# From DDMs to DNNs: Using process data and models of decision-making to improve human-AI interactions

arXiv: https://arxiv.org/abs/2308.15225 · HTML: https://mlsanity.com/paper/2308.15225/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 81 references that are in the map.
- First posted on arXiv 29 Aug 2023.

## Builds on (most cited first, 9 of 9)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Large Language Models Fail on Trivial Alterations to Theory-of-Mind Tasks](https://mlsanity.com/paper/2302.08399/) (2023)
- [Machine Theory of Mind](https://mlsanity.com/paper/1802.07740/) (2018)
- [Multiagent Cooperation and Competition with Deep Reinforcement Learning](https://mlsanity.com/paper/1511.08779/) (2015)
- [Mind the gap: Challenges of deep learning approaches to Theory of Mind](https://mlsanity.com/paper/2203.16540/) (2022)
- [Personalized Education in the AI Era: What to Expect Next?](https://mlsanity.com/paper/2101.10074/) (2021)
- [AI for Explaining Decisions in Multi-Agent Environments](https://mlsanity.com/paper/1910.04404/) (2019)

## Abstract (arXiv)

Over the past decades, cognitive neuroscientists and behavioral economists have recognized the value of describing the process of decision making in detail and modeling the emergence of decisions over time. For example, the time it takes to decide can reveal more about an agent's true hidden preferences than only the decision itself. Similarly, data that track the ongoing decision process such as eye movements or neural recordings contain critical information that can be exploited, even if no decision is made. Here, we argue that artificial intelligence (AI) research would benefit from a stronger focus on insights about how decisions emerge over time and incorporate related process data to improve AI predictions in general and human-AI interactions in particular. First, we introduce a highly established computational framework that assumes decisions to emerge from the noisy accumulation of evidence, and we present related empirical work in psychology, neuroscience, and economics. Next, we discuss to what extent current approaches in multi-agent AI do or do not incorporate process data and models of decision making. Finally, we outline how a more principled inclusion of the evidence-accumulation framework into the training and use of AI can help to improve human-AI interactions in the future.