A bargaining model of financial intermediation

Investment;Bargaining;Financial Institutions

The incidence of interest withholding taxes: Evidence from the LDC loan markets

Taxation;Interest Rate

How government policy affects the consumption of hard drugs: The case of opium in Java, 1873-1907

ecomomic history;government policy;drug addiction;consumption;hard drugs

Simulation of electricity markets using agent-based computational learning

The purpose of this research is to conduct an analysis of how agent-based computational learning may contribute to a better understanding of pricing policies and strategic management of plant portfolio in electricity markets. The contributions of this thesis are methodological and theoretical with applications in policy analysis for electricity markets. At a policy level, this thesis applies agent-based simulation to the analysis of the impact of market design on the players' strategies and on the industry's performance as a whole. This represents the first detailed study of the New Electricity Trading Arrangements (NETA) in the England and Wales (E&W) electricity market, giving insights into the implications of NETA before its introduction. Further, this thesis addresses the issue of dominant position abuse by individual players in electricity markets. The context is a real application to the E&W electricity market as part of a Competition Commission Inquiry. The research contributions are in the areas of both market power and market design policy issues. At a methodological level, this thesis presents two contributions: the Finite Automata Dynamic Game (FADG) and the Plant Trading Game. The FADG models learning and adaptation in N-player extensive form games of incomplete information, where co-evolutionary automata learn and adapt together. The plant trading game is a large coordination game, simulating how players optimally learn and adapt in order to trade electricity plants. At a theoretical level, this thesis presents three contributions. First, it develops a stylised model for conduct-evaluation in electricity markets, which is applied to the analysis of market power abuse and regulatory policy. Second, it studies plant trading within the context of a Cournot game. Third, it shows that, in an FADG, best response is a necessary but not a sufficient condition for rational behaviour

Macroeconomic stabilisation and intervention policy under an exchange rate band

Exchange Rate;Stabilization;Foreign Exchange Market

Inventory management of repairable service parts for personal computers:A case study

Recent years have seen an increase of interest in the field of service parts inventory - particularly in computer industry. The computer industry is a highly competitive industry; products have to be repaired as quickly as possible, since slow repair can lead to loss of future business to competitors with better service reputations. A good reputation is therefore closely linked to the availability of spare parts on the market. Given this fact and using a real-life case study, this paper first elaborates on the management and control of service-parts inventory and presents a brief overview of the contemporary literature on the subject. Next the paper presents the solution approach adopted and the results of study, which indicate that significant savings can be realized through good management of service-parts inventory.

Worker turnover at the firm level and crowding out of lower educated workers

unemployment;wages;labour turnover. education;business cycles

Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review

Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area