Technical and Methodological Guidelines
- Our focus is on multi-agent, mixed-motive settings. We do not expect to fund work on fully cooperative settings. We also tend to favour proposals that consider (or could straightforwardly be applied to) cooperation problems with more than two agents.
- We favour hypothesis-driven work over exploratory work. There can be exceptions to this rule, but for exploratory proposals it is especially important to justify why the approach was chosen and how the authors expect the results to be useful for other settings.
- For projects where agents are trained, it is important that the proposal is clear on how the agents are trained. Work in which agents are trained on joint welfare objectives is not likely to be funded as we are focused on techniques that are applicable to “self-interested” agents. We do not expect to fund any work where agents share rewards or that otherwise assumes we need to control how every agent is designed or trained to be able to achieve cooperative outcomes.
- For most proposals we receive, an important aspect is to justify why the results should be expected to apply to complex settings with advanced agents. Especially for foundational or conceptual work it often makes sense to work with simplified models, but it is then crucial to explain how well the results are expected to generalise to the kind of settings and advanced agents that will be increasingly societally important.
- For this reason, we tend not to fund work that depends on hand-crafted features or overly complex setups, for example, as results from such work are often less likely to scale or generalise.
- We have observed that many proposals using MARL have been rejected based on concerns related to an expected lack of generality or scalability of the results. We therefore want to encourage applicants who propose work in MARL to especially consider this point.
- To be clear, it is not required for proposals to directly make use of LLMs or related models.
- Using tabula rasa (as opposed to pre-trained) models can be relevant in some instances, but this should then be justified. Examples of situations where this could be relevant are when there is little or no human data to learn from, and/or when doing so is not possible (e.g. for privacy reasons, computational reasons, etc.), and/or when we would expect much better performance from not doing so. There could also be cases when using some kind of pre-training wouldn't change the dynamics under investigation very much.
