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.

Contraceptive Choices Pre and Post Pregnancy in Adolescence

STUDY OBJECTIVE: The main aim of this study is to evaluate the impact of adolescent pregnancy in the future contraceptive choices. A secondary aim is to verify whether these choices differ from those made after an abortion. DESIGN: Retrospective study. SETTING:Adolescent Unit of a tertiary care center. PARTICIPANTS:212 pregnant teenagers. INTERVENTIONS: Medical records review. MAIN OUTCOME MEASURES:Intended pregnancy rate and contraceptive methods used before and after pregnancy. For contraceptive choices after pregnancy we considered: Group 1 - teenagers who continued their pregnancy to delivery (n = 106) and Group 2 - the same number of adolescents who chose to terminate their pregnancy. RESULTS: The intended pregnancy rate was 14.2%. Prior to a pregnancy continued to delivery, the most widely used contraceptive method was the male condom (50.9%), followed by oral combined contraceptives (28.3%); 18.9% of adolescents were not using any contraceptive method. After pregnancy, contraceptive implant was chosen by 70.8% of subjects (P < .001) and the oral combined contraceptives remained the second most frequent option (17.9%, P = .058). Comparing these results with Group 2, we found that the outcome of the pregnancy was the main factor in the choices that were made. Thus, after a pregnancy continued to delivery, adolescents prefer the use of LARC [78.4% vs 40.5%, OR: 5,958 - 95% (2.914-12.181), P < .001)], especially contraceptive implants [70.8% vs 38.7%, OR: 4.371 - 95% (2.224-8.591), P < .001], to oral combined contraceptives [17.9% vs 57.5%, OR: 0.118 - 95% CI (0.054-0.258), P < .001]. CONCLUSION:Adolescent pregnancy and its outcome constitute a factor of change in future contraceptive choice.

Three essays in risky behaviors

A PhD Dissertation, presented as part of the requirements for the Degree of Doctor of Philosophy from the NOVA - School of Business and Economics

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