Inferring Best Strategies from the Aggregation of Information from Multiple Agents: The Cultural Approach

Although learning in MAS is described as a collective experience, most of the times its modeling draws solely or mostly on the results of the interaction between the agents. This abruptly contrasts with our everyday experience where learning relies, to a great extent, on a large stock of already codified knowledge rather than on the direct interaction among the agents. If in the course human history this reliance on already codified knowledge had a significant importance, especially since the discovery of writing, during the last decade the size and availability of this stock has increased notably because of the Internet. Even more, humanity has endowed itself with institutions and organizations devoted to fulfill the role of codifying, preserving and diffusing knowledge since its early days. Cultural Algorithms are one of the few cases where the modeling of this process, although in a limited way, has been attempted. However, even in this case, the modeling lacks some of the characteristics that have made it so successful in human populations, notably its frugality in learning only from a rather small subset of the population and a discussion of its dynamics in terms of hypothesis generation and falsification and the relationship between adaptation and discovery. A deep understanding of this process of collective learning, in all its aspects of generalization and re-adoption of this collective and distilled knowledge, together with its diffusion is a key element to understand how human communities function and how a mixed community of humans and electronic agents could effectively learn. And this is more important now than ever because this process has become not only global and available to large populations but also has largely increased its speed. This research aims to contribute to cover this gap, elucidating on the frugality of the mechanism while mapping it in a framework characterized by a variable level of complexity of knowledge. Also seeks to understand the macro dynamics resulting from the micro mechanisms and strategies chosen by the agents. Nevertheless, as any exercise based on modeling, it portrays a stylized description of reality that misses important points and significant aspects of the real behavior. In this case, while we will focus on individual learning and on the process of generalization and ulterior re-use of these generalizations, learning from other agents is notably absent. We believe however, that this choice contributes to make our model easier to understand and easier to expose the causality relationships emerging from our simulation exercises without sacrificing any significant result

Inferring Best Strategies from the Aggregation of Information from Multiple Agents: The Cultural Approach

Although learning in MAS is described as a collective experience, most of the times its modeling draws solely or mostly on the results of the interaction between the agents. This abruptly contrasts with our everyday experience where learning relies, to a great extent, on a large stock of already codified knowledge rather than on the direct interaction among the agents. If in the course human history this reliance on already codified knowledge had a significant importance, especially since the discovery of writing, during the last decade the size and availability of this stock has increased notably because of the Internet. Even more, humanity has endowed itself with institutions and organizations devoted to fulfill the role of codifying, preserving and diffusing knowledge since its early days. Cultural Algorithms are one of the few cases where the modeling of this process, although in a limited way, has been attempted. However, even in this case, the modeling lacks some of the characteristics that have made it so successful in human populations, notably its frugality in learning only from a rather small subset of the population and a discussion of its dynamics in terms of hypothesis generation and falsification and the relationship between adaptation and discovery. A deep understanding of this process of collective learning, in all its aspects of generalization and re-adoption of this collective and distilled knowledge, together with its diffusion is a key element to understand how human communities function and how a mixed community of humans and electronic agents could effectively learn. And this is more important now than ever because this process has become not only global and available to large populations but also has largely increased its speed. This research aims to contribute to cover this gap, elucidating on the frugality of the mechanism while mapping it in a framework characterized by a variable level of complexity of knowledge. Also seeks to understand the macro dynamics resulting from the micro mechanisms and strategies chosen by the agents. Nevertheless, as any exercise based on modeling, it portrays a stylized description of reality that misses important points and significant aspects of the real behavior. In this case, while we will focus on individual learning and on the process of generalization and ulterior re-use of these generalizations, learning from other agents is notably absent. We believe however, that this choice contributes to make our model easier to understand and easier to expose the causality relationships emerging from our simulation exercises without sacrificing any significant result

Building and investigating generators' bidding strategies in an electricity market

