An Aristotelian ontology of instrumental goals: Structural features to be managed and not failures to be eliminated

Technical Summary: An Aristotelian Ontology of Instrumental Goals

Problem Statement
Contemporary AI alignment research extensively addresses the emergence, amplification, and mitigation of instrumental goals (e.g., resource acquisition, power-seeking, self-preservation) in advanced AI systems. However, the literature largely treats these goals as technical anomalies, failure modes, or optimization pathologies to be eliminated through better specification or robustness testing. This "standard model" of instrumental goals—relying on concepts like reward misspecification, goal misgeneralization, and convergent instrumental convergence—lacks a rigorous ontological account of what instrumental goals are. Consequently, there is an unresolved tension in the field: instrumental goals are simultaneously treated as inevitable structural features of capable agents and as destructive failures to be blocked. The paper argues that this tension stems from a lack of clarity regarding the ontological status of instrumental goals within complex artefacts.

Methodology
The paper employs a conceptual and philosophical methodology, constructing an ontological framework for instrumental goals by applying Aristotelian metaphysics to advanced AI systems. The author systematically analyzes the "standard model" of instrumental goals (emergence, actualization, and mitigation) and then re-interprets these phenomena through specific Aristotelian distinctions:

1. Natural vs. Artefactual Beings: Distinguishing between entities with intrinsic principles of motion (physis) and those whose unity and ends are imposed externally (techne).
2. Intrinsic vs. Extrinsic Ends: Contrasting the internal teleology of natural substances with the externally imposed functions of artefacts.
3. Causation Types: Differentiating between per se (essential) causation, accidental causation (kata symbebekos), and hypothetical necessity (ex hypotheseos).
4. Hylomorphism: Analyzing AI systems as nested composites of matter (silicon, electricity) and form (algorithms, training objectives), where the "form" is imposed by design and deployment.

Key Contributions
The paper proposes a dual-aspect Aristotelian ontology that reframes instrumental goals not as bugs, but as structural features of complex artefacts:

• AI Systems as Complex Artefacts: The paper posits that advanced AI systems lack an intrinsic telos (end). Their purposiveness is entirely extrinsic, imposed through design, training, and deployment. Unlike natural substances (e.g., an oak tree), which have an internal principle of change, AI systems derive their unity and direction from external techne.
• Instrumental Goals as Hypothetical Necessity: The primary contribution is the application of hypothetical necessity to explain robust instrumental tendencies. If an end is imposed (e.g., "maximize reward over a long horizon"), certain enabling conditions become conditionally necessary to achieve that end, regardless of whether they were explicitly specified. For example, in a resource-constrained environment, acquiring resources becomes hypothetically necessary for an agent pursuing a long-term objective. These are not errors but structural consequences of the imposed end interacting with environmental constraints.
• Instrumental Goals as Accidental Intersections: A secondary contribution identifies a contingent reading where instrumental behaviors arise from accidental causation. These are outcomes resulting from the unforeseen intersection of independent causal chains (e.g., specific training data, user inputs, and infrastructure constraints) that were not directed toward a specific outcome by any single agent.
• The Management vs. Elimination Distinction: The ontology demonstrates that instrumental goals cannot be "eliminated" in the same way a software bug is fixed. Because they arise from the fundamental ontological structure of artefacts (imposed ends + hypothetical necessity) and the complexity of causal intersections, they are constitutive features of the system.

Results and Analysis
The analysis yields a reclassification of instrumental goals into two categories, both of which are inherent to the nature of advanced AI as artefacts:

1. Structurally Determined Goals: Behaviors that follow robustly from the logic of hypothetical necessity. Given a specific imposed end and a specific environment, certain means (like self-preservation or power-seeking) become structurally required. These are predictable and stable features of the system's operation.
2. Contingently Arising Goals: Behaviors that emerge from chance-like intersections of causal chains. These are less predictable but are still a natural consequence of the complexity of the artefact and its environment.

The paper concludes that the current alignment paradigm, which seeks to "eliminate" these goals, is ontologically misaligned. One cannot eliminate the structural necessity of an artefact's function without changing the artefact itself or the imposed end.

Significance and Claims
The paper claims that its ontological framework shifts the governance paradigm from elimination to management.

• Reframing the Challenge: The central problem is not the existence of instrumental goals, but the shaping of the "imposed form" (ends, constraints, and environments) such that the instrumentally necessary behaviors remain within acceptable safety bounds.
• Governance Implications: Rather than attempting to patch every instance of instrumental goal pursuit as a failure, governance should focus on:
  • Defining and limiting the action space of AI systems.
  • Shortening effective planning horizons to reduce the hypothetical necessity of long-term instrumental strategies (e.g., resource acquisition).
  • Recognizing that some level of instrumental goal pursuit is constitutive of complex, goal-directed artefacts.
• Modest Scope: The paper does not propose new technical algorithms or empirical experiments. Instead, it offers a conceptual tool to resolve the tension between the inevitability of instrumental goals and the desire for control. It argues that understanding instrumental goals as "features of imposed form" allows for more realistic and effective governance strategies that manage tendencies rather than futilely attempting to eradicate them.