Publication Library
A Scalable Gradient-Based Optimization Framework for Sparse Minimum-Variance Portfolio Selection
Description: Portfolio optimization involves selecting asset weights to minimize a risk-reward objective, such as the portfolio variance in the classical minimum-variance framework. Sparse portfolio selection extends this by imposing a cardinality constraint: only k assets from a universe of p may be included. The standard approach models this problem as a mixed-integer quadratic program and relies on commercial solvers to find the optimal solution. However, the computational costs of such methods increase exponentially with k and p, making them too slow for problems of even moderate size. We propose a fast and scalable gradient-based approach that transforms the combinatorial sparse selection problem into a constrained continuous optimization task via Boolean relaxation, while preserving equivalence with the original problem on the set of binary points. Our algorithm employs a tunable parameter that transmutes the auxiliary objective from a convex to a concave function. This allows a stable convex starting point, followed by a controlled path toward a sparse binary solution as the tuning parameter increases and the objective moves toward concavity. In practice, our method matches commercial solvers in asset selection for most instances and, in rare instances, the solution differs by a few assets whilst showing a negligible error in portfolio variance.
Created At: 17 May 2025
Updated At: 17 May 2025
Lost in Models - Structuring Managerial Decision Support in Process Mining with Multi-criteria Decision Making
Description: Process mining is increasingly adopted in modern organizations, producing numerous process models that, while valuable, can lead to model overload and decision-making complexity. This paper explores a multi-criteria decision-making (MCDM) approach to evaluate and prioritize process models by incorporating both quantitative metrics (e.g., fitness, precision) and qualitative factors (e.g., cultural fit). An illustrative logistics example demonstrates how MCDM, specifically the Analytic Hierarchy Process (AHP), facilitates trade-off analysis and promotes alignment with managerial objectives. Initial insights suggest that the MCDM approach enhances context-sensitive decision-making, as selected models address both operational metrics and broader managerial needs. While this study is an early-stage exploration, it provides an initial foundation for deeper exploration of MCDM-driven strategies to enhance the role of process mining in complex organizational settings.
Created At: 17 May 2025
Updated At: 17 May 2025
Design and Evaluation of Generative Agent-based Platform for Human-Assistant Interaction Research - A Tale of 10 User Studies
Description: https://dash-gidea.github.io/ Designing and evaluating personalized and proactive assistant agents remains challenging due to the time, cost, and ethical concerns associated with human-in-the-loop experimentation. Existing Human-Computer Interaction (HCI) methods often require extensive physical setup and human participation, which introduces privacy concerns and limits scalability. Simulated environments offer a partial solution but are typically constrained by rule-based scenarios and still depend heavily on human input to guide interactions and interpret results. Recent advances in large language models (LLMs) have introduced the possibility of generative agents that can simulate realistic human behavior, reasoning, and social dynamics. However, their effectiveness in modeling human-assistant interactions remains largely unexplored. To address this gap, we present a generative agent-based simulation platform designed to simulate human-assistant interactions. We identify ten prior studies on assistant agents that span different aspects of interaction design and replicate these studies using our simulation platform. Our results show that fully simulated experiments using generative agents can approximate key aspects of human-assistant interactions. Based on these simulations, we are able to replicate the core conclusions of the original studies. Our work provides a scalable and cost-effective approach for studying assistant agent design without requiring live human subjects. We will open source both the platform and collected results from the experiments on our website: https://dash-gidea.github.io/
Created At: 17 May 2025
Updated At: 17 May 2025
From Interaction to Collaboration - How Hybrid Intelligence Enhances Chatbot Feedback
Description: Generative AI (GenAI) chatbots are becoming increasingly integrated into virtual assistant technologies, yet their success hinges on the ability to gather meaningful user feedback to improve interaction quality, system outcomes, and overall user acceptance. Successful chatbot interactions can enable organizations to build long-term relationships with their customers and users, supporting customer loyalty and furthering the organization's goals. This study explores the impact of two distinct narratives and feedback collection mechanisms on user engagement and feedback behavior: a standard AI-focused interaction versus a hybrid intelligence (HI) framed interaction. Initial findings indicate that while small-scale survey measures allowed for no significant differences in user willingness to leave feedback, use the system, or trust the system, participants exposed to the HI narrative statistically significantly provided more detailed feedback. These initial findings offer insights into designing effective feedback systems for GenAI virtual assistants, balancing user effort with system improvement potential.
Created At: 17 May 2025
Updated At: 17 May 2025
Formalising Human-in-the-Loop - Computational Reductions, Failure Modes, and Legal-Moral Responsibility
Description: The legal compliance and safety of different Human-in-the-loop (HITL) setups for AI can vary greatly. This manuscript aims to identify new ways of choosing between such setups, and shows that there is an unavoidable trade-off between the attribution of legal responsibility and the technical explainability of AI. We begin by using the notion of oracle machines from computability theory to formalise different HITL setups, distinguishing between trivial human monitoring, single endpoint human action, and highly involved interaction between the human(s) and the AI. These correspond to total functions, many-one reductions, and Turing reductions respectively. A taxonomy categorising HITL failure modes is then presented, highlighting the limitations on what any HITL setup can actually achieve. Our approach then identifies oversights from UK and EU legal frameworks, which focus on certain HITL setups which may not always achieve the desired ethical, legal, and sociotechnical outcomes. We suggest areas where the law should recognise the effectiveness of different HITL setups and assign responsibility in these contexts, avoiding unnecessary and unproductive human "scapegoating". Overall, we show how HITL setups involve many technical design decisions, and can be prone to failures which are often out of the humans' control. This opens up a new analytic perspective on the challenges arising in the creation of HITL setups, helping inform AI developers and lawmakers on designing HITL to better achieve their desired outcomes.
Created At: 17 May 2025
Updated At: 17 May 2025