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To reconcile theoretical discrepancies between discrete emotion, dimensional emotion (positive vs. negative affect), and the circumplex model, we propose the bifurcation model of affect structure (BMAS). Based on complexity theory, this model explores how emotion as an adaptive complex system reacts to affective events through negative and positive feedback loops, resulting in self-organizing oscillation and transformations between three states: equilibrium emotion, discrete positive and negative emotion in the near-equilibrium state, and chaotic emotion. We argue that the BMAS is superior to the extant models in revealing the dynamic connections between emotions and the intensity of affective events in organizational settings.

In a computer-based experimental study, we explored intensity of pleasant and unpleasant emotional experiences (affect), following immediate outcomes of risky choices over time under three levels of uncertainty (80%, 50%, 20%). We found that the intensity of pleasant affect initially increased linearly before suddenly reducing after the seventh task, and then resumed the linear upward trend. In contrast, the intensity of unpleasant affect cyclically changed after every five decision tasks, displaying a wave-like pattern. Interestingly, the 50% probability (maximum information entropy) group demonstrated patterns quite different to the other two groups (20%, 80%). For pleasant affect, this group reduced in positive affect significantly more than the other two groups after the seventh decision task. For unpleasant affect, the 50% group displayed an increasing negative affect trend, while the other two groups displayed a reducing negative affect trend. In sum, our findings reveal different temporal patterns of pleasant emotions from correct decisions and unpleasant emotions resulting from wrong decisions. We conclude that, consistent with the self-organization theory, these differences reflect nonlinear changes in the emotional system to cope with the challenge of uncertainty (or entropy).

This chapter discusses the emerging network science approach to the study of complex adaptive systems and applies tools derived from statistical physics to the analysis of tourism destinations. The authors provide a brief history of network science and the characteristics of a network as well as different models such as small world and scale free networks, and dynamic properties such as resilience and information diffusion. The Italian resort island of Elba is used as a case study allowing comparison of the communication network of tourist organizations and the virtual network formed by the websites of these organizations. The study compares the parameters of these networks to networks from the literature and to randomly created networks. The analyses include computer simulations to assess the dynamic properties of these networks. The results indicate that the Elba tourism network has a low degree of collaboration between members. These findings provide a quantitative measure of network performance. In general, the application of network science to the study of social systems offers opportunities for better management of tourism destinations and complex social systems.

One crucial but sometimes overlooked fact regarding the difference between observation in the cross-section and observation over time must be stated before proceeding further. Tempting though it is to draw conclusions about the dynamics of a process from cross-sectional observations taken as a snapshot of that process, it is a fallacious practice except under a very precise condition that is highly unlikely to obtain in processes of interest to the social scientist. That condition is known as ergodicity.

We examine the garbage can model as a case of organizational decision making under temporal order constraints such as in the case of a dispatcher on whose desk problems, solutions, and choice opportunities arrive in a stochastic order and get resolved at particular points in time. We follow the original (Cohen, March, & Olsen, 1972) simulation except their particular setup where the problems and decision makers have access to information regarding the decisions made up to the end of the previous period a rule that we consider to be myopic. Making the assignment (or rule) less myopic would be to provide information about problems that were attached in the current period and about decision makers that were assigned a choice this period to problems and to decision makers. We also bring in an expediter who can select one or two choices to expedite each period. The expediter is endowed with a total energy of 55 that can be expended on resolving any choice to decision. Each period after the problems are attached and decision makers assigned to choices, the expediter scans the choices. The expediter selects the two choices that are the closest to decision and expends energy to move them to decision. The simulations we ran show improvement in certain situations as measured by the number of unsolved problems. We discuss our results in the sense of providing simple design features to a complex decision situation. We also discuss the paradigm of the garbage can model in the larger context of organizational decision making.

While many inconsistencies can be found in Marx's theory if one chooses a view of reality in which time is absent, these inconsistencies disappear if the view is taken that time is an essential component of that theory. The debate is thus between the simultaneist and the temporalist camp. This article sides with the temporalist approach but at the same time it argues that both sides have focused mainly on quantitative and formal logic aspects. This is the limit of the debate. The debate should move on from being only a critique and counter-critique of each other applying only formal logic to the issue of consistency to showing how and whether the different postulates (a time-less versus a time-full reality) and the interpretations deriving from them are an instance of a wider theory of radical social change. From this angle, simultaneism implies equilibrium and thus a view of the economy tending toward its equilibrated reproduction. Capitalism is thus theorized as an inherently rational system and any attempt to supersede it is irrational. This is simultaneism's social content. Temporalism, if immersed in a dialectical context, reaches the opposite conclusions: the economy is in a constant state of nonequilibrium and tends cyclically toward its own supersession. Capitalism is inherently irrational and any attempt to supersede it is rational. Simultaneist authors should now show how their approach to the issue of consistency fits into a broader theory furthering the liberation of Labor.

To choose a dialectical view of temporalism is thus to take sides for Labor.