126 research outputs found
Collective agency:From philosophical and logical perspectives
People inhabit a vast and intricate social network nowadays. In addition to our own decisions and actions, we confront those of various groups every day. Collective decisions and actions are more complex and bewildering compared to those made by individuals. As members of a collective, we contribute to its decisions, but our contributions may not always align with the outcome. We may also find ourselves excluded from certain groups and passively subjected to their influences without being aware of the source. We are used to being in overlapping groups and may switch identities, supporting or opposing the claims of particular groups. But rarely do we pause to think: What do we talk about when we talk about groups and their decisions?At the heart of this dissertation is the question of collective agency, i.e., in what sense can we treat a group as a rational agent capable of its action. There are two perspectives we take: a philosophical and logical one. The philosophical perspective mainly discusses the ontological and epistemological issues related to collective agency, sorts out the relevant philosophical history, and argues that the combination of a relational view of collective agency and a dispositional view of collective intentionality provides a rational and realistic account. The logical perspective is associated with formal theories of groups, it disregards the psychological content involved in the philosophical perspective, establishes a logical system that is sufficiently formal and objective, and axiomatizes the nature of a collective
Collaborative Decision-Making and the k-Strong Price of Anarchy in Common Interest Games
The control of large-scale, multi-agent systems often entails distributing
decision-making across the system components. However, with advances in
communication and computation technologies, we can consider new collaborative
decision-making paradigms that exist somewhere between centralized and
distributed control. In this work, we seek to understand the benefits and costs
of increased collaborative communication in multi-agent systems. We
specifically study this in the context of common interest games in which groups
of up to k agents can coordinate their actions in maximizing the common
objective function. The equilibria that emerge in these systems are the
k-strong Nash equilibria of the common interest game; studying the properties
of these states can provide relevant insights into the efficacy of inter-agent
collaboration. Our contributions come threefold: 1) provide bounds on how well
k-strong Nash equilibria approximate the optimal system welfare, formalized by
the k-strong price of anarchy, 2) study the run-time and transient performance
of collaborative agent-based dynamics, and 3) consider the task of redesigning
objectives for groups of agents which improve system performance. We study
these three facets generally as well as in the context of resource allocation
problems, in which we provide tractable linear programs that give tight bounds
on the k-strong price of anarchy.Comment: arXiv admin note: text overlap with arXiv:2308.0804
Combining Optimization and Machine Learning for the Formation of Collectives
This thesis considers the problem of forming collectives of agents for real-world applications aligned with Sustainable Development Goals (e.g., shared mobility and cooperative learning). Such problems require fast approaches that can produce solutions of high quality for hundreds of agents. With this goal in mind, existing solutions for the formation of collectives focus on enhancing the optimization approach by exploiting the characteristics of a domain. However, the resulting approaches rely on specific domain knowledge and are not transferable to other collective formation problems. Therefore, approaches that can be applied to various problems need to be studied in order to obtain general approaches that do not require prior knowledge of the domain. Along these lines, this thesis proposes a general approach for the formation of collectives based on a novel combination of machine learning and an \emph{Integer Linear Program}. More precisely, a machine learning component is trained to generate a set of promising collectives that are likely to be part of a solution. Then, such collectives and their corresponding utility values are introduced into an \emph{Integer Linear Program} which finds a solution to the collective formation problem. In that way, the machine learning component learns the structure shared by ``good'' collectives in a particular domain, making the whole approach valid for various applications. In addition, the empirical analysis conducted on two real-world domains (i.e., ridesharing and team formation) shows that the proposed approach provides solutions of comparable quality to state-of-the-art approaches specific to each domain. Finally, this thesis also shows that the proposed approach can be extended to problems that combine the formation of collectives with other optimization objectives. Thus, this thesis proposes an extension of the collective formation approach for assigning pickup and delivery locations to robots in a warehouse environment. The experimental evaluation shows that, although it is possible to use the collective formation approach for that purpose, several improvements are required to compete with state-of-the-art approaches. Overall, this thesis aims to demonstrate that machine learning can be successfully intertwined with classical optimization approaches for the formation of collectives by learning the structure of a domain, reducing the need for ad-hoc algorithms devised for a specific application
Efficiently computing the Shapley value of connectivity games in low-treewidth graphs
