Negotiation support using the Graph Model for Conflict Resolution

The Graph Model for Conflict Resolution is a flexible methodology for systematically studying strategic conflicts in the real world, and is therefore a natural tool for negotiation support. The basic definitions underlying the graph model are reviewed, and the techniques for analysis and interpretation are discussed. The modeling and analysis of a case study, an international trade negotiation concerning the export of Canadian softwood lumber to the United States, are used to demonstrate the practical application of the Graph Model for Conflict Resolution as a negotiation support tool. The modeling and analysis is carried out using the GMCR software system. The ability of the Graph Model for Conflict Resolution to provide insights and advice to negotiators is emphasized.     

International trade in a differentiated good: Trade elasticities in the world rice market

In many previous rice trade models, the commodity has been regarded as a homogeneous product. However, homogeneity is not an appropriate assumption, given the various types of rice that are traded and consumed. Parameters estimated from these models, therefore, do not reflect the real world market for rice and, hence, may mislead decision makers who use the results for policy evaluation purposes. This study uses an Armington approach to model the world rice trade as a differential good market and to derive trade elasticity parameters.     

The Economics and Financing of Hospitals in Australia

The Australian hospital industry is described and it is noted that the term ‘hospital’ is applied to a wide range of institutions. The main problems and issues associated with hospitals are discussed, including the lack of adequate methods of measuring their ‘products’ and of allocating funds to them. The role of the medical staff in influencing the ‘demand’ for hospital beds and in resource allocation is emphasised as a major influence on hospital behaviour. In a study of the determinants of the costs of Victorian public hospitals it was found that the complexity of the case-mix as measured by diagnosis related groups (DRGs) was a major determinant of teaching hospital costs but not of those of other large hospitals. DRG-based funding and costing of hospitals is recommended as a strategy for achieving increased efficiency.     

Neuroeconomics: Why Economics Needs Brains

Neuroeconomics uses knowledge about brain mechanisms to inform economic theory. It opens up the “black box” of the brain, much as organizational economics opened up the theory of the firm. Neuroscientists use many tools—including brain imaging, behavior of patients with brain damage, animal behavior and recording single neuron activity. The key insight for economics is that the brain is composed of multiple systems which interact. Controlled systems (“executive function”) interrupt automatic ones. Brain evidence complicates standard assumptions about basic preference, to include homeostasis and other kinds of state‐dependence, and shows emotional activation in ambiguous choice and strategic interaction.     

Simulating the Evolution of the Implied Distribution

Motivated by the implied stochastic volatility literature (Britten–Jones and Neuberger, forthcoming; Derman and Kani, 1997; Ledoit and Santa–Clara, 1998) this paper proposes a new and general method for constructing smile–consistent stochastic volatility models. The method is developed by recognising that option pricing and hedging can be accomplished via the simulation of the implied risk neutral distribution. We devise an algorithm for the simulation of the implied distribution, when the first two moments change over time. The algorithm can be implemented easily, and it is based on an economic interpretation of the concept of mixture of distributions. It can also be generalised to cases where more complicated forms for the mixture are assumed.     

The Role of Holding Company Financial Information in the Insurer-Rating Process: Evidence From the Property-Liability Industry

We examine data for the year ended December 31, 1997 for 80 publicly traded property‐liability insurers that have Best financial strength ratings of their consolidated insurance‐operating subsidiaries. These firms employ a holding company structure, in which a parent owns the stock of multiple insurance‐operating subsidiaries. The operating subsidiaries prepare a consolidated annual report using the Statutory Accounting Principles (SAP), and an analogous set of financial statements based on the Generally Accepted Accounting Principles (GAAP) is released by the parent. We find that the financial characteristics important in determining ratings at the individual firm level—capitalization, liquidity, profitability, and size—are also important at the group level. Further, financial ratios from holding company statements are incrementally useful in the ratings' process, after group‐level ratios have been taken into account. Robustness tests based on a subsample of holding companies with minimal investment outside of the property‐liability industry reinforce our conclusion that parent company statements influence consolidated group ratings. However, our data do not allow us to separate the relative contribution of the GAAP model and underlying transactions to the ratings decision.