Appraisal of Rail Projects.

This paper reviews the particular characteristics of rail investment projects, taking as a starting point four examples ranging from decisions on individual routes to national rail investment programmes. The motivation for rail investment, and the interdependence of projects are examined, before turning to the identification of base case and options and the measurement of costs and benefits. It is argued that the main problems in rail investment appraisal are not technical ones relating to measuring costs and benefits but are contextual ones relating to the interdependence between rail projects and with decisions in other sectors of the economy. For this reason it is essential that rail projects be appraised with an appropriate planning framework

The economic implications of a multiple species approach to bioeconomic modelling : a thesis presented in partial fulfilment of the requirements for the degree of Master of Applied Economics at Massey University, Palmerston North, New Zealand

Human activity frequently leads to the endangerment or extinction of other species. While ecologists study the biological facets of species loss, economics, as the science of understanding people's behaviour, has been charged with investigating the incentives underlying the actions people take that lead to this loss. One approach economists have taken to gain this understanding is to develop models of endangered species that include both economic and biological components, known as bioeconomic models. While ecologists frequently note the importance of modelling entire ecosystems rather than single species, most bioeconomic models in the current literature focus only on a single species. This thesis addresses the economic significance of this assumption through the development of a series of multiple species models and demonstrates, using African Wildlife as an example, the importance of interrelationships and economic values to the survival of endangered species. From these models one can infer the conditions under which a single species model may be appropriate, at least in general terms. If species are independent, and either the opportunity cost of capital or the value of habitat is very low relative to the value of the species in question, then a single species model may yield results similar to that of a multiple species model. In contrast, if species are independent and these additional conditions are not met, a single species model may significantly underestimate both optimal stock levels and land allocation. However, species do not live independently; they interact with species with which they share habitat and, when species interact, the potential for misapplication of the single species framework is even greater. When species compete, the single species framework consistently produces higher stock levels than the multiple species framework, the greater the level of competition the greater the difference. In a predator-prey relationship, the relative values of predator and prey are critical to determining the outcome of the multiple species model. It is demonstrated that the inclusion of at least all economically valuable species in an ecosystem is important when constructing bioeconomic models. Using single species models where multiple species are economically significant could lead to misleading results and ultimately to incorrect policy decisions

Emerging Consciousness as a Result of Complex-Dynamical Interaction Process

A quite general interaction process within a multi-component system is analysed by the extended effective potential method, liberated from usual limitations of perturbation theory or integrable model. The obtained causally complete solution of the many-body problem reveals the phenomenon of dynamic multivaluedness, or redundance, of emerging, incompatible system realisations and dynamic entanglement of system components within each realisation. The ensuing concept of dynamic complexity (and related intrinsic chaoticity) is absolutely universal and can be applied to the problem of consciousness that emerges now as a high enough, properly specified level of unreduced complexity of a suitable interaction process. This complexity level can be identified with the appearance of bound, permanently localised states in the multivalued brain dynamics from strongly chaotic states of unconscious intelligence, by analogy with classical behaviour emergence from quantum states at much lower levels of world dynamics. We show that the main properties of this dynamically emerging consciousness (and intelligence, at the preceding complexity level) correspond to empirically derived properties of natural versions and obtain causally substantiated conclusions about their artificial realisation, including the fundamentally justified paradigm of genuine machine consciousness. This rigorously defined machine consciousness is different from both natural consciousness and any mechanistic, dynamically single-valued imitation of the latter. We use then the same, truly universal concept of complexity to derive equally rigorous conclusions about mental and social implications of the machine consciousness paradigm, demonstrating its indispensable role in the next stage of civilisation development

Building resilience into Sitka spruce (Picea sitchensis [Bong.] Carr.) forests in Scotland in response to the threat of climate change

Peer reviewedPublisher PD

Wages, productivity, and work intensity in the Great Depression

We show that U.S. manufacturing wages during the Great Depression were importantlydetermined by forces on firms' intensive margins. Short-run changes in work intensity and the longer-term goal of restoring full potential productivity combined to influence real wage growth. By contrast, the external effects of unemployment and replacement rates had much less impact. Empirical work is undertaken against the background of an efficient bargaining model that embraces employment, hours of work and work intensity

Processing second-order stochastic dominance models using cutting-plane representations

This is the post-print version of the Article. The official published version can be accessed from the links below. Copyright @ 2011 Springer-VerlagSecond-order stochastic dominance (SSD) is widely recognised as an important decision criterion in portfolio selection. Unfortunately, stochastic dominance models are known to be very demanding from a computational point of view. In this paper we consider two classes of models which use SSD as a choice criterion. The first, proposed by Dentcheva and Ruszczyński (J Bank Finance 30:433–451, 2006), uses a SSD constraint, which can be expressed as integrated chance constraints (ICCs). The second, proposed by Roman et al. (Math Program, Ser B 108:541–569, 2006) uses SSD through a multi-objective formulation with CVaR objectives. Cutting plane representations and algorithms were proposed by Klein Haneveld and Van der Vlerk (Comput Manage Sci 3:245–269, 2006) for ICCs, and by Künzi-Bay and Mayer (Comput Manage Sci 3:3–27, 2006) for CVaR minimization. These concepts are taken into consideration to propose representations and solution methods for the above class of SSD based models. We describe a cutting plane based solution algorithm and outline implementation details. A computational study is presented, which demonstrates the effectiveness and the scale-up properties of the solution algorithm, as applied to the SSD model of Roman et al. (Math Program, Ser B 108:541–569, 2006).This study was funded by OTKA, Hungarian National Fund for Scientific Research, project 47340; by Mobile Innovation Centre, Budapest University of Technology, project 2.2; Optirisk Systems, Uxbridge, UK and by BRIEF (Brunel University Research Innovation and Enterprise Fund)