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The cognitive sciences, having emerged in the second half of the twentieth century, are recently experiencing a spectacular renewal, which cannot leave unaffected any discipline that deals with human behavior. The primary motivation for our project has been to weigh up the impact that this ongoing revolution of the sciences of the mind is likely to have on social sciences – in particular, on economics. The idea was to gather together a diverse group of social scientists to think about the following questions. Have the various new approaches to cognition provoked a crisis in economic science?¹ Should we speak of a scientific revolution (in the sense of Kuhn) also in contemporary social sciences, occurring under the growing influence of the cognitive paradigm? Above all, can a more precise knowledge of the complex functioning of the human mind and brain advance in any way the understanding of economic decision-making?

Data visualizations in some form or another have served as decision-support tools for many centuries. In conjunction with advancements in information technology, data visualizations have become more accessible and more efficient to generate. In fact, virtually all enterprise resource planning and human resource (HR) information system vendors offer off-the-shelf data visualizations as part of decision-support dashboards as well as stand-alone images and displays for reporting. Plus, advances in programing languages and software such as Tableau, Microsoft Power BI, R, and Python have expanded the possibilities of fully customized graphics. Despite the proliferation of data visualization, relatively little is known about how to design data visualizations for displaying different types of HR data to different user groups, for different purposes, and with the overarching goal of improving the ways in which users comprehend and interpret data visualizations for decision-making purposes. To understand the state of science and practice as they relate to HR data visualizations and data visualizations in general, we review the literature on data visualizations across disciplines and offer an organizing framework that emphasizes the roles data visualization characteristics (e.g., display type, features), user characteristics (e.g., experience, individual differences), tasks, and objectives (e.g., compare values) play in user comprehension, interpretation, and decision-making. Finally, we close by proposing future directions for science and practice.

As the cognitive sciences – particularly neuroscience, cognitive psychology, and a rejuvenated artificial intelligence movement that has largely abandoned the model of the mind as a formal machine – have seen major development over the past quarter-century, it is inevitable that the findings thrown up by this ‘cognitive revolution’ should be examined for their relevance to the understanding of economic behavior. This ongoing examination has tended to emphasize those characteristics of human cognitive capabilities that call into question the descriptive adequacy of the rational-choice model, focusing on departures from individual rationality that may have economic consequences at the market level.¹ Such a move may be the obvious one for an economist confronted with this interdisciplinary challenge, but it is not the only one. The new insights into the functioning of the brain can also be deployed in the understanding of complex systems in general – and of specific social arrangements in particular – and that is the direction taken here. By critically examining the systemic similarities and differences between the social arrangements of science and market, the aim is to show how a complex systems approach, inspired by developments in cognitive psychology but applying these at the level of the system rather than of the individual, can provide a new and useful way of understanding social systems.

The question facing sociology is whether it is a field or a discipline. If it is a field, then there is no need for theorizing. However, if sociology is a discipline, then problem-solving cannot be disentangled from theorizing without a loss of intelligibility – the inability to explain the social as the concept of the discipline. Through the quasi-realism of problem-solving as a course of activity, this chapter presents cognitive sociology as a paradigm appropriate to the concept of the social understood as an ongoing course of activity. In doing so, it is shown how bounded rationality and expertise play a crucial role in how communication interacts with the division of cognitive labor, especially through the idea of representational representationality. Representational representationality is an idea that reveals how the degree of clarity among language, meaning, and thought is relative to the issues of audience and ignorance. Representational representationality is significant because it demonstrates how the relationship among meaning, language, and thought is subject to communicative errors – errors arising from a predicament of intelligibility and not merely arising from issues of computational skill, as described by Herbert Simon's model of bounded rationality and expertise in human problem-solving. The argument that follows from this shows how the means for adapting to ambiguity amounts to the difference between Simon's model and a quasi-real model in terms of its principle of rationality, principle of efficiency, and its cognitive style of problem-solving for deliberate practice. These dimensions are shown to effect what “examples” are good for in the problem-solving process, thereby revealing the politics of expertise. The politics of expertise demonstrates how the conflicts in sociological explanations of strategy are not merely conflicts that can be set aside as a pluralism of values. Rather, the conflicting explanations of theory and theorizing can only be resolved when the situational rationality of sociology as a discipline realizes the quasi-realism of problem-solving as a course of activity.

This paper conceives of Hayek's overall project as presenting a theory of sociocognition, explication of which has a two-fold purpose: (1) to locate Hayek within the non-Cartesian tradition of cognitive science, and (2) to show how Hayek's philosophical psychology infuses his social theory.

The chapter examines to what extent research from social cognitive neuroscience can inform ethical leadership. We evaluate the contribution of brain research to the understanding of ethical leaders as moral persons as well the understanding of their role as moral managers. The areas of social cognitive neuroscience that mirror these two aspects of ethical leadership comprise research relating to understanding oneself, understanding others, and the relationship between the self and others. Within these, we deem it relevant for ethical leadership to incorporate research findings about self-reflection, self-regulation, theory of mind, empathy, trust, and fairness. The chapter highlights social cognitive neuroscience research in these areas and discusses its actual and potential contributions to ethical leadership. The chapter thereby engages also with the broader discussion on the neuroscience of leadership. We suggest new avenues for future research in the field of leadership ethics and responsibility.

Team cognition research continues to evolve as the need for understanding and improving complex problem solving itself grows. Complex problem solving requires members to engage in a number of complicated collaborative processes to generate solutions. This chapter illustrates how the Macrocognition in Teams model, developed to guide research on these processes, can be utilized to propose how intelligent tutoring systems (ITSs) could be developed to train collaborative problem solving. Metacognitive prompting, based upon macrocognitive processes, was offered as an intervention to scaffold learning these complex processes. Our objective is to provide a theoretically grounded approach for linking intelligent tutoring research and development with team cognition. In this way, team members are more likely to learn how to identify and integrate relevant knowledge, as well as plan, monitor, and reflect on their problem-solving performance as it evolves. We argue that ITSs that utilize metacognitive prompting that promotes team planning during the preparation stage, team knowledge building during the execution stage, and team reflexivity and team knowledge sharing interventions during the reflection stage can improve collaborative problem solving.