"Profitability and the Time-Varying Liquidity Premium in the Term Structure of Interest Rates"

There have been numerous empirical studies of the term structure. Broadly, the evidence may be said to be consistent with some influence from expectations plus the existence of a liquidity premium. Long rates or the spread between long and short rates have seemed to be systematically related to expectations of future rates, though the expectations embodied in long rates or the spread are biased upwards as the liquidity preference theory would predict. The degree of influence of expectations and the behavior of the liquidity premium, however, have remained matters of controversy. In several recent studies (e.g., Robert Shillert 1979; Shiller, John Campbell, and Kermit Schoenholtz, 1983; David Jones and Vance Roley, 1983; Mankiw and Summers, 1984; and Mankiw, 1986) the expectations theory has performed poorly, even allowing for the existence of a constant liquidity premium, in attempts to test the joint hypothesis of rational expectations and the expectations theory. Shiller, Campbell, and Schoenholtz and Mankiw and Summers, among others, have suggested renewing the search for the determinants of a time-varying liquidity premium as a possibility for explaining what is going on but have had little success themselves in finding such.

Exploring the use of Controlled English for communication with ACT-R agents

Research is being undertaken into sense-making by collaborative agents, based upon a cognitive framework of human behaviour, ACT-R, together with communication between the agents. We explore the use of Controlled English for this purpos

A Formal Context Representation Framework for Network-Enabled Cognition

Network-accessible resources are inherently contextual with respect to the specific situations (e.g., location and default assumptions) in which they are used. Therefore, the explicit conceptualization and representation of contexts is required to address a number of problems in Network- Enabled Cognition (NEC). We propose a context representation framework to address the computational specification of contexts. Our focus is on developing a formal model of context for the unambiguous and effective delivery of data and knowledge, in particular, for enabling forms of automated inference that address contextual differences between agents in a distributed network environment. We identify several components for the conceptualization of contexts within the context representation framework. These include jurisdictions (which can be used to interpret contextual data), semantic assumptions (which highlight the meaning of data), provenance information and inter-context relationships. Finally, we demonstrate the application of the context representation framework in a collaborative military coalition planning scenario. We show how the framework can be used to support the representation of plan-relevant contextual information