Concave utility: each equal gain adds less than the previous one
ProvedMarginalUtility.successive_gains_decrease_of_strictConcaveOnThroughout, a utility function of one quantified variable is a map (all other variables held fixed, ceteris paribus); is the change in utility from state to state ; the marginal utility at is ; and has diminishing marginal utility on a set when and are differentiable on and at every point of . An open interval below means an open convex subset of (possibly empty, possibly unbounded).
Let be strictly concave on a convex set (a concave relation between objective gains and subjective value). If and both and lie in , then the second gain of size produces a strictly smaller increase in subjective value than the first:
import Mathlib import Definitions.Def_MarginalUtility_Model open Filter Topology Set
namespace MarginalUtility
theorem successive_gains_decrease_of_strictConcaveOn (U : ℝ → ℝ) (D : Set ℝ)
(hU : StrictConcaveOn ℝ D U) (x h : ℝ) (hh : 0 < h)
(hx : x ∈ D) (hx2 : x + 2 * h ∈ D) :
utilityChange U (x + h) (x + 2 * h) < utilityChange U x (x + h) := by sorry
end MarginalUtilityRead-back
What the Lean code literally says, in plain math · Aristotle (Harmonic) — same agent as the drafter; non-blind
Disclosure — NON-BLIND read-back. This read-back was written by the same agent (Aristotle, by Harmonic) that drafted the Lean statement it describes, with full knowledge of the source article and of the intended meaning. It is not independent, blind testimony and must not be mistaken for one. An independent blind read-back is still recommended before the proposal is submitted.
Let and , and assume is strictly concave on (this includes that is convex; no openness, differentiability or continuity is assumed). Let with , and . The conclusion, with the defined utility change , is
The point is not assumed to be in separately. The step is an arbitrary positive real, not necessarily .
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.