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Rather than organize as traditional firms, many of today’s companies organize as platforms that sit at the nexus of multiple exchange and production relationships. This chapter considers a most basic question of organization in platform contexts: the choice of boundaries. Herein, I investigate how classical economic theories of firm boundaries apply to platform-based organization and empirically study how executives made boundary choices in response to changing market and technical challenges in the early mobile computing industry (the predecessor to today’s smartphones). Rather than a strict or unavoidable tradeoff between “openness-versus-control,” most successful platform owners chose their boundaries in a way to simultaneously open-up to outside developers while maintaining coordination across the entire system.

In this chapter, we explore whether various true, endogenous social cycle theories share common patterns and characteristics.

We examine a number of prominent social theories describing cyclical patterns, and attempt to abstract an ideal type common to all of them, based on the idea of two populations disrupting each other and adjusting to the other’s disruptions.

At the core of such theories we typically find a variation of a two-population model. In these theories, cycles emerge when one of the populations seems to disrupt the other population’s plans, leading to recurring adjustments and disruptions that constitute the cycle.

Finding such commonalities in the world of theories can be useful for several reasons. For one thing, noticing that two theories share certain traits may help us understand each of them better. Furthermore, we show that agent-based modelers using modern object-oriented programming techniques can benefit from finding common patterns in theories.

While the term “humanities” is not in itself a particularly contentious one among academics, the addition of the term “digital” creates all sorts of problems, even the superficially illogical contention that digital humanities are not humanities at all. The fundamental rupture between digital and print humanities lies in the turning of a materialist, object-oriented analysis upon the practices of humanistic scholarship. That is, in their newness, the digital humanities are unsurprisingly self-reflective about the materiality of their scholarly practices. This self-reflection has been largely absent from traditional humanities where we had all but naturalized the material composition of dissertations, journal articles, monographs, and so on. As a result, even as we continue to pursue traditional scholarly methods, it becomes increasingly difficult to do so without a self-reflective awareness of the historical-material contingency of these practices. In short, they are no longer the same. To explore this issue, this chapter takes up assemblage theory, and actor-network theory to investigate the intersection of mobile technologies and social media in the digital humanities including conference backchannels and networked research communities mediated through Twitter, Google Buzz, and similar applications. The chapter considers how, even for those who continue to publish in traditional genres on traditional subjects, the development of these digital assemblages are transforming compositional practices.

Massively parallel desktop computing capabilities now well within the reach of individual academics modify the environment for posterior simulation in fundamental and potentially quite advantageous ways. But to fully exploit these benefits algorithms that conform to parallel computing environments are needed. This paper presents a sequential posterior simulator designed to operate efficiently in this context. The simulator makes fewer analytical and programming demands on investigators, and is faster, more reliable, and more complete than conventional posterior simulators. The paper extends existing sequential Monte Carlo methods and theory to provide a thorough and practical foundation for sequential posterior simulation that is well suited to massively parallel computing environments. It provides detailed recommendations on implementation, yielding an algorithm that requires only code for simulation from the prior and evaluation of prior and data densities and works well in a variety of applications representative of serious empirical work in economics and finance. The algorithm facilitates Bayesian model comparison by producing marginal likelihood approximations of unprecedented accuracy as an incidental by-product, is robust to pathological posterior distributions, and provides estimates of numerical standard error and relative numerical efficiency intrinsically. The paper concludes with an application that illustrates the potential of these simulators for applied Bayesian inference.

In his discussion of early lexical development in children Barrett (1995) notes that utterances can be of the following types: an expression of an internal state, a response to a specific context, a social-pragmatic utterance and a referential utterance. A referential utterance can be thought of in the following way: the thing referred to, a mental representation, a word representation and a word sound. An utterance may refer to an object, an action, an attribute or an event. Some utterances are used as the names of classes of objects while other utterances are used as the proper names of individual objects. Looking at this in abstract we might say that, in the early years of childhood, language is used to refer to elements, sets, functions and relations – in other words to the mathematical structures which were discussed in Chapter 2. Of course although early language is used to refer to mathematical objects, the character of the language itself takes the form of ordinary language.