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Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Agency from a causal perspective, published by Tom Everitt on June 30, 2023 on The AI Alignment Forum.
Post 3 of Towards Causal Foundations of Safe AGI, preceded by Post 1: Introduction and Post 2: Causality.
By Matt MacDermott, James Fox, Rhys Ward, Jonathan Richens, and Tom Everitt representing the Causal Incentives Working Group. Thanks also to Ryan Carey, Toby Shevlane, and Aliya Ahmad.
The purpose of this post is twofold: to lay the foundation for subsequent posts by exploring what agency means from a causal perspective, and to sketch a research program for a deeper understanding of agency.
The Importance of Understanding Agency
Agency is a complex concept that has been studied from multiple perspectives, including social science, philosophy, and AI research. Broadly it refers to a system able to act autonomously. For the purposes of this blog post, we interpret agency as goal-directedness, i.e. acting as if trying to direct the world in some particular direction.
There are strong incentives to create more agentic AI systems. Such systems could potentially do many tasks humans are currently needed for, such as independently researching topics, or even run their own companies. However, making systems more agentic comes with an additional set of potential dangers and harms, as goal-directed AI systems could become capable adversaries if their goals are misaligned with human interest.
A better understanding of agency may let us:
Understand dangers and harms from powerful machine learning systems.
Evaluate whether a particular ML model is dangerously agentic.
Design systems that are not agentic, such as AGI scientists or oracles, or which are agentic in a safe way.
Lay a foundation for progress on other AGI safety topics, such as interpretability, incentives, and generalisation.
Preserve human agency, e.g. through a better understanding of the conditions under which agency is enhanced or diminished.
Degrees of freedom
(Goal-directed) agents come in all shapes and sizes – from bacteria to humans, from football teams to governments, and from RL policies to LLM simulacra – but they share some fundamental features.
First, an agent needs the freedom to choose between a set of options. We don’t need to assume that this decision is free from causal influence, or that we can’t make any prediction about it in advance – but there does need to be a sense in which it could either go one way or another. Dennett calls this degrees of freedom.
For example, Mr Jones can choose to turn his sprinkler on or not. We can model his decision as a random variable with “watering” and “not watering” as possible outcomes:
Freedom comes in degrees. A thermostat can only choose heater output, while most humans have access to a range of physical and verbal actions.
Influence
Second, in order to be relevant, an agent’s behaviour must have consequences. Mr Jones decision to turn on the sprinkler affects how green his grass becomes:
The amount of influence varies between different agents. For example, a language model’s influence will heavily depend on whether it only interacts with its own developers, or with millions of users through a public API. Suggested measures of influence include (causal) channel capacity, performative power, and power in Markov decision processes.
Adaptation
Third, and most importantly, goal-directed agents do things for reasons. That is, (they act as if) they have preferences about the world, and these preferences drive their behaviour: Mr Jones turns on the sprinkler because it makes the grass green. If the grass didn’t need water, then Mr Jones likely wouldn’t water it. The consequences drive the behaviour.
This feedback loop, or backwards causality, can be represented by adding a so-called mechanism node to each object-level node in the original graph. The mechanism n...