59 citations · 59 across the 7 of their papers we have counts for
10 papers
Prompt framing governs LLM default following in collective-action
Eladio Montero-Porras, Axel Abels, Tom Lenaerts
Large language models are increasingly deployed as agents that make or recommend decisions on behalf of users, often operating through interfaces that pre-fill suggested values or…
Conditioning LLMs on Social Value Orientation improves behavioural alignment in a sequential social dilemma
Marco Saponara, Axel Abels, Ann Nowé +1
Large language Models (LLMs) are increasingly used to simulate human decision-making, yet their outputs often under-represent human behavioural heterogeneity. We investigate whethe…
Improving Cross-Cultural Survey Simulation with Calibrated Value Personas
Axel Abels, Elias Fernandez Domingos, Apurva Shah +1
Large language models (LLMs) are increasingly used to simulate human opinions and survey responses, but their ability to reproduce population responses across cultures remains limi…
Optimizing Resources for On-the-Fly Label Estimation with Multiple Unknown Medical Experts
Tim Bary, Tiffanie Godelaine, Axel Abels +1
Accurate ground truth estimation in medical screening programs often relies on coalitions of experts and peer second opinions. Algorithms that efficiently aggregate noisy annotatio…
Artificial Delegates Resolve Fairness Issues in Perpetual Voting with Partial Turnout
Apurva Shah, Axel Abels, Ann Nowé +1
Perpetual voting addresses fairness in sequential collective decision-making by evaluating representational equity over time. However, existing perpetual voting rules rely on full…
Wisdom from Diversity: Bias Mitigation Through Hybrid Human-LLM Crowds
Axel Abels, Tom Lenaerts
Despite their performance, large language models (LLMs) can inadvertently perpetuate biases found in the data they are trained on. By analyzing LLM responses to bias-eliciting head…
Mitigating Biases in Collective Decision-Making: Enhancing Performance in the Face of Fake News
Axel Abels, Elias Fernandez Domingos, Ann Nowé +1
Individual and social biases undermine the effectiveness of human advisers by inducing judgment errors which can disadvantage protected groups. In this paper, we study the influenc…
Expertise Trees Resolve Knowledge Limitations in Collective Decision-Making
Axel Abels, Tom Lenaerts, Vito Trianni +1
Experts advising decision-makers are likely to display expertise which varies as a function of the problem instance. In practice, this may lead to sub-optimal or discriminatory dec…
Dealing with Expert Bias in Collective Decision-Making
Axel Abels, Tom Lenaerts, Vito Trianni +1
Quite some real-world problems can be formulated as decision-making problems wherein one must repeatedly make an appropriate choice from a set of alternatives. Multiple expert judg…
Dynamic Weights in Multi-Objective Deep Reinforcement Learning
Axel Abels, Diederik M. Roijers, Tom Lenaerts +2
Many real-world decision problems are characterized by multiple conflicting objectives which must be balanced based on their relative importance. In the dynamic weights setting the…