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Building on work that explores the relationship between individual beliefs and ability to recognize discrimination (e.g., Kaiser and Major, 2006), we examine how an adherence to beliefs about gender essentialism, gender egalitarianism, and meritocracy shape one’s interpretation of an illegal act of sexual harassment involving a male supervisor and female subordinate. We also consider whether the role of the gendered culture of engineering (Faulkner, 2009) matters for this relationship. Specifically, we conducted an online survey-experiment asking individuals to report their beliefs about gender and meritocracy and subsequently to evaluate a fictitious but illegal act of sexual harassment in one of two university research settings: an engineering department, a male-dominated setting whose culture is documented as being unwelcoming to women (Hatmaker, 2013; Seron, Silbey, Cech, and Rubineau, 2018), and an ambiguous research setting. We find evidence that the stronger one’s adherence to gender egalitarian beliefs, the greater one’s ability to detect inappropriate behavior and sexual harassment while gender essentialist beliefs play no role in their detection. The stronger one’s adherence to merit beliefs, the less likely they are to view an illegal interaction as either inappropriate or as sexual harassment. We account for respondent knowledge of sexual harassment and their socio-demographic characteristics, finding that the former is more often associated with the detection of inappropriate behavior and sexual harassment at work. We close with a discussion of the transferability of results and policy implications of our findings.

We replicate and extend the social history treatment of the Berg, Dickhaut, and McCabe (1995) investment game, to further document how the reporting of financial history influences how laboratory societies organize themselves over time. We replicate Berg et al. (1995) by conducting a No History and a Financial History session to determine whether a report summarizing the financial transactions of a previous experimental session will significantly reduce entropy in the amounts sent by Investors and returned by Stewards in the investment game, as Berg et al. (1995) found. We extend Berg et al. (1995) in two ways. First, we conduct a total of five sessions (one No History and four Financial History sessions). Second, we introduce Shannon’s (1948) measure of entropy from information theory to assess whether the introduction of financial transaction history reduces the amount of dispersion in the amounts invested and returned across generations of players. Results across sessions indicate that entropy declined in both the amounts sent by Investors and the percentage returned by Stewards, but these patterns are weaker and mixed compared to those in the Berg et al. (1995) study. Additional research is needed to test how initial conditions, path dependencies, actors’ strategic reasoning about others’ behavior, multiple sessions, and communication may mediate the impact of financial history. The study’s multiple successive Financial History sessions and entropy measure are new to the investment game literature.

The Legends in Marketing series presents compilations of the seminal works of marketing scholars who have made significant contributions to the discipline of marketing. This review discusses the structure and contents of the volumes that comprise Legends in Marketing: Shelby D. Hunt (Sage, forthcoming).

Despite a legacy of research that emphasizes contradictions and their role in explaining change, less is understood about their character or the mechanisms that support them. This gap is especially problematic when making causal claims about the sources of institutional change and our overall conceptions of how institutions matter in social meanings and organizational practices. If we treat contradictions as a persistent societal feature, then a primary analytic task is to distinguish their prevalence from their effects. We address this gap in the context of US electoral discourse and education through an analysis of presidential platforms. We ask how contradictions take hold, persist, and might be observed prior to, or independently of, their strategic use. Through a novel combination of content analysis and computational linguistics, we observe contradictions in qualitative differences in form and quantitative differences in degree. Whereas much work predicts that ideologies produce contradictions between groups, our analysis demonstrates that they actually support convergence in meaning between groups while promoting contradiction within groups.

In this article, we survey experiments that are directly related to monetary policy and central banking. We argue that experiments can also be used as a tool for central bankers for bench testing policy measures or rules. We distinguish experiments that analyze the reasons for non-neutrality of monetary policy, experiments in which subjects play the role of central bankers, experiments that analyze the role of central bank communication and its implications, experiments on the optimal implementation of monetary policy, and experiments relevant for monetary policy responses to financial crises. Finally, we mention open issues and raise new avenues for future research.

This chapter describes a neurodynamic modeling approach which may be useful for dynamically assessing teamwork in healthcare and military situations. It begins with a description of electroencephalographic (EEG) signal acquisition and the transformation of the physical units of EEG signals into quantities of information. This transformation provides quantitative, dynamic, and generalizable neurodynamic models that are directly comparable across teams, tasks, training protocols, and team experience levels using the same measurement scale, bits of information. These bits of information can be further used to dynamically guide team performance or to provide after-action feedback that is linked to task events and team actions.

These ideas are instantiated and expanded in the second section of the chapter by showing how these data abstractions, compressions, and transformations take advantage of the natural information redundancy in biologic signals to substantially reduce the number of data dimensions, making the incorporation of neurodynamic feedback into Intelligent Tutoring Systems (ITSs) achievable.