Optimal Multiphase Investment Strategies for Influencing Opinions in a Social Network

We study the problem of optimally investing in nodes of a social network in a competitive setting, where two camps aim to maximize adoption of their opinions by the population. In particular, we consider the possibility of campaigning in multiple phases, where the final opinion of a node in a phase acts as its initial biased opinion for the following phase. Using an extension of the popular DeGroot-Friedkin model, we formulate the utility functions of the camps, and show that they involve what can be interpreted as multiphase Katz centrality. Focusing on two phases, we analytically derive Nash equilibrium investment strategies, and the extent of loss that a camp would incur if it acted myopically. Our simulation study affirms that nodes attributing higher weightage to initial biases necessitate higher investment in the first phase, so as to influence these biases for the terminal phase. We then study the setting in which a camp's influence on a node depends on its initial bias. For single camp, we present a polynomial time algorithm for determining an optimal way to split the budget between the two phases. For competing camps, we show the existence of Nash equilibria under reasonable assumptions, and that they can be computed in polynomial time

Optimal multiphase investment strategies for influencing opinions in a social network

International audienceWe study the problem of two competing camps aiming to maximize the adoption of their respective opinions, by optimally investing in nodes of a social network in multiple phases. The final opinion of a node in a phase acts as its biased opinion in the following phase. Using an extension of Friedkin-Johnsen model, we formulate the camps' utility functions, which we show to involve what can be interpreted as multiphase Katz centrality. We hence present optimal investment strategies of the camps, and the loss incurred if myopic strategy is employed. Simulations affirm that nodes attributing higher weightage to bias necessitate higher investment in initial phase. The extended version of this paper analyzes a setting where a camp's influence on a node depends on the node's bias; we show existence and polynomial time computability of Nash equilibrium

A Two Phase Investment Game for Competitive Opinion Dynamics in Social Networks

We propose a setting for two-phase opinion dynamics in social networks, where a node's final opinion in the first phase acts as its initial biased opinion in the second phase. In this setting, we study the problem of two camps aiming to maximize adoption of their respective opinions, by strategically investing on nodes in the two phases. A node's initial opinion in the second phase naturally plays a key role in determining the final opinion of that node, and hence also of other nodes in the network due to its influence on them. More importantly, this bias also determines the effectiveness of a camp's investment on that node in the second phase. To formalize this two-phase investment setting, we propose an extension of Friedkin-Johnsen model, and hence formulate the utility functions of the camps. There is a tradeoff while splitting the budget between the two phases. A lower investment in the first phase results in worse initial biases for the second phase, while a higher investment spares a lower available budget for the second phase. We first analyze the non-competitive case where only one camp invests, for which we present a polynomial time algorithm for determining an optimal way to split the camp's budget between the two phases. We then analyze the case of competing camps, where we show the existence of Nash equilibrium and that it can be computed in polynomial time under reasonable assumptions. We conclude our study with simulations on real-world network datasets, in order to quantify the effects of the initial biases and the weightage attributed by nodes to their initial biases, as well as that of a camp deviating from its equilibrium strategy. Our main conclusion is that, if nodes attribute high weightage to their initial biases, it is advantageous to have a high investment in the first phase, so as to effectively influence the biases to be harnessed in the second phase

Manipulating opinion dynamics in social networks in two phases

International audienceWe propose a setting for two-phase opinion dynamics in social networks, where the final opinion of a node in the first phase acts as its initial biased opinion in the second phase. In this setting, we study the problem of two camps aiming to maximize adoption of their respective opinions by strategically investing on nodes, where the effectiveness of a camp's investment on a node depends on the node's initial bias. We propose an extension of Friedkin-Johnsen model for our setting, and hence formulate the utility functions of the camps. For the non-competitive case where only one camp invests, we present a polynomial time algorithm for determining an optimal way to split the camp's budget between the two phases. For the case of competing camps, we show the existence of Nash equilibria under reasonable assumptions, and that they can be computed in polynomial time. Our main conclusion is that, if nodes attribute high weightage to their initial biases, it is advantageous to have a high investment in the first phase, so as to exploit the manipulated biases in the second phase

A two phase investment game for competitive opinion dynamics in social networks

International audienceWe propose a setting for two-phase opinion dynamics in social networks, where a node's final opinion in the first phase acts as its initial biased opinion in the second phase. In this setting, we study the problem of two camps aiming to maximize adoption of their respective opinions, by strategically investing on nodes in the two phases. A node's initial opinion in the second phase naturally plays a key role in determining the final opinion of that node, and hence also of other nodes in the network due to its influence on them. However, more importantly, this bias also determines the effectiveness of a camp's investment on that node in the second phase. In order to formalize this two-phase investment setting, we propose an extension of Friedkin-Johnsen model, and hence formulate the utility functions of the camps. We arrive at a decision parameter which can be interpreted as two-phase Katz centrality. There is a natural tradeoff while splitting the available budget between the two phases. A lower investment in the first phase results in worse initial biases in the network for the second phase. On the other hand, a higher investment in the first phase spares a lower available budget for the second phase, resulting in an inability to fully harness the influenced biases. We first analyze the non-competitive case where only one camp invests, for which we present a polynomial time algorithm for determining an optimal way to split the camp's budget between the two phases. We then analyze the case of competing camps, where we show the existence of Nash equilibrium and that it can be computed in polynomial time under reasonable assumptions. We conclude our study with simulations on real-world network datasets, in order to quantify the effects of the initial biases and the weightage attributed by nodes to their initial biases, as well as that of a camp deviating from its equilibrium strategy. Our main conclusion is that, if nodes attribute high weightage to their initial biases, it is advantageous to have a high investment in the first phase, so as to effectively influence the biases to be harnessed in the second phase

