decision makers willingness to revise his or ...

Eliciting and Aggregating Forecasts When Information is Shared

Using the wisdom of crowds -- combining many individual judgments to obtain an aggregate estimate -- can be an effective technique for improving judgment accuracy. In practice, however, accuracy is limited by the presence of correlated judgment errors, which often emerge because information is shared. To address this problem, we propose an elicitation procedure in which respondents are asked to provide both their own best judgment and an estimate of the average judgment that will be given by all other respondents. We develop an aggregation method, called pivoting, which separates individual judgments into shared and private information and then recombines these results in the optimal manner. In several studies, we investigate the method and examine the accuracy of the aggregate estimates. Overall, the empirical data suggest that the pivoting method provides an effective judgment aggregation procedure that can significantly outperform the simple crowd average.

Which reference class is evoked

Any instance (i.e., event, behavior, trait) belongs to infinitely many reference classes, hence there are infinitely many base rates from which to choose. People clearly do not entertain all possible reference classes, however, so something must be limiting the search space. We suggest some possible mechanisms that determine which reference class is evoked for the purpose of judgment and decision.