Testing the Positive Theory of Government Finance

Researchers characterizing optimal tax policies for dynamic economies have reasoned that optimally chosen tax rates should approximately follow a random walk. We conduct a frequency-domain examination of the properties of the tax rate series and conclude that while there is a substantial smoothing role for debt, one rejects the hypothesis that the first difference in the series is white noise. This conclusion follows both from an analysis of the entire spectral distribution function of tax changes as well as from the behavior of individual frequencies. The source of the rejection is pronounced activity of tax changes at an eight year cycle which is suggestive of an electoral component to tax changes. Regression analysis confirms the finding that there is a cyclical component to tax changes corresponding to changes in political party administration. The results suggest that the positive theory of government finance needs to be refined to incorporate features of political equilibrium.

Sequential Banking.

The authors study environments in which agents may borrow sequentially from more than one leader. Although debt is prioritized, additional lending imposes an externality on prior debt because, with moral hazard, the probability of repayment of prior loans decreases. Equilibrium interest rates are higher than they would be if borrowers could commit to borrow from at most one bank. Even though the loan terms are less favorable than they would be under commitment, the indebtedness of borrowers is greater. Further, additional lending causes the probability of default to increase. The results apply to markets for consumer, corporate, and international debt. Copyright 1992 by University of Chicago Press.

HealthAgents: distributed multi-agent brain tumor diagnosis and prognosis

We present an agent-based distributed decision support system for the diagnosis and prognosis of brain tumors developed by the HEALTHAGENTS project. HEALTHAGENTS is a European Union funded research project, which aims to enhance the classification of brain tumours using such a decision support system based on intelligent agents to securely connect a network of clinical centres. The HEALTHAGENTS system is implementing novel pattern recognition discrimination methods, in order to analyse in vivo Magnetic Resonance Spectroscopy (MRS) and ex vivo/in vitro High Resolution Magic Angle Spinning Nuclear Magnetic Resonance (HR-MAS) and DNA micro-array data. HEALTHAGENTS intends not only to apply forefront agent technology to the biomedical field, but also develop the HEALTHAGENTS network, a globally distributed information and knowledge repository for brain tumour diagnosis and prognosis