A Dynamic Game Model of Collective Choice in Multi-Agent Systems

Inspired by successful biological collective decision mechanisms such as honey bees searching for a new colony or the collective navigation of fish schools, we consider a mean field games (MFG)-like scenario where a large number of agents have to make a choice among a set of different potential target destinations. Each individual both influences and is influenced by the group's decision, as well as the mean trajectory of all the agents. The model can be interpreted as a stylized version of opinion crystallization in an election for example. The agents' biases are dictated first by their initial spatial position and, in a subsequent generalization of the model, by a combination of initial position and a priori individual preference. The agents have linear dynamics and are coupled through a modified form of quadratic cost. Fixed point based finite population equilibrium conditions are identified and associated existence conditions are established. In general multiple equilibria may exist and the agents need to know all initial conditions to compute them precisely. However, as the number of agents increases sufficiently, we show that 1) the computed fixed point equilibria qualify as epsilon Nash equilibria, 2) agents no longer require all initial conditions to compute the equilibria but rather can do so based on a representative probability distribution of these conditions now viewed as random variables. Numerical results are reported

Recent Research in Cooperative Control of Multivehicle Systems

This paper presents a survey of recent research in cooperative control of multivehicle systems, using a common mathematical framework to allow different methods to be described in a unified way. The survey has three primary parts: an overview of current applications of cooperative control, a summary of some of the key technical approaches that have been explored, and a description of some possible future directions for research. Specific technical areas that are discussed include formation control, cooperative tasking, spatiotemporal planning, and consensus

Latecomers’ science-based catch-up in transition: the case of the Korean pharmaceutical industry

This thesis investigates the 25-year transitional process of the Korean pharmaceutical industry from its initial focus on the imitative production of generic drugs to the development of new drugs. The catch-up dynamics of latecomer countries in science-intensive industries, such as the pharmaceutical industry, is an overlooked research topic in existing literature on innovation studies. This thesis provides an in-depth analysis of Korea’s science-intensive catch-up and applies an ‘exploration and exploitation’ framework to a latecomer setting and in a novel institutional and market context of the transitional phase. This thesis argues that the rate of change in the transition from imitating drugs to developing new drugs depends on the institutional and organisational mechanisms that enable a new form of technological learning, termed ‘exploratory learning’. This form of learning is often unfamiliar to firms in latecomer countries, whereas it is necessary for producing innovative drugs. That is, latecomers’ institutional and organisational promotion of exploratory learning is related to a ‘pattern change’ in the previously established institutional and organisational routines associated with imitative learning. The findings show that the rate of industrial transition in this sector was constrained by the problematic operation of S&T policies promoting key characteristics of exploratory learning, such as high-risk long-term learning as well as dense interactions between a diverse number of innovation actors. The findings also illuminate some latecomer firms’ initial difficulties in managing the new mode of technological learning, and in strategically applying that mode of learning to overcome the barriers to moving through the transitional phase towards producing competitive innovation. The thesis also suggests that the nature of drugs as integral products, deeply grounded in science, makes it difficult to effectively promote institutional and organisational transformations in favour of exploratory learning

A dynamic game model of collective choice in multi-agent systems

Inspired by successful biological collective decision mechanisms such as honey bees searching for a new colony or the collective navigation of fish schools, we consider a scenario where a large number of agents engaged in a dynamic game have to make a choice among a finite set of different potential target destinations. Each individual both influences and is influenced by the group's decision, as represented by the mean trajectory of all agents. Agents are assumed linear and coupled through a modified form of quadratic cost, whereby the terminal cost captures the discrete choice component of the problem. Following the mean field games methodology, we identify sufficient conditions under which allocations of destination choices over agents lead to self replication of the overall mean trajectory under the best response by the agents. Importantly, we establish that when the number of agents increases sufficiently, (i) the best response strategies to the self replicating mean trajectories qualify as epsilon-Nash equilibria of the population game; (ii) these epsilon-Nash strategies can be computed solely based on the knowledge of the joint probability distribution of the initial conditions, dynamics parameters and destination preferences, now viewed as random variables. Our results are illustrated through numerical simulations

Smart Grid Enabling Low Carbon Future Power Systems Towards Prosumers Era

In efforts to meet the targets of carbon emissions reduction in power systems, policy makers formulate measures for facilitating the integration of renewable energy sources and demand side carbon mitigation. Smart grid provides an opportunity for bidirectional communication among policy makers, generators and consumers. With the help of smart meters, increasing number of consumers is able to produce, store, and consume energy, giving them the new role of prosumers. This thesis aims to address how smart grid enables prosumers to be appropriately integrated into energy markets for decarbonising power systems. This thesis firstly proposes a Stackelberg game-theoretic model for dynamic negotiation of policy measures and determining optimal power profiles of generators and consumers in day-ahead market. Simulation results show that the proposed model is capable of saving electricity bills, reducing carbon emissions, and increasing the penetration of renewable energy sources. Secondly, a data-driven prosumer-centric energy scheduling tool is developed by using learning approaches to reduce computational complexity from model-based optimisation. This scheduling tool exploits convolutional neural networks to extract prosumption patterns, and uses scenarios to analyse possible variations of uncertainties caused by the intermittency of renewable energy sources and flexible demand. Case studies confirm that the proposed scheduling tool can accurately predict optimal scheduling decisions under various system scales and uncertain scenarios. Thirdly, a blockchain-based peer-to-peer trading framework is designed to trade energy and carbon allowance. The bidding/selling prices of individual prosumers can directly incentivise the reshaping of prosumption behaviours. Case studies demonstrate the execution of smart contract on the Ethereum blockchain and testify that the proposed trading framework outperforms the centralised trading and aggregator-based trading in terms of regional energy balance and reducing carbon emissions caused by long-distance transmissions

Affect Intensity as a Moderator of the Relationship Between Emotional Intelligence and Transformational Leadership

Researchers have reported mixed findings on the relationship between emotional intelligence (EI) and transformational leadership, leading many to suspect the presence of moderating variables. This study was conducted to address the problem by analyzing the moderating effect that affect intensity may have upon this relationship. Based on a theoretical framework consisting of ability-based EI and the full-range theory of leadership, it was hypothesized that EI would be positively correlated with transformational leadership. In addition, based upon the arousal regulation theory of affect, it was hypothesized that affect intensity would be a statistically significant moderator of that relationship. A convenience sample of leaders (N = 142) working in the hospitality industry completed the Mayer Salovey Caruso Emotional Intelligence Test, the Multifactor Leadership Questionnaire form 5X, and the Affect Intensity Measure. Pearson\u27s Product-Moment correlational analysis revealed that, consistent with expectations, total EI scores and the managing emotions branch scores of EI were positively correlated with transformational leadership; however, the branch scores for perceiving, using, and understanding emotion were not. Contrary to expectations, affect intensity was not a statistically significant moderator in this sample. Findings from this research support the proposition that EI may best predict transformational leadership within service-based environments where employees face intense emotional labor demands. A thorough understanding of the ways in which EI predicts leader behavior will not only help organizations improve leader selection and development, but also help to improve vital social outcomes, such as employee job satisfaction, engagement, and well-being