When predicting the next outcome in a sequence of events, people often appear to expect streaky patterns, such as that sport players can develop a “hot hand,” even if the sequence is actually random. This expectation, referred to as positive recency, can be adaptive in environments characterized by resources that are clustered across space or time (e.g., expecting to find multiple berries on separate bushes). But how strong is this disposition towards positive recency? If people perceive random sequences as streaky, will there be situations in which they forego a payoff because they prefer an unpredictable random environment over an exploitable but alternating pattern? To find out, 238 participants repeatedly chose to bet on the next outcome of one of two sequences of (binary) events, presented next to each other. One sequence displayed events at random while the other sequence was either more streaky (positively autocorrelated) or more alternating (negatively autocorrelated) than chance. The degree of autocorrelation varied in a between-subject design. Most people preferred to predict purely random sequences over those with moderate negative autocorrelation and thus missed the opportunity for above-chance payoff. Positive recency persisted despite extensive feedback and the opportunity to learn more rewarding behavior over time. Further, most participants' choice strategies were best described by a win-stay/lose-shift strategy, adaptive in clumpy or streaky environments. We discuss the implications regarding an evolved human tendency to expect streaky patterns, even if the sequence is actually random.
Affective states can change how people react to measures aimed at influencing their decisions such as providing a default option. Previous research has shown that when defaults maintain the status quo positive mood increases reliance on the default and negative mood decreases it. Similarly, it has been demonstrated that positive mood enhances the preference for inaction. We extend this research by investigating how mood states influence reliance on the default if the default leads to a change, thus pitting preference for status quo against a preference for inaction. Specifically, we tested in an online study how happiness and sadness influenced reliance on two types of default (1) a default maintaining status quo and (2) a default inducing change. Our results suggest that the effect of emotions depends on the type of default: people in a happy mood were more likely than sad people to follow a default when it maintained status quo but less likely to follow a default when it introduced change. These results are in line with mood maintenance theory.
The heritability of variety seeking in the food domain was estimated from a large sample (N = 5,543) of middle age to elderly monozygotic and dizygotic twins from the “Virginia 30,000” twin study. Different dietary variety scores were calculated based on a semi-quantitative food choice questionnaire that assessed consumption frequencies and quantities for a list of 99 common foods. Results indicate that up to 30% of the observed variance in dietary variety was explained through heritable influences. Most of the differences between twins were due to environmental influences that are not shared between twins. Additional non-genetic analyses further revealed a weak relationship between dietary variety and particular demographic variables, including socioeconomic status, age, sex, religious faith, and the number of people living in the same household.
Two models of how people predict the next outcome in a sequence of binary events were developed and compared on the basis of gambling data from a lab experiment using hierarchical Bayesian techniques. The results from a student sample (N = 39) indicated that a model that considers run length (“drift model”)—that is, how often the same event has previously occurred in a row—provided a better description of the data than did a stationary model taking only the immediately prior event into account. Both, expectation of negative and of positive recency was observed, and these tendencies mostly grew stronger with run length. For some individuals, however, the relationship was reversed, leading to a qualitative shift from expecting positive recency for short runs to expecting negative recency for long runs. Both patterns could be accounted for by the drift model but not the stationary model. The results highlight the importance of applying hierarchical analyses that provide both group- and individual-level estimates. Further extensions and applications of the approach in the context of the prediction literature are discussed.
Decision-making is one of the core tasks in project management. Traditionally, optimization methods have been developed to support managers in finding the best solutions. Alternatively, decisions can be based on simple rules of thumb, or heuristics. Even though simple heuristics only require little in the way of time and information, they have been shown to outperform optimization methods in complex decision tasks across a wide range of situations. This chapter outlines relevant decision heuristics commonly used, demonstrates situations in which they outperform more complex decision algorithms and explains why and when simple heuristics provide powerful decision tools.
Many economic theories of decision making assume that people evaluate options independently of other available options. However, recent cognitive theories such as decision field theory suggest that people’s evaluations rely on a relative comparison of the options’ potential consequences such that the subjective value of an option critically depends on the context in which it is presented. To test this prediction, we examined pairwise choices between monetary gambles and varied the degree to which the gambles’ outcomes covered with one another. When people evaluate options by comparing their outcomes, a high covariance between these outcomes should make a decision easier, as suggested by decision field theory. In line with this prediction, the observed choice proportions in 2 experiments (N = 39 and 24, respectively) depended on the magnitude of the covariance. We call this effect the covariance effect. Our findings are in line with the theoretic predictions and show that the discriminability ratio in decision field theory can reflect the choice difficulty. These results confirm that interdependent evaluations of options play an important role in human decision making under risk and show that covariance is an important aspect of the choice context.
Many cognitive theories of judgement and decision making assume that choice options are evaluated relative to other available options. The extent to which the preference for one option is influenced by other available options will often depend on how similar the options are to each other, where similarity is assumed to be a decreasing function of the distance between options. We examine how the distance between preferential options that are described on multiple attributes can be determined. Previous distance functions do not take into account that attributes differ in their subjective importance, are limited to two attributes, or neglect the preferential relationship between the options. To measure the distance between preferential options it is necessary to take the subjective preferences of the decision maker into account. Accordingly, the multi-attribute space that defines the relationship between options can be stretched or shrunk relative to the attention or importance that a person gives to different attributes describing the options. Here, we propose a generalized distance function for preferential choices that takes subjective attribute importance into account and allows for individual differences according to such subjective preferences. Using a hands-on example, we illustrate the application of the function and compare it to previous distance measures. We conclude with a discussion of the suitability and limitations of the proposed distance function.
In management research, empirical data are often analyzed using p-value null hypothesis significance testing (pNHST). Here we outline the conceptual and practical advantages of an alternative analysis method: Bayesian hypothesis testing and model selection using the Bayes factor. In contrast to pNHST, Bayes factors allow researchers to quantify evidence in favor of the null hypothesis. Also, Bayes factors do not require adjustment for the intention with which the data were collected. The use of Bayes factors is demonstrated through an extended example for hierarchical regression based on the design of an experiment recently published in the Journal of Management. This example also highlights the fact that p values overestimate the evidence against the null hypothesis, misleading researchers into believing that their findings are more reliable than is warranted by the data.
Consumers’ purchase decisions depend on whether a product is perceived as a bargain or as overpriced. But how do consumers evaluate sales prices? The standard approach in economics, psychology, and marketing suggests that consumers’ estimates are best described by a attribute-based or piecemeal strategy that integrates information about products in a linear additive fashion. Here, we outline and test an alternative theoretical approach from the categorization literature suggesting that consumers sometimes follow an exemplar-based strategy that relies on similarity to previously encountered products. We hypothesize that people switch between these two estimation strategies depending on the context they face. To test this hypothesis, we conducted an experiment in which 64 participants repeatedly estimated the market price of different consumer products (bottles of wine). In one condition, the product prices could be well approximated with an attribute-based strategy whereas in the other condition an exemplar-based strategy worked best. Results of a subsequent testing phase indicated that participants switched between strategies depending on the structure of the presented sets. These results show that people rely on different strategies to estimate market prices, which should influence people’s consumption behavior. The results suggest that theories on categorization learning can provide a deeper insight into behavior in an economic context and allow predicting consumer behavior more accurately.