An integrated model for asset reliability, risk and production efficiency management in subsea oil and gas operations

PhD ThesisThe global demand for energy has been predicted to rise by 56% between 2010 and 2040 due to industrialization and population growth. This continuous rise in energy demand has consequently prompted oil and gas firms to shift activities from onshore oil fields to tougher terrains such as shallow, deep, ultra-deep and arctic fields. Operations in these domains often require deployment of unconventional subsea assets and technology. Subsea assets when installed offshore are super-bombarded by marine elements and human factors which increase the risk of failure. Whilst many risk standards, asset integrity and reliability analysis models have been suggested by many previous researchers, there is a gap on the capability of predictive reliability models to simultaneously address the impact of corrosion inducing elements such as temperature, pressure, pH corrosion on material wear-out and failure. There is also a gap in the methodology for evaluation of capital expenditure, human factor risk elements and use of historical data to evaluate risk. This thesis aims to contribute original knowledge to help improve production assurance by developing an integrated model which addresses pump-pipe capital expenditure, asset risk and reliability in subsea systems. The key contributions of this research is the development of a practical model which links four sub-models on reliability analysis, asset capital cost, event risk severity analysis and subsea risk management implementation. Firstly, an accelerated reliability analysis model was developed by incorporating a corrosion covariate stress on Weibull model of OREDA data. This was applied on a subsea compression system to predict failure times. A second methodology was developed by enhancing Hubbert oil production forecast model, and using nodal analysis for asset capital cost analysis of a pump-pipe system and optimal selection of best option based on physical parameters such as pipeline diameter, power needs, pressure drop and velocity of fluid. Thirdly, a risk evaluation method based on the mathematical determinant of historical event magnitude, frequency and influencing factors was developed for estimating the severity of risk in a system. Finally, a survey is conducted on subsea engineers and the results along with the previous models were developed into an integrated assurance model for ensuring asset reliability and risk management in subsea operations. A guide is provided for subsea asset management with due consideration to both technical and operational perspectives. The operational requirements of a subsea system can be measured, analysed and improved using the mix of mathematical, computational, stochastic and logical frameworks recommended in this work

Subsea fluid sampling to maximise production asset in offshore field development

The acquisition of representative subsea fluid sampling from offshore field development asset is crucial for the correct evaluation of oil reserves and for the design of subsea production facilities. Due to rising operational expenditures, operators and manufacturers have been working hard to provide systems to enable cost effective subsea fluid sampling solutions. To achieve this, any system has to collect sufficient sample volumes to ensure statistically valid characterisation of the sampled fluids. In executing the research project, various subsea sampling methods used in the offshore industry were examined and ranked using multi criteria decision making; a solution using a remote operated vehicle was selected as the preferred method, to compliment the subsea multiphase flowmeter capability, used to provide well diagnostics to measure individual phases – oil, gas, and water. A mechanistic (compositional fluid tracking) model is employed, using the fluid properties that are equivalent to the production flow stream being measured, to predict reliable reservoir fluid characteristics on the subsea production system. This is applicable even under conditions where significant variations in the reservoir fluid composition occur in transient production operations. The model also adds value in the decision to employ subsea processing in managing water breakthrough as the field matures. This can be achieved through efficient processing of the fluid with separation and boosting delivered to the topside facilities or for water re-injection to the reservoir. The combination of multiphase flowmeter, remote operated vehicle deployed fluid sampling and the mechanistic model provides a balanced approach to reservoir performance monitoring. Therefore, regular and systematic field tailored application of subsea fluid sampling should provide detailed understanding on formation fluid, a basis for accurate prediction of reservoir fluid characteristic, to maximize well production in offshore field development

The Role of Business in Driving and Shaping Renewable Energy Policies in China

This report investigates the role of business actors in shaping China’s renewable energy policy process and governance. It finds that with the tremendous growth of renewable industry over the past two decades, a new government–business coalition is taking shape in China. Business actors such as the manufacturers of wind turbine or solar PV and investors in renewable energy projects play a key role in this coalition. The coalition has been exerting notable influence successfully at both the policymaking and policy implementation stages to advance its strategic preference for the continuous expansion of renewable industries, mainly by accommodating conflicts or scepticism from actors outside the coalition and integrating other major social actors’ interests both at national and local level, namely the grid companies and local state officers. Consequently, its influence has generated profound impact on the speed, scale and quality of China’s renewable energy development. The study reveals a rather different political economy of China’s policymaking process in the climate governance domain, compared to a traditional description of ‘top-down’ or ‘command-and-control’ mode of governance where Chinese central government is often believed to be the only crucial actor to steer climate-related policy. Rather, it is argued in this report that central government’s autonomy in the policy process is largely constrained by the rising business interest groups who possess considerable material and institutional power to change policy orientation.UK Department for International Developmen

Outlook for an integrated sustainable development of pig production in the Red River Delta

224pThe situation in Thai Binh province offers a significant number of assets to be capitalized upon in terms of recycling of effluents, but the stakeholders concerned have an urgent need for strong measures to help them live from their livestock farming activity while protecting an endangered environment. This chapter highlights the additional agronomical and economic references as well as decision making tools that still need to be acquired. The technical and organizational solutions that will need to be tested or promoted more widely are presented. The key issues for the future and the establishment of a hierarchy or priorities in terms of support for agricultural development, necessary local know-how and avenues of research to study in more depth the complex problems encountered in North Vietnam are discussed. Emphasis is given on the pig production in the Red River Delt