Completed Projects - Human-Machine Interaction

This study examines the strategic role of cybersecurity based on survey data from 1,083 managers

across Europe, the UK, and the United States. The findings indicate growing recognition of

cybersecurity as a source of competitive advantage, although firms continue to face barriers such as

limited resources, talent shortages, and cultural resistance. Larger and high-tech firms tend to adopt

more proactive strategies, while SMEs and low-tech sectors display greater variability. Key

managerial tensions emerge in balancing security with innovation and agility. Notable country-level

differences are observed across Europe, the UK, and the United States. Across all contexts,

leadership and employee engagement appear central to closing the gap between strategic intent and

operational practice.

Final report

This research project explores the influence of feedback complexity on learning outcomes in interactive settings, comparing the effectiveness of feedback from artificial intelligence (AI) algorithms with that from human counterparts. With the increasing use of AI algorithms for training in complex scenarios, the debate surrounding their efficacy necessitates a nuanced examination. The central hypothesis posits that, in certain contexts, feedback from an efficient algorithm may be too intricate for effective learning, potentially resulting in misinterpretation.

The study aims to identify essential characteristics that an algorithm should possess to provide optimal feedback. It challenges the assumption that feedback from an efficient algorithm is always informative, suggesting that feedback simplicity should be tailored to the strategic sophistication of the agent.

In practical terms, the project focuses on repeated strategic interactions involving two or more agents, where timely feedback influences learning outcomes. Experimental data will be collected through online experiments. The research builds on previous findings indicating that the type of feedback significantly impacts the learning process.

The project's outcomes are expected to provide valuable insights into the design of effective AI algorithms for educational and interactive applications. Understanding how feedback complexity influences learning is crucial in optimizing algorithmic interventions in human interactions.