Applications of Negotiation Theory to Water Issues

The purpose of the paper is to review the applications of non-cooperative bargaining theory to water related issues – which fall in the category of formal models of negotiation. The ultimate aim is that to, on the one hand, identify the conditions under which agreements are likely to emerge, and their characteristics; and, on the other hand, to support policy makers in devising the “rules of the game” that could help obtain a desired result. Despite the fact that allocation of natural resources, especially of trans-boundary nature, has all the characteristics of a negotiation problem, there are not many applications of formal negotiation theory to the issue. Therefore, this paper first discusses the non-cooperative bargaining models applied to water allocation problems found in the literature. Particular attention will be given to those directly modelling the process of negotiation, although some attempts at finding strategies to maintain the efficient allocation solution will also be illustrated. In addition, this paper will focus on Negotiation Support Systems (NSS), developed to support the process of negotiation. This field of research is still relatively new, however, and NSS have not yet found much use in real life negotiation. The paper will conclude by highlighting the key remaining gaps in the literature.Negotiation theory, Water, Agreeements, Stochasticity, Stakeholders

A methodological framework for quantifying impacts of truck traffic on regional network with implications to transport policy

Increased global trade has promoted the importance of shipping industry and the introduction of mega-ships has created an opportunity to be more cost-effective. Because of this, the expected change in freight transportation influences the operating regimes and schedules at the port terminals. Trucks being the predominant mode of transportation used to carry the freight transport, there is a growing concern about the impact of trucks in the region. The problems are further expected to grow as the improvements to resolve them are hindered by funding shortfalls. Public agencies are therefore involved in developing comprehensive state freight plans that outline immediate and long-range plans for freight-related transportation improvements. However, for states to develop and implement investment policies that can adequately address challenges, there is a need for a policy framework that can evaluate the impact of freight. The lack of the framework makes it difficult for state/metropolitan planning organizations to implement investment strategies in the best possible way. The proposed framework in the dissertation tries to fill the gap by developing a methodological framework, which can help agencies to evaluate multiple policies and their impact on local communities. Additionally, the framework can ascertain the magnitude of impacts that the infrastructure or policy in conjunction with the change in truck traffic might have on a regional level. The developed framework thus can help decision makers to prioritize policies that will benefit both public and freight transportation needs. Three demand models are used in the framework, which is built on the principle of behavioral route choice and mode-choice assignment problem. The outputs from the demand models are further used to quantify the impact in terms of cost-benefit analysis. The dissertation includes a real-world case study demonstrating how the framework can be used to evaluate alternative policies and its impact on a regional level. To this end, the developed framework in the dissertation addresses the research questions to present stakeholder\u27s complex implications that policy can have on the region. It also answers the question of how much the change in truck demand affects the region regarding monetary costs such as safety, congestion, environment, and pavement damage. The research further provides an insight of the change in travel behavior as a result of policy decision and its effect on communities

Energy demand models for policy formulation : a comparative study of energy demand models

This paper critically reviews existing energy demand forecasting methodologies highlighting the methodological diversities and developments over the past four decades in order to investigate whether the existing energy demand models are appropriate for capturing the specific features of developing countries. The study finds that two types of approaches, econometric and end-use accounting, are used in the existing energy demand models. Although energy demand models have greatly evolved since the early 1970s, key issues such as the poor-rich and urban-rural divides, traditional energy resources, and differentiation between commercial and non-commercial energy commodities are often poorly reflected in these models. While the end-use energy accounting models with detailed sector representations produce more realistic projections compared with the econometric models, they still suffer from huge data deficiencies especially in developing countries. Development and maintenance of more detailed energy databases, further development of models to better reflect developing country context, and institutionalizing the modeling capacity in developing countries are the key requirements for energy demand modeling to deliver richer and more reliable input to policy formulation in developing countries.Energy Production and Transportation,Energy Demand,Environment and Energy Efficiency,Energy and Environment,Economic Theory&Research

Network Modeling for Walking Infrastructure: Developing Pedestrian Traffic Assignment Methodologies for Large-Scale Footpath Networks

