The willingness to punish norm violation is an important component of many legal and social institutions, and much prior research demonstrates an apparent willingness to incur costs to punish individuals who act unfairly. But, will people rely on “excuses” to get out of having to act on costly punishment intentions, as they do with other costly pro-social acts? And how may the answer to this question depend on whether the punisher is the victim of a norm violation or an independent third party? We conduct an experiment and find that third parties punish reluctantly: although they indicate a preference to punish, they choose to avoid the opportunity to punish when they can do so without explicitly revealing that this is their preference. In contrast, second parties, who have been directly wronged, are resolute punishers—they actively seek out the opportunity to punish, even misrepresenting random outcomes in order to ensure that punishment is implemented. Our findings highlight important differences in the motives underlying second- and third-party punishment.
Multi-unit ascending auctions allow for equilibria in which bidders strategically reduce their demand and split the market at low prices. At the same time, they allow for preemptive bidding by incumbent bidders in a coordinated attempt to exclude entrants from the market. We consider an environment where both demand reduction and preemptive bidding are supported as equilibrium phenomena of the ascending auction. In a series of experiments, we compare its performance to that of the discriminatory auction. Strategic demand reduction is quite prevalent in the ascending auction even when entry imposes a (large) negative externality on incumbents. As a result, the ascending auction performs worse than the discriminatory auction both in terms of revenue and efficiency, while entrants’ chances are similar across the two formats.
Even before the Great Recession, U.S. employment growth was unimpressive. Between 2000 and 2007, the economy gave back the considerable employment gains achieved during the 1990s, with a historic contraction in manufacturing employment being a prime contributor to the slump. We estimate that import competition from China, which surged after 2000, was a major force behind both recent reductions in U.S. manufacturing employment and—through input-output linkages and other general equilibrium channels—weak overall U.S. job growth. Our central estimates suggest job losses from rising Chinese import competition over 1999 through 2011 in the range of 2.0 to 2.4 million.
Optimal rank-order tournaments have traditionally been studied using a first-order approach. The present analysis relies instead on the construction of an "upper envelope" over all incentive compatibility conditions. lt turns out that the first-order approach is not innocuous. For example, in contrast to the traditional understanding, tournaments may be dominated by piece rates even if workers are risk-neutral. The paper also offers a strikingly simple characterization of the optimal tournament for quadratic costs and CARA utility, as well as an extension to large tournaments.
Firms frequently attempt to increase profits by replacing some existing workers with new lower-wage workers. However, this strategy may be ineffective in an incomplete-contract environment because the new workers may provide lower effort in response to their lower wages and hiring new lower-wage workers may damage the remaining original workers' reciprocal relationship with the firm. We conduct an experiment to examine this issue and find that when new lower-wage workers become available, firms hire them to replace original higher-wage workers and pay the new workers lower wages. However, these lower wages do not improve firm profit because the decision to hire new lower-wage workers causes both the new and remaining workers to provide lower effort. Moreover, hiring lower-wage workers reduces new workers' payoffs and thus decreases social welfare. These unintended consequences suggest that firms should consider both the wage savings and the potential costs when deciding whether to replace some workers with new lower-wage workers. We discuss the implications of our findings for contract design, hiring practices, and managerial accountants.
The mispricing of marketing performance indicators (e.g., brand equity, churn, and customer satisfaction) is an important element of arguments in favor of the financial value of marketing investments. Evidence for mispricing can be assessed by examining whether or not portfolios composed of firms that load highly on marketing performance indicators deliver excess returns. Unfortunately, extant portfolio formation methods that require the use of a risk model are open to the criticism of time-varying risk factor loadings due to the changing composition of the portfolio over time. This is a serious critique as the direction of the induced bias is unknown. As an alternative, we propose a new method and construct portfolios that are neutral with respect to the desired risk factors a priori. Consequently, no risk model is needed when analyzing the observed returns of our portfolios. We apply our method to a frequently studied marketing performance indicator, customer satisfaction, and using various ways of measuring customer satisfaction, we do not find any convincing evidence that portfolios that load on high customer satisfaction lead to abnormal returns.
Recognizing intentions of strangers from facial cues is crucial in everyday social interactions. Recent studies demonstrated enhanced event-related potential (ERP) responses to untrustworthy compared to trustworthy faces. The aim of the present study was to investigate the electrophysiological correlates of automatic processing of trustworthiness cues in a visual oddball paradigm in two consecutive experimental blocks. In one block, frequent trustworthy (p = 0.9) and rare untrustworthy face stimuli (p = 0.1) were briefly presented on a computer screen with each stimulus consisting of four peripherally positioned faces. In the other block stimuli were presented with reversed probabilities enabling the comparison of ERPs evoked by physically identical deviant and standard stimuli. To avoid attentional effects participants engaged in a central detection task. Analyses of deviant minus standard difference waveforms revealed that deviant untrustworthy but not trustworthy faces elicited the visual mismatch negativity (vMMN) component. The present results indicate that adaptation occurred to repeated unattended trustworthy (but not untrustworthy) faces, i.e., an automatic expectation was elicited towards trustworthiness signals, which was violated by deviant untrustworthy faces. As an evolutionary adaptive mechanism, the observed fast detection of trustworthiness-related social facial cues may serve as the basis of conscious recognition of reliable partners.
