hardertoreasonabout

Hardertoreasonabout is a coined label used in discussions of analysis and decision-making to describe problems that become more difficult to reason about as the depth or breadth of reasoning increases. The phrase captures the intuition that adding layers of explanation, assumptions, or components often raises complexity or creates unexpected interactions.

Definition and scope: It is not a formal theory but a heuristic. The term describes situations in which modeling, forecasting, or ethical evaluation yields increasing uncertainty, conflicting priors, or escalating cognitive load as one attempts to account for more factors or future states. It often involves recursive or self-referential aspects, where trying to understand the reasoning process itself alters the problem.

Causes: Nonlinear dynamics, emergent properties, feedback loops, hidden dependencies, model misspecification, conflicting objectives, and normative ambiguity can all contribute. New information or shifting contexts can change the problem’s structure in ways that are hard to anticipate.

Examples: In artificial intelligence, attempting to predict or control a system whose behavior depends on its own predictions can become harder to reason about. In public policy, evaluating policies with extensive indirect effects or multi-agent responses can escalate complexity. In ethics and law, adding constraints may generate new dilemmas or loopholes that were not apparent at the outset.

Implications: The concept helps frame caution against overconfidence in models that seem tractable at first glance. It supports practices such as modular analysis, explicit assumption accounting, robustness checks, and second-order thinking, while warning against assuming that increases in detail always yield clearer understanding.

See also: second-order thinking, recursive reasoning, complexity theory, frame problem, unintended consequences. The term is informal and mainly used in analytical discussions rather than formal domains.