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Purpose – This chapter discusses the role of emotion expression in decision-making. To understand connections between emotion and decision it is helpful first to differentiate between emotion experience and emotion expression. Understanding how emotion expression influences decision-making is important as a practical matter. However, in contrast to emotion experience, economic research has paid little attention to the significance of emotion expression in decision-making.

Approach – I review recent studies on emotion expression, paying specific attention to possible connections between emotion expression, punishment, fair economic exchange, and well-being.

Practical implications – In contrast to emotions, which are typically difficult to control, I suggest that opportunities for emotion expression can feasibly be manipulated through appropriately designed policies. I further suggest that this approach may have the ability to positively affect well-being and economic outcomes.

Value of the chapter – The chapter provides new perspectives on how policy-makers can benefit by understanding the effect of emotion expression in decision-making. The chapter also suggests future research to improve our understanding of emotion expression.

Incivility is widespread in the workplace and has been shown to have significant affective and behavioral consequences. However, the authors still have a limited understanding as to whether, how and when discrete incivility events impact team performance. Adopting a resource depletion perspective and focusing on the cognitive implications of such events, the authors introduce a multi-level model linking the adverse effects of such events on team members’ working memory – the “workbench” of the cognitive system where most planning, analyses, and management of goals occur – to team effectiveness. The model which the authors develop proposes that that uncivil interpersonal behavior in general, and rudeness – a central manifestation of incivility – in particular, may place a significant drain on individuals’ working memory capacity, affecting team effectiveness via its effects on individual performance and coordination-related team emergent states and action-phase processes. In the context of this model, the authors offer an overarching framework for making sense of disparate findings regarding how, why and when incivility affects performance outcomes at multiple levels. More specifically, the authors use this framework to: (a) suggest how individual-level cognitive impairment and weakened coordinative team processes may mediate these incivility-based effects, and (b) explain how event, context, and individual difference factors moderators may attenuate or exacerbate these cognition-mediated effects.

Laboratory studies of social interaction have revealed a wide range of phenomena that are difficult to explain using standard economic models. For example, people will often sacrifice their own earnings in order to be generous, cooperative, punitive, and retributive in interactions with anonymous strangers. “Behavioral” models that redefine agents’ preferences attempt to provide an account of these phenomena as reflecting a “taste for fairness” or altruism, aversion to inequality, concern about others’ beliefs, and so on. Such models either fail to account for the rich sensitivity of actions to context or in allowing for rich context-dependence, these models ultimately substitute description for explanation. Hayek’s work provides a foundation for thinking about how to explain these phenomena, by conceiving of people as both purpose-seeking (as in economic models) and rule-following. Decisions are shaped both by material interests and by a normative framework that is evoked by context and helps people decide what one ought to do in a particular situation. The implication of this approach is that rather than trying to understand heterogeneity across individuals in terms of preferences, experimenters should instead try to understand heterogeneity across contexts in terms of the rules and norms that operate in the background and guide or constrain people’s purpose-seeking tendencies. What economics needs, then, is a theory of how and why these rules and norms vary with context as they do.

Neuroeconomics is the study of how the brain makes economic decisions. By its nature neuroeconomics studies the mechanisms of decision-making, assumed to be computational, in order to better understand the strategies people use and the choices that people make. The focus of this book is how neuroeconomics connects to health economics in a way that improves our understanding of health care and treatment decisions. This is natural for several reasons. First, the brain and the body are intimately connected to each other and the health of one depends on the other. Second, the health system is inherently about decisions. Decisions to stay healthy, decisions to diagnose illness, decisions to treat, decisions to invest in new treatments, decisions to insure, and decisions to pay. Finally, these decisions can be difficult, as the media's consistent attention to this area attests. In light of this, for this volume we chose to include chapters that review basic research on emotion or social preference that have direct relevance to decisions in health economics. We have also included chapters that refer more specifically to some aspect of people's health care or treatment decisions. In the following we indicate the chapters within each topic area. Although many chapters could arguably fit in multiple categories, we have listed each chapter only once and without particular order.

Is social network analysis just measures and methods with no theory? We attempt to clarify some confusions, address some previous critiques and controversies surrounding the issues of structure, human agency, endogeneity, tie content, network change, and context, and add a few critiques of our own. We use these issues as an opportunity to discuss the fundamental characteristics of network theory and to provide our thoughts on opportunities for future research in social network analysis.

This chapter used empirical data from five developed markets and five emerging markets to perform an examination of anomalies using common financial economic approaches along with more innovative econometric models. Of the methodologies used to test for anomalies, the data-driven panel and quantile regressions were empirically found to be better suited over the traditionally common approaches to describe the non-linear, switching behavior of the anomalies. In the developed markets, the statistically significant small firms (size) had the highest average returns. In the developing markets, the lower price-to-earnings (P/E) ratios (value) had the highest average returns. In addition, the research found (1) a small country effect, (2) sales had a negative relationship with returns, and (3) a lower (higher) book-to-market (B/M) was associated with higher returns in the developed (developing) markets, indicating investors received a higher premium for growth (value) equities. The semi-strong form of the efficient market hypothesis was also found to be violated. The anomalies’ behavior varied between sorted portfolios, industries, and developed to emerging markets; though it was found to be consistent through time (not disrupted by bear or bull markets).

This paper addresses the issue of granularity. The authors argue that in routine dynamics research granularity can be usefully defined by the number of actors, the variety of places/contexts, and the amount of time it takes to successfully accomplish an action. This is an important but often overlooked aspect of studying routines. It is important because different granularities imply different challenges and opportunities for performing and patterning. The authors propose a framework to distinguish between fine-, medium-, and coarse-grained actions, illustrate how different granularities have been used in existing routine dynamics studies, and discuss the implications for understanding routine dynamics. The authors conclude that granularity is a key construct that needs to be taken seriously and suggest a four-step procedure to help researchers establish and report on the granularity of actions in their research process.