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Found 11 papers, 4 papers with code
A theory of appropriateness with applications to generative artificial intelligence
Humans navigate a multi-scale mosaic of interlocking notions of what is appropriate for different situations.
Using deep reinforcement learning to promote sustainable human behaviour on a common pool resource problem
A canonical social dilemma arises when finite resources are allocated to a group of people, who can choose to either reciprocate with interest, or keep the proceeds for themselves.
Perception Test: A Diagnostic Benchmark for Multimodal Video Models
We propose a novel multimodal video benchmark - the Perception Test - to evaluate the perception and reasoning skills of pre-trained multimodal models (e. g. Flamingo, SeViLA, or GPT-4).
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Perception Test: A Diagnostic Benchmark for Multimodal Models
We propose a novel multimodal benchmark – the Perception Test – that aims to extensively evaluate perception and reasoning skills of multimodal models.
The Good Shepherd: An Oracle Agent for Mechanism Design
Our results show that our mechanisms are able to shepherd the participants strategies towards favorable outcomes, indicating a path for modern institutions to effectively and automatically influence the strategies and behaviors of their constituents.
HCMD-zero: Learning Value Aligned Mechanisms from Data
Our analysis shows HCMD-zero consistently makes the mechanism policy more and more likely to be preferred by human participants over the course of training, and that it results in a mechanism with an interpretable and intuitive policy.
Human-centered mechanism design with Democratic AI
Building artificial intelligence (AI) that aligns with human values is an unsolved problem.
Role of Human-AI Interaction in Selective Prediction
Using real-world conservation data and a selective prediction system that improves expected accuracy over that of the human or AI system working individually, we show that this messaging has a significant impact on the accuracy of human judgements.
Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot
Existing evaluation suites for multi-agent reinforcement learning (MARL) do not assess generalization to novel situations as their primary objective (unlike supervised-learning benchmarks).
Multi-agent Reinforcement Learning
reinforcement-learning
+2
A multi-agent reinforcement learning model of reputation and cooperation in human groups
Collective action demands that individuals efficiently coordinate how much, where, and when to cooperate.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Inequity aversion improves cooperation in intertemporal social dilemmas
Groups of humans are often able to find ways to cooperate with one another in complex, temporally extended social dilemmas.