The diagnosis of Attention Deficit Hyperactivity Disorder (ADHD) is based on subjective measures despite evidence for multisystemic structural and functional deficits. ADHD patients have consistent neurofunctional deficits in motor response inhibition. The aim of this study was to apply pattern classification to task-based functional magnetic resonance imaging (fMRI) of inhibition, to accurately predict the diagnostic status of ADHD. Thirty adolescent ADHD and thirty age-matched healthy boys underwent fMRI while performing a Stop task. fMRI data were analyzed with Gaussian process classifiers (GPC), a machine learning approach, to predict individual ADHD diagnosis based on task-based activation patterns. Traditional univariate case-control analyses were also performed to replicate previous findings in a relatively large dataset. The pattern of brain activation correctly classified up to 90% of patients and 63% of controls, achieving an overall classification accuracy of 77%. The regions of the discriminative network most predictive of controls included later developing lateral prefrontal, striatal, and temporo-parietal areas that mediate inhibition, while regions most predictive of ADHD were in earlier developing ventromedial fronto-limbic regions, which furthermore correlated with symptom severity. Univariate analysis showed reduced activation in ADHD in bilateral ventrolateral prefrontal, striatal, and temporo-parietal regions that overlapped with areas predictive of controls, suggesting the latter are dysfunctional areas in ADHD. We show that significant individual classification of ADHD patients of 77% can be achieved using whole brain pattern analysis of task-based fMRI inhibition data, suggesting that multivariate pattern recognition analyses of inhibition networks can provide objective diagnostic neuroimaging biomarkers of ADHD.
Incidental negative emotions unrelated to the current task, such as background anxiety, can strongly influence decisions. This is most evident in psychiatric disorders associated with generalized emotional disturbances. However, the neural mechanisms by which incidental emotions may affect choices remain poorly understood. Here we study the effects of incidental anxiety on human risky decision making, focusing on both behavioral preferences and their underlying neural processes. Although observable choices remained stable across affective contexts with high and low incidental anxiety, we found a clear change in neural valuation signals: during high incidental anxiety, activity in ventromedial prefrontal cortex and ventral striatum showed a marked reduction in (1) neural coding of the expected subjective value (ESV) of risky options, (2) prediction of observed choices, (3) functional coupling with other areas of the valuation system, and (4) baseline activity. At the same time, activity in the anterior insula showed an increase in coding the negative ESV of risky lotteries, and this neural activity predicted whether the risky lotteries would be rejected. This pattern of results suggests that incidental anxiety can shift the focus of neural valuation from possible positive consequences to anticipated negative consequences of choice options. Moreover, our findings show that these changes in neural value coding can occur in the absence of changes in overt behavior. This suggest a possible pathway by which background anxiety may lead to the development of chronic reward desensitization and a maladaptive focus on negative cognitions, as prevalent in affective and anxiety disorders.
Inferring on others' (potentially time-varying) intentions is a fundamental problem during many social transactions. To investigate the underlying mechanisms, we applied computational modeling to behavioral data from an economic game in which 16 pairs of volunteers (randomly assigned to “player” or “adviser” roles) interacted. The player performed a probabilistic reinforcement learning task, receiving information about a binary lottery from a visual pie chart. The adviser, who received more predictive information, issued an additional recommendation. Critically, the game was structured such that the adviser's incentives to provide helpful or misleading information varied in time. Using a meta-Bayesian modeling framework, we found that the players' behavior was best explained by the deployment of hierarchical learning: they inferred upon the volatility of the advisers' intentions in order to optimize their predictions about the validity of their advice. Beyond learning, volatility estimates also affected the trial-by-trial variability of decisions: participants were more likely to rely on their estimates of advice accuracy for making choices when they believed that the adviser's intentions were presently stable. Finally, our model of the players' inference predicted the players' interpersonal reactivity index (IRI) scores, explicit ratings of the advisers' helpfulness and the advisers' self-reports on their chosen strategy. Overall, our results suggest that humans (i) employ hierarchical generative models to infer on the changing intentions of others, (ii) use volatility estimates to inform decision-making in social interactions, and (iii) integrate estimates of advice accuracy with non-social sources of information. The Bayesian framework presented here can quantify individual differences in these mechanisms from simple behavioral readouts and may prove useful in future clinical studies of maladaptive social cognition.