In a deregulated electricity market environment, Generation Companies (GENCOs) compete with each other in the market through spot energy trading, bilateral contracts and other financial instruments. For a GENCO, risk management is among the most important tasks. At the same time, how to maximise its profit in the electricity market is the primary objective of its operations and strategic planning. Therefore, to achieve the best risk-return trade-off, a GENCO needs to determine how to allocate its assets. This problem is also called portfolio optimization. This dissertation presents advanced techniques for generator strategic bidding, portfolio optimization, risk assessment, and a framework for system adequacy optimisation and control in an electricity market environment. Most of the generator bidding related problems can be regarded as complex optimisation problems. In this dissertation, detailed discussions of optimisation methods are given and a number of approaches are proposed based on heuristic global optimisation algorithms for optimisation purposes. The increased level of uncertainty in an electricity market can result in higher risk for market participants, especially GENCOs, and contribute significantly to the drivers for appropriate bidding and risk management tasks for GENCOs in the market. Accordingly, how to build an optimal bidding strategy considering market uncertainty is a fundamental task for GENCOs. A framework of optimal bidding strategy is developed out of this research. To further enhance the effectiveness of the optimal bidding framework; a Support Vector Machine (SVM) based method is developed to handle the incomplete information of other generators in the market, and therefore form a reliable basis for a particular GENCO to build an optimal bidding strategy. A portfolio optimisation model is proposed to maximise the return and minimise the risk of a GENCO by optimally allocating the GENCO's assets among different markets, namely spot market and financial market. A new market pnce forecasting framework is given In this dissertation as an indispensable part of the overall research topic. It further enhances the bidding and portfolio selection methods by providing more reliable market price information and therefore concludes a rather comprehensive package for GENCO risk management in a market environment. A detailed risk assessment method is presented to further the price modelling work and cover the associated risk management practices in an electricity market. In addition to the issues stemmed from the individual GENCO, issues from an electricity market should also be considered in order to draw a whole picture of a GENCO's risk management. In summary, the contributions of this thesis include: 1) a framework of GENCO strategic bidding considering market uncertainty and incomplete information from rivals; 2) a portfolio optimisation model achieving best risk-return trade-off; 3) a FIA based MCP forecasting method; and 4) a risk assessment method and portfolio evaluation framework quantifying market risk exposure; through out the research, real market data and structure from the Australian NEM are used to validate the methods. This research has led to a number of publications in book chapters, journals and refereed conference proceedings

Open, temporal, and cultural practices in new ways of strategizing

Organizations operate in a changing environment due to new market dynamics, technological and societal demands. This thesis follows a strategy-as-practice approach to investigate the micro processes and practices that organizations adopt over time to develop dynamic capabilities for dealing with the above challenges. In line with this approach, I conducted three studies: one at a large international automotive supplier (AutoParts – disguised name), and two at NASA’s Johnson Space Center in Houston. I relied on inductive reasoning and grounded theory to analyze the in-depth qualitative case study data that I collected between January 2016 and April 2019. The first study investigates the practices that AutoParts enacted in its open strategy process. My findings suggest a new typology of strategy tools based on their affordances and consequently the type of strategizing practices they favor or constrain - either deductive top-down strategizing or inductive bottom-up strategy-making. Further, I discovered the central role that the interplay of these two types of strategy tools play for gradually reaching strategic consensus and for developing a corresponding dynamic capability. The second study is a longitudinal account of NASA’s human space exploration activities and its related management of temporal tensions. I identified two types of temporal strategizing practices in the context of long-term strategic change. Change-related temporal practices are purposefully executed to facilitate change initiatives, while vision-related temporal practices either facilitate or inhibit change in an overt or covert way. I suggest a process model that can be associated with a dynamic capability for managing temporal tensions in long-term strategic change. The third study focuses on an incumbent firm’s ability to adapt to environmental changes by becoming more ambidextrous. I studied the innovative subculture of the Pirates within the large organization of NASA. My findings reveal that the interaction between the Pirates’ subcultural practices and the practices from the dominant organizational culture fostered ambidexterity. Through a process, which I term cultural oscillation; these practices produced direct and residual effects for the organization