Information theoretic measures of UHG graphs with low computational complexity

We introduce a novel graph class we call universal hierarchical graphs (UHG) whose topology can be found numerously in problems representing, e.g., temporal, spacial or general process structures of systems. For this graph class we show, that we can naturally assign two probability distributions, for nodes and for edges, which lead us directly to the definition of the entropy and joint entropy and, hence, mutual information establishing an information theory for this graph class. Furthermore, we provide some results under which conditions these constraint probability distributions maximize the corresponding entropy. Also, we demonstrate that these entropic measures can be computed efficiently which is a prerequisite for every large scale practical application and show some numerical examples. (c) 2007 Elsevier Inc. All rights reserved.

Some further implications of incorporating the warm glow of giving into welfare measures: A comment on the use of donation mechanisms by Champ et al

Champ er al. [J. Environ. Management 33, 151-162 (1997)] claim that they have (a) explored the welfare implication of warm glow for Hicksian measures of value for public goods and (b) established that actual donations place a