For an equivariant commutative ring spectrum R, ?0R has algebraic structure reflecting the presence of both additive transfers and multiplicative norms. The additive structure gives rise to a Mackey functor and the multiplicative structure yields the additional structure of a Tambara functor. If R is an N? ring spectrum in the category of genuine G-spectra, then all possible additive transfers are present and ?0R has the structure of an incomplete Tambara functor. However, if R is an N? ring spectrum in a category of incomplete G-spectra, the situation is more subtle. In this chapter, we study the algebraic theory of Tambara structures on incomplete Mackey functors, which we call bi-incomplete Tambara functors. Just as incomplete Tambara functors have compatibility conditions that control which systems of norms are possible, bi-incomplete Tambara functors have algebraic constraints arising from the possible interactions of transfers and norms. We give a complete description of the possible interactions between the additive and multiplicative structures.

Stories are a pervasive phenomenon of human life. They also represent a cognitive tool to understand and make sense of the world and of its happenings. In this contribution we describe a narratology-based framework for modeling stories as a combination of different data structures and to automatically extract them from news articles. We introduce a distinction among three data structures (timelines, causelines, and storylines) that capture different narratological dimensions, respectively chronological ordering, causal connections, and plot structure. We developed the Circumstantial Event Ontology (CEO) for modeling (implicit) circumstantial relations as well as explicit causal relations and create two benchmark corpora: ECB+/CEO, for causelines, and the Event Storyline Corpus (ESC), for storylines. To test our framework and the difficulty in automatically extract causelines and storylines, we develop a series of reasonable baseline systems

A variety of approaches exist for annotating temporal and event information in text, but it has been difficult to compare and contrast these different corpora. The Richer Event Description (RED) corpus, as an ambitious annotation of temporal, causal, and coreference annotation, provides one point of comparison for discussing how different annotation decisions contribute to the timeline and causal chains which define a document. We present an overview of how different event corpora differ and present new methods for studying the impact of these temporal annotation decisions upon the resulting document timeline. This focus on illuminating the contribution of three particular sources of information – event coreference, causal relations with temporal information, and long-distance temporal containment – to the actual timeline of a document. By studying the impact of specific annotation strategies and framing the RED annotation in the larger context of other event–event relation annotation corpora, we hope to provide a clearer sense of the current state of event annotation and of promising future directions for annotation.

In the past we experimented with variations of an approach we call semantic storytelling, in which we use multiple text analytics components including named entity recognition and event detection. This chapter summarizes some of our previous work with an emphasis on the detection of movement action events, and describes the long-term semantic storytelling vision as well as the setup and approach of our future work towards a robust technical solution, which is primarily driven by three industry use cases. Ultimately, we plan to contribute an implemented approach for semantic storytelling that makes use of various analytics services and that can be deployed in a flexible way in various industrial production environments.

A crucial aspect of understanding and reconstructing narratives is identifying the underlying causal chains, which explain why certain things happened and make a coherent story. To build such causal chains, we need to identify causal links between events in the story, which may be expressed explicitly as well as understood implicitly using commonsense knowledge.

This chapter reviews research efforts on the automated extraction of such event causality from natural language text. It starts with a brief review of existing causal models in psychology and psycholinguistics as a building block for understanding causation. These models are useful tools for guiding the annotation process to build corpora annotated with causal pairs. I then outline existing annotated resources, which are used to build and evaluate automated causality extraction systems. Furthermore, circumstantial events surrounding the causal complex are rarely expressed with language as they are part of common sense knowledge. Therefore, discovering causal common sense is also important to fill the gaps in the causal chains, and I discuss existing work in this line of research.

This chapter reviews the current landscape of ontological and lexical resources that motivated the development of the Rich Event Ontology (REO). Aimed at a whole that is greater than the sum of its parts, REO functions as a conceptual organization of event types that facilitates mapping between FrameNet, VerbNet, and the Entities, Relations, and Events corpus annotation from the Linguistic Data Consortium.

The definition of the homotopy limit of a diagram of left Quillen functors of model categories has been useful in a number of applications. In this chapter we review its definition and summarise some of these applications. We conclude with a discussion of why we could work with right Quillen functors instead, but cannot work with a combination of the two.

This chapter presents techniques for examining the distributional properties of narrative schemas in a subset of the New York Times (NYT) Corpus. In one technique, the narrative argument salience through entities annotated (NASTEA) task, we use the event participants indicated by narrative schemas to replicate salient entity annotations from the NYT Corpus. In another technique, we measure narrative schema stability by generating schemas with various permutations of input documents. Both of these techniques show differences between homogeneous and heterogeneous document categories. Homogeneous categories tend to perform better on the NASTEA task using fewer schemas and exhibit more stability, whereas heterogeneous categories require more schemas applied on average to peak in performance at the NASTEA task and exhibit less stability. This suggests that narrative schemas succeed at detecting and modeling the repetitive nature of template-written text, whereas more sophisticated models are required to understand and interpret the complex novelty found in heterogeneous categories.

Chapter 3 presents models in which artificial agents interact through games. It first introduces a typical mainstream economics model known as the prisoner’s dilemma, in which agents interact through a classic game theory game, and then contrast it with an artificial evolutionary game based on the same dilemma. In this evolutionary game, the dynamic evolution of a population of boundedly rational agents is represented and simulated using a genetic algorithm. It finally contrasts the assumptions of artificial economics against those of mainstream economics when modeling games.

There are various paradigms and programming methods suitable for different types of applications. The programming and simulation of an artificial economy can be carried out with standard methods, or through object-oriented programming, which is a very structured way of constructing computational objects, which are encapsulated in themselves, but can interact with other computational objects.

Chapter 8 presents the main positions in economics and in the social sciences regarding the agent/structure problem, and explores some contributions that can be made from artificial economics. First, it presents and discusses the individualist/reductionist, structuralist/holistic, and intermediate positions, regarding the agent/structure problem. Then presents simple artificial economics examples of the generation of endogenous preferences, agents' behavioral changes derived from their economic interaction, and of the demographic effects of the introduction of a market institution into an artificial economy.