This dissertation focuses on exploring network-wide pedestrian bidirectional dynamics, developing frameworks for both static and dynamic pedestrian network traffic assignment models, and estimating an empirical pedestrian route choice model using revealed preference (RP) data from Sydney, Australia. To capture the bidirectional impact (e.g. lane formation and congestion due to flows from counter direction), four different types of pedestrian volume delay functions (pVDFs) are proposed and calibrated against controlled experimental data. The proposed pVDF is an essential component to develop a user equilibrium traffic assignment problem (UE-pTAP) framework. The proposed UE-pTAP framework has been applied to both a small-scale toy network and a largescale real-world network to demonstrate the impact of the bidirectionality of walking streams on the network assignment solution. To capture the pedestrian travel behavior in a dynamic context, this dissertation also proposes a pedestrian dynamic network traffic assignment (DTA) model based on the link transmission model (LTM). The formulated dynamic UE (DUE) adopts a bidirectional fundamental diagram that takes into account properties of bidirectional pedestrian streams such as self-organization. The applicability and validity of the model are demonstrated in a small grid network, a long corridor, and a large-scale real-world network. To better understand the pedestrian route choice decision-making in an urban environment, four types of logit-based route choice models, C-logit (CL), path size logit (PSL), and error component (EC), are also estimated using revealed preference (RP) data passively collected from smartphones in Sydney, Australia. The cross-validation results show that the four route choice models perform equally well. However, the PSL has a slight edge over other models because it has the least variability and requires much less computational resources than the EC models. Overall, the quantitative values for various attributes e.g., distance, the number of turns, maximum gradient, the number of points of interest (POI) along a path, and green view index (GVI), are comparable to previous findings in the literature. Overall, this dissertation offers valuable and new insights into the study of pedestrian bidirectional dynamics and the development of pedestrian network models in both static and dynamic contexts. The development of stochastic route choice models using smartphone data also provides an in-depth knowledge of decision-making behavior influenced by various built environment factors beyond travel distance. Results of this research help with the strategic planning of walking infrastructure in cities as well as estimating foot traffic across footpath networks for operational purposes

Game Theory Relaunched

The game is on. Do you know how to play? Game theory sets out to explore what can be said about making decisions which go beyond accepting the rules of a game. Since 1942, a well elaborated mathematical apparatus has been developed to do so; but there is more. During the last three decades game theoretic reasoning has popped up in many other fields as well - from engineering to biology and psychology. New simulation tools and network analysis have made game theory omnipresent these days. This book collects recent research papers in game theory, which come from diverse scientific communities all across the world; they combine many different fields like economics, politics, history, engineering, mathematics, physics, and psychology. All of them have as a common denominator some method of game theory. Enjoy

Exploring nonlinear regression methods, with application to association studies

The field of nonlinear regression is a long way from reaching a consensus. Once a method decides to explore nonlinear combinations of predictors, a number of questions are raised, such as what nonlinear combinations to permit and how best to search the resulting model space. Genetic Association Studies comprise an area that stands to gain greatly from the development of more sophisticated regression methods. While these studies’ ability to interrogate the genome has advanced rapidly over recent years, it is thought that a lack of suitable regression tools prevents them from achieving their full potential. I have tried to investigate the area of regression in a methodical manner. In Chapter 1, I explain the regression problem and outline existing methods. I observe that both linear and nonlinear methods can be categorised according to the restrictions enforced by their underlying model assumptions and speculate that a method with as few restrictions as possible might prove more powerful. In order to design such a method, I begin by assuming each predictor is tertiary (takes no more than three distinct values). In Chapters 2 and 3, I propose the method Sparse Partitioning. Its name derives from the way it searches for high scoring partitions of the predictor set, where each partition defines groups of predictors that jointly contribute towards the response. A sparsity assumption supposes most predictors belong in the “null group” indicating they have no effect on the outcome. In Chapter 4, I compare the performance of Sparse Partitioning to existing methods using simulated and real data. The results highlight how greatly a method’s power depends on the validity of its model assumptions. For this reason, Sparse Partitioning appears to offer a robust alternative to current methods, as its lack of restrictions allows it to maintain power in scenarios where other methods will fail. Sparse Partitioning relies on Markov chain Monte Carlo estimation, which limits the size of problem on which it can be used. Therefore, in Chapter 5, I propose a deterministic version of the method which, although less powerful, is not affected by convergence issues. In Chapter 6, I describe Bayesian Projection Pursuit, which adds spline fitting into the method to cope with non-tertiary predictors

Reconsidering the calculation and role of environmental footprints

Following the recent Copenhagen Climate Change conference, there has been discussion of the methods and underlying principles that inform climate change targets. Climate change targets following the Kyoto Protocol are broadly based on a production accounting principle (PAP). This approach focuses on emissions produced within given geographical boundaries. An alternative approach is a consumption accounting principle (CAP), where the focus is on emissions produced globally to meet consumption demand within the national (or regional) economy1. Increasingly popular environmental footprint measures, including ecological and carbon footprints, attempt to measure environmental impacts based on CAP methods. The perception that human consumption decisions lie at the heart of the climate change problem is the impetus driving pressure on policymakers for a more widespread use of CAP measures. At a global level of course, emissions accounted for under the production and consumption accounting principles would be equal. It is international trade that leads to differences in emissions under the two principles. This paper, the second in this special issue of the Fraser Commentary, examines how input-output accounting techniques may be applied to examine pollution generation under both of these accounting principles, focussing on waste and carbon generation in the Welsh economy as a case study. However, we take a different focus, arguing that the ‘domestic technology assumption’, taken as something of a mid-point in moving between production and consumption accounting in the first paper, may actually constitute a more useful focus for regional policymakers than full footprint analyses

The electricity generation mix in Scotland : the long and windy road?

This article reports on research funded by the Engineering and Physical Sciences Research Council (EPSRC) at the University of Strathclyde