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It is well-established that market governance can be provided by both public (state) and private organizations. However, the concept of private governance has been used, this article contends, to refer to two distinct forms of non-state governance: private governance and community governance. We distinguish between these two forms, arguing that private governance should be understood as the provision of market governance by (external) private parties, while community governance refers to a process where a group, a community, or society has the autonomy to govern its own affairs without interference from external authorities. The former internalizes the externalities associated with governance, while the latter comes about mainly as an unintended externality of social interaction in markets. To further illuminate the differences, and the relative strengths of these types of non-state governance, we distinguish among three elements of market governance: (1) the formation and interpretation of rules, (2) the administration of rules of ownership and exchange, and (3) the enforcement of rules. We argue that community governance is of great relevance for the formation and interpretation of the rules of ownership and exchange, which is consequently very hard to outsource to external parties, private or public. Community governance also plays a frequently overlooked role in administration and enforcement through the process of co-production. Rule formation and interpretation are theorized as the epistemic components of market governance, which can be analyzed within the Governing Knowledge Commons framework. [ABSTRACT FROM AUTHOR]

In the 2011 Franz Cuhel Memorial Lecture, I argue that the study of endogenous rule formation in economic life (what I term the positive political economy of anarchism) should be studied in-depth and that the economic analysis of the Austrian school of economics provides many of the key analytical insights necessary for such study. [ABSTRACT FROM AUTHOR]

Automatic Feature Recognition (AFR) techniques are an important tool for achieving a true integration of design and manufacturing stages during the product development. In particular, AFR systems offer capabilities for recognising high-level geometrical entities, features, in Computer-Aided Design (CAD) models. However, the recognition performances of most of the existing AFR systems are limited to the requirements of specific applications. This paper presents automatic knowledge acquisition techniques to support the development of AFR systems that could be deployed in different application domains. In particular, a method to generate automatically feature recognition rules is proposed. These rules are formed by applying an inductive learning algorithm on training data consisting of feature examples. In addition, a technique for defining automatically feature hints from such rule sets is described. The knowledge acquisition techniques presented in this study are implemented within a prototype feature recognition system and its capabilities are verified on two benchmarking parts. [ABSTRACT FROM AUTHOR]

COVIS (COmpetition between Verbal and Implicit Systems; Ashby, Alfonso-Reese, & Waldron, 1998) is a prominent model of categorization which hypothesizes that humans have two independent categorization systems - one declarative, one associative - that can be recruited to solve category learning tasks. To date, most COVIS-related research has focused on just two experimental tasks: linear rule-based (RB) tasks, which purportedly encourage declarative rule use, and linear information-integration (II) tasks, which purportedly require associative learning mechanisms. We introduce and investigate a novel alternative: the concentric-rings task, a nonlinear category structure that both humans and pigeons can successfully learn and transfer to untrained exemplars. Yet, despite their broad behavioral similarities, humans and pigeons achieve their successful learning through decidedly different means. As predicted by COVIS, pigeons appear to rely solely on associative learning mechanisms, whereas humans appear to initially test but subsequently reject unidimensional rules. We discuss how variants of our concentric-rings task might yield further insights into which category-learning mechanisms are shared across species, as well as how categorization strategies might change throughout training. [ABSTRACT FROM AUTHOR]