The SEIQS stochastic epidemic model with external source of infection

This paper deals with a stochastic epidemic model for computer viruses with latent and quarantine periods, and two sources of infection: internal and external. All sojourn times are considered random variables which are assumed to be independent and exponentially distributed. For this model extinction and hazard times are analyzed, giving results for their Laplace transforms and moments. The transient behavior is considered by studying the number of times that computers are susceptible, exposed, infectious and quarantined during a period of time (0, t] and results for their joint and marginal distributions, moments and cross moments are presented. In order to give light this analysis, some numerical examples are showed.

Employee-owned firms: a labour model with potential?

In Spain, the companies that are mainly owned by the employees form a part of the Social Economy and offer an alternative business model, which is found in a conventional capitalist economy. The objective of this study is to establish whether there are significant differences in the performance of Employee Owned Firms (EOFs) and more conventionally structured businesses, non-Employee Owned Firms (non-EOFs), due to the inherent differences in the capital-ownership structure. The aim is to establish whether or not a corporate governance structure characterised by the employee participation for both the financial and the informational decision-making aspects can be advocated. The results show differences in favour of the conventional non-EOFs for various indicators measuring economic performance and confirm the different objectives of each business type; however, they provide evidence of significant differences in favour of the EOFs in terms of the efficient use of the capital and labour factors of production, according to the theoretical literature.

The difference model with guessing explains interval bias in two-alternative forced- chocice detection procedures

The standard difference model of two-alternative forced-choice (2AFC) tasks implies that performance should be the same when the target is presented in the first or the second interval. Empirical data often show “interval bias” in that percentage correct differs significantly when the signal is presented in the first or the second interval. We present an extension of the standard difference model that accounts for interval bias by incorporating an indifference zone around the null value of the decision variable. Analytical predictions are derived which reveal how interval bias may occur when data generated by the guessing model are analyzed as prescribed by the standard difference model. Parameter estimation methods and goodness-of-fit testing approaches for the guessing model are also developed and presented. A simulation study is included whose results show that the parameters of the guessing model can be estimated accurately. Finally, the guessing model is tested empirically in a 2AFC detection procedure in which guesses were explicitly recorded. The results support the guessing model and indicate that interval bias is not observed when guesses are separated out.

Fueling growth when oil peaks: directed technological change and the limits to efficiency

While fossil energy dependency has declined and energy supply has grown in the postwar world economy, future resource scarcity could cast its shadow on world economic growth soon if energy markets are forward looking. We develop an endogenous growth model that reconciles the current aggregate trends in energy use and productivity growth with the intertemporal dynamics of forward looking resource markets. Combining scarcity-rent driven energy supply (in the spirit of Hotelling) with profit-driven Directed Technical Change (in the spirit of Romer/Acemoglu), we generate transitional dynamics that can be qualitatively calibrated to current trends. The long-run properties of the model are studied to examine whether current trends are sustainable. We highlight the role of extraction costs in mining.