The Shapley value is the solution concept in cooperative game theory that is most used in both theoretical and practical settings. Unfortunately, in general, computing the Shapley value is computationally intractable. This paper focuses on computing the Shapley value of (weighted) connectivity games. For these connectivity games, which are defined on an underlying (weighted) graph, computing the Shapley value is #P-hard, and thus (likely) intractable even for graphs with a moderate number of vertices. We present an algorithm that can efficiently compute the Shapley value if the underlying graph has bounded treewidth. Next, we apply our algorithm to several real-world (covert) networks. We show that our algorithm can quickly compute exact Shapley values for these networks, whereas in prior work these values could only be approximated using a heuristic method. Finally, it is demonstrated that our algorithm can also efficiently compute the Shapley value time for several larger (artificial) benchmark graphs from the PACE 2018 challenge
Operational Research: methods and applications
This is the final version. Available on open access from Taylor & Francis via the DOI in this recordThroughout its history, Operational Research has evolved to include methods, models and algorithms that have been applied to a wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first summarises the up-to-date knowledge and provides an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion and used as a point of reference by a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes
Local Energy Markets - Simulative Evaluation and Field Test Application of Energy Markets on Distribution Grid Level
Widespread introduction of Distributed Energy Resources (DERs) such as volatile renewable generation, electric vehicles, heat-pumps and battery storages causes a paradigm shift of the power system. Traditional power systems with few large-scale power plants are expanded or replaced by millions of small- to medium-size DERs. Local Energy Markets (LEMs) are a promising approach to facilitate the optimal operation and dispatch of DERs and enhance grid-integration on regional grid levels. In this Thesis, a novel linear-optimization-based market model for LEMs is developed. The market matching problem aims to maximize the social welfare of participants while considering technical and financial aspects of participants’ assets and the distribution grid. A simulative framework is set-up to evaluate the model with regards to its capabilities to foster the optimal use of flexibilities, to provide sufficient financial incentives for participants and to improve grid-integration. Yearly simulations of LEMs and a benchmark case are carried out for three different grid types (rural, semiurban, urban) and scenario years ranging from 2020 until 2035 in 5 year steps. The simulation results reveal that self-consumption and self-sufficiency of the local energy system can be increased by 4 ... 23 and 1 ... 9 percentage points depending on the grid type when compared to a business as usual benchmark case. An analysis of possible designs for regulated electricity price components in LEMs shows that a reduction of feed-in and load peaks of 30 ... 64 % can be achieved when considering power fees in the market matching problem. The simulative evaluation also shows that the market model is able to generate temporal, spatial, and asset-specific prices signals. Depending on the grid type and its load-generation ratio, participants with generation assets have higher benefits in urban, load-dominated grids whereas consumers have higher benefits in generation-dominated rural and semiurban grids. Load forecast uncertainty is identified as one of the major challenges in LEMs. Compared to simulations with perfect foresight, benefits of market participants are substantially decreased taking into account typical electric load forecast errors on the level of individual households. The application of the market model in a six months field-test in Southern Germany demonstrates the real world applicability of the developed approach. The field-test confirms findings from the simulative evaluation regarding the implication of forecast errors and generated price signals. It additionally shows that market interfaces to the Distribution System Operator (DSO) might further increase grid-integration capabilities of LEMs. By taking into account active power constraints of the DSO, 1499 events of critical grid load could be avoided
Learning representations for graph-structured socio-technical systems
The recent widespread use of social media platforms and web services has led to a vast amount of behavioral data that can be used to model socio-technical systems. A significant part of this data can be represented as graphs or networks, which have become the prevalent mathematical framework for studying the structure and the dynamics of complex interacting systems. However, analyzing and understanding these data presents new challenges due to their increasing complexity and diversity. For instance, the characterization of real-world networks includes the need of accounting for their temporal dimension, together with incorporating higher-order interactions beyond the traditional pairwise formalism.
The ongoing growth of AI has led to the integration of traditional graph mining techniques with representation learning and low-dimensional embeddings of networks to address current challenges. These methods capture the underlying similarities and geometry of graph-shaped data, generating latent representations that enable the resolution of various tasks, such as link prediction, node classification, and graph clustering. As these techniques gain popularity, there is even a growing concern about their responsible use. In particular, there has been an increased
emphasis on addressing the limitations of interpretability in graph representation learning.
This thesis contributes to the advancement of knowledge in the field of graph representation learning and has potential applications in a wide range of complex systems domains. We initially focus on forecasting problems related to face-to-face contact networks with time-varying graph embeddings. Then, we study hyperedge prediction and reconstruction with simplicial complex embeddings. Finally, we analyze the problem of interpreting latent dimensions in node embeddings for graphs. The proposed models are extensively evaluated in multiple experimental settings and the results demonstrate their effectiveness and reliability, achieving state-of-the-art performances and providing valuable insights into the properties of the learned representations
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