Polynomially solvable special cases of the quadratic bottleneck assignment problem

Quadratic bottleneck assignment problems (QBAP) are obtained by replacing the addition of cost terms in the objective function of a quadratic (sum) assignment problem by taking their maximum. Since the QBAP is an NP\mathcal{NP}-hard problem, polynimially solvable special cases of the QBAP are of interest. In this paper we specify conditions on the cost matrices of QBAP leading to special cases which can be solved to optimality in polynomial time. In particular, the following three cases are discussed: (i) any permutation is optimal (constant QBAP), (ii) a certain specified permutation is optimal (constant permutation QBAP) and (iii) the solution can be found algorithmically by a polynomial algorithm. Moreover, the max-cone of bottleneck Monge matrices is investigated, its generating matrices are identified and it is used as a tool in proving polynomiality results.     

A multidimensional view on poverty in the European Union by partial order theory

Poverty can be seen as a multidimensional phenomenon described by a set of indicators, the poverty components. A one-dimensional measure of poverty serving as a ranking index can be obtained by combining the component indicators via aggregation techniques. Ranking indices are thought of as supporting political decisions. This paper proposes an alternative to aggregation based on simple concepts of partial order theory and illustrates the pros and cons of this approach taking as case study a multidimensional measure of poverty comprising three components – absolute poverty, relative poverty and income – computed for the European Union regions. The analysis enables one to highlight conflicts across the components with some regions detected as controversial, with, for example, low levels of relative poverty and high levels of monetary poverty. The partial order approach enables one to point to the regions with the most severe data conflicts and to the component indicators that cause these conflicts.     

Quo vadis public procurement of innovation?

Governments are actively looking for ways to use public procurement so that it would become more effective in facilitating innovation across public and private sectors. However, a shift towards public procurement of innovation (PPI) has proven to be difficult. Whereas the contemporary debate has mostly focused on how to reduce the barriers of PPI through re-conceptualizing the procurement process, there is a need to take into account also wider strategic factors through which governments create capacity to undertake PPI. By revisiting historic and contemporary policy initiatives, four strategies for the future can be envisioned: PPI as experimental innovation policy, from fiscal policy under austerity to PPI, mission-oriented PPI and shifts in administrative culture towards PPI. Each of the strategies demands different capacities from the entrepreneurial sector, as well as state, policy and administrative capacities from the public sector. These issues should be an inherent part of future policy-making and offer new avenues for PPI-related policy analysis and academic research.     

Optimal cutpoints for random observations

For the discretisation of a continuous random variable into different categories the choice of cutpoints is essential. A popular application is the contingent valuation method. In a parametric approach, the choice of cutpoints directly effects the quality of the estimates. Therefore, optimal cutpoints are desirable in order to estimate the parameters most accurately. We consider an arbitrary number of cutpoints and determine optimal cutpoints for the exponential and Gumbel distribution and prove that the c-optimal cutpoints for the location parameter of the logistic distribution have corresponding equal category probabilities. Furthermore, we show that in the limiting case for infinitely many cutpoints there is no loss of information.     

Drivers of biodiversity patterns in parks of a growing South American megacity

How urban habitats contribute to biodiversity conservation is a key challenge in a rapidly urbanising world. Urban parks can provide important habitats for native species, but previous studies are geographically biased; fast growing megacities, in particular in South America, are clearly understudied. To assess habitat functions and underlying drivers in parks of Santiago de Chile, we analysed the assemblages of wild growing plant species in two ubiquitous park habitat types (grasslands, wooded areas) in 15 parks (150 plots) along an urban-rural gradient. We first used linear contrasts to compare species richness, beta diversity and the proportion of introduced species. We then tested for the explanatory value of environmental variables operating at different spatial scales (plot, park, urban matrix). Unlike in most previous studies, biodiversity patterns were not related to the position of the parks on the urban-rural gradient. Introduced species, mostly from Europe, generally dominated both habitat types (>90 %). Socio-economic (population growth or density), but not spatial, variables were retained in most models. Maintenance intensity was most influential in predicting species assemblages, complemented by park age in wooded areas. A high proportion of European grassland species indicates a trend of homogenisation in park grassland at a cross-continental scale. We conclude that habitat functions of urban parks for native species that have been mainly demonstrated for Europe cannot be generalised to South American megacities. This highlights the need for innovative and locally appropriate conservation approaches (e.g., re-introduction of native species) to foster biodiversity functions in urban parks of South American megacities.