The Optimal Design of Fallible Organizations: Invariance of Optimal Decision Criterion and Uniqueness of Hierarchy and Polyarchy Structures

We present a general framework to study the project selection problem in an organization of fallible decision-makers. We show that when the organizational size and the majority rule for project acceptance are optimized simultaneously, the optimal quality of decision-making, as determined by the decision criterion, is invariant, and depends only on the expertise of decision-makers. This result clarifies that the circumstances under which the decision-making quality varies with the organizational structure are situations where the organizational size or majority rule is restricted from reaching the optimal level. Moreover, in contrast to earlier findings in the literature that the hierarchy and the polyarchy are suboptimal structures, we show that when the size, structure and decision criterion are simultaneously optimized, the hierarchy and the polyarchy are in fact the only possible optimal organizational structures when decision-making costs are present.organizational decision-making, structure, quality, hierarchy, polyarchy

Fluid mechanics of waste water disposal in the ocean

Outfall pipes into the ocean are analogous to chimneys in the atmosphere: they are each intended for returning contaminated fluids to the environment in a way that promotes adequate transport and dispersion of the waste fluids. A waste-water treatment plant and an adjoining outfall constitute a system for environmental control; it is practically never feasible to provide such complete treatment that an outfall is not necessary, nor is it common to depend entirely on an outfall with no treatment. Although outfalls and chimneys are functionally similar, there are important differences in their relationships to the coastal waters and atmosphere respectively. Urban and industrial areas, generating waste water, are located along the shallow edge of the ocean, with often tens or even hundreds of kilometers of continental shelf between the shoreline and the deep ocean. The bottom slope on the shelf is typically less than one percent. Thus outfalls extending several kilometers offshore discharge into a body of water of large lateral extent compared to the depth, and are still remote from the main body of ocean water. In contrast, most atmospheric contaminants are introduced at the base of the atmosphere and circulate throughout the whole atmosphere much more readily. Vertical convection mixes the troposphere rapidly in most places and the wind systems circulate the air around the globe in a matter of weeks. Outfalls and chimneys are useful in reducing pollutant concentrations only locally. Far away from the sources, it makes little difference how the pollutants are discharged. The decay times of the pollutants are important in the choice of effective discharge strategies. For example, the problems of very persistent contaminants such as DDT cannot be alleviated by dispersion from an outfall; such pollutants must be intercepted at the source and prevented from entering the environment. On the other hand, degradable organic wastes, as in domestic sewage, may be effectively disposed of through a good ocean outfall. Since the decay time is only a few days, potential problems are only local, and not regional or global

Occupational health for an ageing workforce: Do we need a geriatric perspective?

10.1186/1745-6673-1-8Journal of Occupational Medicine and Toxicology11

Stress relief as the driving force for self-assembled Bi nanolines

Stress resulting from mismatch between a substrate and an adsorbed material has often been thought to be the driving force for the self-assembly of nanoscale structures. Bi nanolines self-assemble on Si(001), and are remarkable for their straightness and length -- they are often more than 400 nm long, and a kink in a nanoline has never been observed. Through electronic structure calculations, we have found an energetically favourable structure for these nanolines that agrees with our scanning tunneling microscopy and photoemission experiments; the structure has an extremely unusual subsurface structure, comprising a double core of 7-membered rings of silicon. Our proposed structure explains all the observed features of the nanolines, and shows that surface stress resulting from the mismatch between the Bi and the Si substrate are responsible for their self-assembly. This has wider implications for the controlled growth of nanostructures on semiconductor surfaces.Comment: 4 pages, 4 figures, submitted to Phys. Rev. Let

Testing for Serial Correlation, Spatial Autocorrelation and Random Effects

This paper considers a spatial panel data regression model with serial correlation on each spatial unit over time as well as spatial dependence between the spatial units at each point in time. In addition, the model allows for heterogeneity across the spatial units using random effects. The paper then derives several Lagrange Multiplier tests for this panel data regression model including a joint test for serial correlation, spatial autocorrelation and random effects. These tests draw upon two strands of earlier work. The first is the LM tests for the spatial error correlation model discussed in Anselin and Bera (1998) and in the panel data context by Baltagi, Song and Koh (2003). The second is the LM tests for the error component panel data model with serial correlation derived by Baltagi and Li (1995). Hence the joint LM test derived in this paper encompasses those derived in both strands of earlier works. In fact, in the context of our general model, the earlier LM tests become marginal LM tests that ignore either serial correlation over time or spatial error correlation. The paper then derives conditional LM and LR tests that do not ignore these correlations and contrast them with their marginal LM and LR counterparts. The small sample performance of these tests is investigated using Monte Carlo experiments. As expected, ignoring any correlation when it is significant can lead to misleading inference.panel data, spatial correlation

Testing Panel Data Regression Models with Spatial Error Correlation

This paper derives several Lagrange Multiplier tests for the panel data regression model with spatial error correlation. These tests draw upon two strands of earlier work. The first is the LM tests for the spatial error correlation model discussed in Anselin (1988, 1999) and Anselin, Bera, Florax and Yoon (1996), and the second is the LM tests for the error component panel data model discussed in Breusch and Pagan (1980) and Baltagi, Chang and Li (1992). The idea is to allow for both spatial error correlation as well as random region effects in the panel data regression model and to test for their joint significance. Additionally, this paper derives conditional LMtests, which test for random regional effects given the presence of spatial error correlation. Also, spatial error correlation given the presence of random regional effects. These conditional LM tests are an alternative to the one directional LM tests that test for random regional effects ignoring the presence of spatial error correlation or the one directional LM tests for spatial error correlation ignoring the presence of random regional effects. We argue that these joint and conditional LM tests guard against possible misspecification. Extensive Monte Carlo experiments are conducted to study the performance of these LM tests as well as the corresponding Likelihood Ratio tests.Panel data; Spatial error correlation, Lagrange Multiplier tests, Likelihood Ratio tests