Several empirical investigations indicate that family firms are more innovative under the founding generation’s leadership and become less in later stages, while others state opposite. Within this debate, limited attention has been devoted to understanding how intra-family succession might be an opportunity maintain or improve firms’ innovativeness. This paper aims explore innovativeness may ev...

We establish that for every computably enumerable (c.e.) Turing degree b, the upper cone of c.e. Turing degrees determined by b is the degree spectrum of the successor relation of some computable linear ordering. This follows from our main result, that for a large class of linear orderings, the degree spectrum of the successor relation is closed upward in the c.e. Turing degrees.

Shannon entropy was defined for probability distributions and then its using was expanded to measure the uncertainty of knowledge for systems with complete information. In this article, it is proposed to extend the using of Shannon entropy to under-defined or over-defined information systems. To be able to use Shannon entropy, the information is normalized by an affine transformation. The const...

We show how to develop a purely functional algorithm that computes maximum matchings in bipartite graphs by using relation algebra. Our algorithm is based upon the representation of graphs by lists of successor lists and a generalisation to specific container types is discussed. The algorithm itself can be implemented in Haskell and we will provide a complete implementation using the successor ...

Successor variety is a commonly used measure for segmentation in language processing. It is based on a simple idea that large variety of letters (or phonemes) following an initial word (or utterance) segment indicates a possible boundary. It dates back to Harris (1955), and several methods based on successor variety have been used in the literature, particularly for the purposes of segmenting w...

To understand animals’ behavior in finding relations between similar tasks and adapting themselves to changes the tasks, it is necessary know how brain generalizes learned knowledge from a previous task unseen tasks. Recent studies neuroscience suggest that Successor Representation (SR)-based models provide adaptation goal locations or reward function faster than model-free algorithms, together...