Roberto Infante

Bounded agent

A bounded agent cannot consider every possible explanation, action, or future consequence due to limited resources.

How the framing evolved history

Version A: a literal hypothesis set.

ℋ_t = {h₁, h₂, h₃},  P(h^* ∉ ℋ_t) > 0

"How do I know h^* isn't in my set?"

Version B: but what even counts as h? Can hypotheses be partial?

h = these four share r,  r = ?

Current: don't require a literal hypothesis set.

G_t ⟷ C_t ⟶ A_t

Possibilities of different degrees of specification become available during generative reasoning. The project studies what signals tell the reasoner that what is currently available may be insufficient, without first solving where a hypothesis begins.

Questions we still need to answer (C_t: what's on the table)

• What is the representational object? Is it a rule, causal model, gist, category, explanation, structured relation?
• Can it be incomplete or coarse? Can the person have "something about these belongs together" without the full explanation?
• Can that partial representation guide a deliberate decision?
• Can it later be refined while preserving earlier content?
• When does the literature treat it as the same representation becoming more specific versus a new hypothesis replacing the old one?
• What behavioral evidence shows that the representation is actually available to the person?
• What counts as a currently available possibility in a language model? Must a possibility have been explicitly generated into context by the time of a decision, or can an unexpressed internal representation count as available?

Open questions (A_t: recognizing insufficiency)

• How do humans recognize that the possibilities currently available during reasoning may be insufficient?
• What information in the dynamics of ongoing reasoning provides evidence that the currently available possibilities may not be enough?
• How does a reasoner distinguish "I have not solved this yet" from "what I currently have may itself be insufficient"?
• Can recognition of insufficiency occur before a reasoner can identify what possibility is missing?
• Does recognition of insufficiency depend on a single diagnostic signal, or on the integration of multiple signals over time?
• How do failure, confidence, progress, retrieval, and changes in candidate possibilities contribute to recognition of insufficiency?
• Does the history of reasoning matter (repeated failure, repeated return to the same possibilities), or is the current reasoning state sufficient?
• How does the partial development of a possibility affect judgments of insufficiency? Does a vague but promising possibility feel different from no promising possibility at all?
• When a useful possibility has failed to enter consideration, are there detectable signatures in the reasoning process before that possibility is eventually discovered?
• Can a computational model predict when a person will conclude that their currently available possibilities may be insufficient?
• Can that model distinguish recognition of insufficiency from low confidence, difficulty, retrieval failure, conflict, or simply needing more time?
• What causes recognition of insufficiency to change subsequent reasoning? Does it lead to more search, different search, refinement of partial possibilities, retrieval, or representational change?
• When does additional search reflect recognition of insufficiency rather than ordinary persistence?
• Do people differ systematically in their ability to recognize insufficiency, and do those differences predict successful discovery of previously unconsidered possibilities?
• Does experience with a task change recognition of insufficiency by changing which possibilities become available, by changing how their adequacy is monitored, or both?
• Do contemporary machine reasoners recognize when their currently available possibilities may be insufficient, or do they primarily continue evaluating and elaborating what they have already generated?
• When humans and machines fail because a useful possibility never entered consideration, do they show different signatures before the failure?
• Do machines use signals analogous to those that predict recognition of insufficiency in humans?
• Can a computational mechanism derived from human recognition of insufficiency predict machine failures that confidence alone cannot?
• Can introducing such a mechanism improve a machine reasoner's ability to change its search when its currently available possibilities are inadequate?