Are people willing to gamble more for themselves than what they deem reasonable for others? We addressed this question in a simplified computer gambling task in which subjects chose from a set of 10 cards. Subjects selected one card at a time after being instructed that 9 cards were good (win a dollar per card) and one was really bad (lose all the money and end the game). Subjects could stop playing at any time to collect their winnings. Some subjects played the game, others observed a confederate. Both groups took risks beyond what was rational (i.e., 5 cards) but actors were riskier than observers. The actor/observer asymmetry occurred even after controlling for monetary outcome (i.e., having observers win prizes) and after controlling for how the question was framed (i.e., asking observers what they themselves would do as opposed to what the confederate should do). We discuss these results in relation to theories of decision making that emphasize separate contributions of rational and experiential systems.

Researchers in the decision making tradition usually analyze multiple decisions within experiments by aggregating choices across individuals and using the individual subject as the unit of analysis. This approach can mask important variations and patterns within the data. Specifically, it ignores variations in decisions across a task or game and possible influences of characteristics of the subject or the experiment on these variations. We demonstrate, by reanalyzing data from two previously published articles, how a mixed model analysis addresses these limitations. Our results, with a modified Iowa gambling task and a prisoner's dilemma game, illustrate the ways in which such an analysis can test hypotheses not possible with other techniques, is more parsimonious, and is more likely to be faithful to theoretical models.

In markets with asymmetric information, only sellers have knowledge about the quality of goods. Sellers may of course make a declaration of the quality, but unless there are sanctions imposed on false declarations or reputations are at stake, such declarations are tantamount to cheap talk. Nonetheless, in an experimental study we find that most people make honest declarations, which is in line with recent findings that lies damaging another party are costly in terms of the liar's utility. Moreover, we find in this experimental market that deceptive sellers offer lower prices than honest sellers, which could possibly be explained by the same wish to limit the damage to the other party. However, when the recipient of the offer is a social tie we find no evidence for lower prices of deceptive offers, which seems to indicate that the rationale for the lower price in deceptive offers to strangers is in fact profit-seeking (by making the deal more attractive) rather than moral.

It has been proposed that recognition can form the basis of simple but ecologically rational decision strategies (Gigerenzer & Goldstein, 1996). Borges, Goldstein, Ortmann, & Gigerenzer (1999) found that constructing share portfolios based on simple name recognition alone often yielded better returns than the market index. We describe four studies with seven samples of participants from three countries (total N = 319) in which the returns of recognized and unrecognized shares from several stock markets were tracked over various periods of time. We find no support for the claim that a simple strategy of name recognition can be used as a general strategy to select stocks that yield better-than-average returns. However, there was some suggestion in the data that recognition performs better when the market is falling and worse when it is rising. A follow-up study indicated that the absence of an overall recognition effect could not easily be attributed to our reliance on student participants or smaller samples than Borges et al. (1999) had used. We conclude that, with respect to changes in value, selecting stocks on the basis of name recognition is a near-random method of portfolio construction that offers little, if any, benefit to the personal investor.

Previous research has identified important determinants of overall evaluations for experiences lived across time. By means of a novel guessing task, I study what decision-makers themselves consider important. As Informants, some participants live and evaluate an experience. As Guessers, others have to infer its overall evaluation by asking Informants questions. I rewarded accurate inferences, and analyzed and classified the questions in four experiments involving auditory, gustatory and viewing experiences. Results show that Guessers thought of overall evaluations as reflecting average momentary impressions. Moreover and alternatively, they tended to consider the personality and attitudes of the experiencing person, experience-specific holistic judgments and behavioral intentions regarding the experience. Thus, according to lay intuitions, overall evaluations are more than a reflection of the experience's momentary impressions.

Previous research on anchoring has shown this heuristic to be a very robust psychological phenomenon ubiquitous across many domains of human judgment and decision-making. Despite the prevalence of anchoring effects, researchers have only recently begun to investigate the underlying factors responsible for how and in what ways a person is susceptible to them. This paper examines how one such factor, the Big-Five personality trait of openness-to-experience, influences the effect of previously presented anchors on participants' judgments. Our findings indicate that participants high in openness-to-experience were significantly more influenced by anchoring cues relative to participants low in this trait. These findings were consistent across two different types of anchoring tasks providing convergent evidence for our hypothesis.

This paper reports two experiments comparing variants of multiple explanation applied in the early stages of a judgment task (a case involving employee theft) where participants are not given a menu of response options. Because prior research has focused on situations where response options are provided to judges, we identify relevant dependent variables that an intervention might affect when such options are not given. We use these variables to build a causal model of intervention that illustrates both the intended effects of multiple explanation and some potentially competing processes that it may trigger. Although multiple explanation clearly conveys some benefits (e.g., willingness to delay action to engage in information search, increased detail, quality and confidence in alternative explanations) in the present experiments, we also found evidence that it may initiate or enhance processes that attenuate its advantages (e.g., feelings that one does not need more data if one has multiple good explanations).

We compare experts’ judgments of the appropriateness of a treatment (interferon treatment for melanoma) on the basis of important attributes of this disease (thickness, ulceration, lymph node involvement and type of metastases) to a decision analytic model in which the probabilities of deterioration are derived from the medical literature and from epidemiological studies. The comparison is based on what we call the linearity test, which examines whether appropriateness judgments are a linear function of the epidemiological value of p2, the probability of deterioration of the patient condition if he would have received the treatment. This comparison allows for the assessment of the validity of the experts’ judgments under the assumption that the decision analytic model is valid, or alternatively, the assessment of the validity of the decision analytic model under the assumption that the experts’ judgments are valid. Under the former assumption the results indicate that appropriateness judgments are by and large accurate. Under the latter assumption the results support the idea of a constant treatment effect, the idea that efficacy of a treatment is constant over various levels of severity of the disease. Our results also support the idea that experts’ aggregate judgments far exceed individuals’ judgments.