Transformation or bureaucratisation?: the changing role of community representation in local strategic partnerships in England

The debate about the need to build social capital and to engage local communities in public policy has become a central issue in many advanced liberal societies and developing countries. In many countries new forms of governance have emerged out of a growing realisation that representative democracy by itself is no longer sufficient. One of the most significant public policy trends in the UK has been the involvement of community organisations and their members in the delivery of national policy, mediated through local systems of governance and management. One such policy area is urban regeneration. Central government now requires local authorities in England to set up Local Strategic Partnerships (LSPs) to bring together stakeholders who can prepare Community Strategies and deliver social and economic programmes which target areas of deprivation. This paper reviews the key institutional processes which must be addressed, such as representation, accountability and transformation.

Local strategic partnerships in England: the continuing search for collaborative advantage, leadership and strategy in urban governance

Local Strategic Partnerships are being established in England to provide an inclusive, collaborative and strategic focus to regeneration strategies at the local level. They are also required to rationalise the proliferation of local and micro-partnerships set up by a succession of funding initiatives over the last 25 years. This article explores their remit, resources and membership and discusses how this initiative relates to theoretical work on urban governance, community engagement and leadership. It concludes by debating whether urban policy in England is now entering a new and more advanced phase based on inter-organisational networks with a strategic purpose. But questions remain about whether the institutional capacity is sufficient to deliver strong local leadership, accountability and community engagement.

What Attitude Changes Are Needed to Cause SMEs to Take a Strategic Approach to Information Security?

Spending on security in an SME usually has to compete with demands for hardware, infrastructure, and strategic applications. In this paper, the authors seek to explore the reasons why smaller SMEs in particular have consistently failed to see securing information as strategic year-on-year spending, and just regard as part of an overall tight IT budget. The authors scrutinise the typical SMEs reasoning for choosing to see non-spending on security as an acceptable strategic risk. They look particularly at possible reasons why SMEs tend not to take much notice of "scare stories" in the media based on research showing they are increasingly at risk, whilst larger businesses are taking greater precautions and become more difficult to penetrate. The results and their analysis provide useful pointers towards broader business environment changes that would cause SMEs to be more risk-averse and ethical in their approach to securing their own and their clients’ information.

Strategic tensions within the smartphones industry: the case of BlackBerry

This paper reviews some aspects of corporate strategy in a well-known smart phone provider. Two approaches to strategy are analysed: one concerning the industry and the other related to the organization. A general introduction on the smart phones industry is given followed by specific background on BlackBerry. Two perspectives are explored: the first talks about the paradox of compliance and choice within the industry and the second discusses the paradox of control and chaos in BlackBerry. The paper concludes with a brief overview on the company performance from 2006 to 2012 leading to some recommendations.

MASCEM: electricity markets simulation with strategic agents

Electricity markets are complex environments, involving numerous entities trying to obtain the best advantages and profits while limited by power-network characteristics and constraints.1 The restructuring and consequent deregulation of electricity markets introduced a new economic dimension to the power industry. Some observers have criticized the restructuring process, however, because it has failed to improve market efficiency and has complicated the assurance of reliability and fairness of operations. To study and understand this type of market, we developed the Multiagent Simulator of Competitive Electricity Markets (MASCEM) platform based on multiagent simulation. The MASCEM multiagent model includes players with strategies for bid definition, acting in forward, day-ahead, and balancing markets and considering both simple and complex bids. Our goal with MASCEM was to simulate as many market models and player types as possible. This approach makes MASCEM both a short- and mediumterm simulation as well as a tool to support long-term decisions, such as those taken by regulators. This article proposes a new methodology integrated in MASCEM for bid definition in electricity markets. This methodology uses reinforcement learning algorithms to let players perceive changes in the environment, thus helping them react to the dynamic environment and adapt their bids accordingly.

Stochastic Framework for Strategic Decision-making of Load-serving Entities for Day-ahead Market

The deregulation of electricity markets has diversified the range of financial transaction modes between independent system operator (ISO), generation companies (GENCO) and load-serving entities (LSE) as the main interacting players of a day-ahead market (DAM). LSEs sell electricity to end-users and retail customers. The LSE that owns distributed generation (DG) or energy storage units can supply part of its serving loads when the nodal price of electricity rises. This opportunity stimulates them to have storage or generation facilities at the buses with higher locational marginal prices (LMP). The short-term advantage of this model is reducing the risk of financial losses for LSEs in DAMs and its long-term benefit for the LSEs and the whole system is market power mitigation by virtually increasing the price elasticity of demand. This model also enables the LSEs to manage the financial risks with a stochastic programming framework.

Strategic Bidding in Electricity Markets: An agent-based simulator with game theory for scenario analysis

Electricity markets are complex environments, involving a large number of different entities, with specific characteristics and objectives, making their decisions and interacting in a dynamic scene. Game-theory has been widely used to support decisions in competitive environments; therefore its application in electricity markets can prove to be a high potential tool. This paper proposes a new scenario analysis algorithm, which includes the application of game-theory, to evaluate and preview different scenarios and provide players with the ability to strategically react in order to exhibit the behavior that better fits their objectives. This model includes forecasts of competitor players’ actions, to build models of their behavior, in order to define the most probable expected scenarios. Once the scenarios are defined, game theory is applied to support the choice of the action to be performed. Our use of game theory is intended for supporting one specific agent and not for achieving the equilibrium in the market. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. The scenario analysis algorithm has been tested within MASCEM and our experimental findings with a case study based on real data from the Iberian Electricity Market are presented and discussed.

Metalearner based on Dynamic Neural Network for Strategic Bidding in electricity Markets

The restructuring of electricity markets, conducted to increase the competition in this sector, and decrease the electricity prices, brought with it an enormous increase in the complexity of the considered mechanisms. The electricity market became a complex and unpredictable environment, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. Software tools became, therefore, essential to provide simulation and decision support capabilities, in order to potentiate the involved players’ actions. This paper presents the development of a metalearner, applied to the decision support of electricity markets’ negotiation entities. The proposed metalearner executes a dynamic artificial neural network to create its own output, taking advantage on several learning algorithms implemented in ALBidS, an adaptive learning system that provides decision support to electricity markets’ players. The proposed metalearner considers different weights for each strategy, depending on its individual quality of performance. The results of the proposed method are studied and analyzed in scenarios based on real electricity markets’ data, using MASCEM - a multi-agent electricity market simulator that simulates market players’ operation in the market.

Strategic trade policy and signaling costs with differentiated goods

We consider a trade policy model, where the costs of the home firm are private information but can be signaled through the output levels of the firm to a foreign competitor and a home policymaker. We compute the separating equilibrium and the Bayesian Nash equilibrium, and we compare the subsidies, firms’ expected profits and home government’s welfare in both equilibria, for different values of the own price effect parameter.

Strategic marketing plan for Sant'Anna

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

Oi merger with Portugal Telecom: A strategic partnership

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics