---
title: "When Does Design Thinking Work? A Convergence Theorem for the Iterate-Test Core"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2611.03034v1"
permalink: "https://feed.astralor.com/items/itm_876294c799f062570eab46a9"
published: "2026-11-02T12:31:35.000Z"
score: 58
channel: "finance"
tags: ["When Does Design","Thinking Work","Convergence Theorem","Iterate-Test Core Design","Taking","Design","Bounded"]
---

# When Does Design Thinking Work? A Convergence Theorem for the Iterate-Test Core

## 速览

Design thinking is widely taught as an iterative loop of ideation, prototyping, and testing, yet conditions under which such a loop must improve innovation outcomes remain unspecified. Taking problem framing as given, w…

## 关键点

- Design thinking is widely taught as an iterative loop of ideation, prototyping, and testing, yet conditions under which such a loop must improve innovation outcomes remain unspecified.
- Taking problem framing as given, we formalize the iterate-test core as search over a finite prototype space whose utility is learned through noisy evaluations.
- We prove that the prototype a team believes to be best converges almost surely to a local optimum under five conditions: finite framing, local ideation, non-summable discovery rates, unbiased repeated testing, and reten…

## 全文

Design thinking is widely taught as an iterative loop of ideation, prototyping, and testing, yet conditions under which such a loop must improve innovation outcomes remain unspecified. Taking problem framing as given, we formalize the iterate-test core as search over a finite prototype space whose utility is learned through noisy evaluations. We prove that the prototype a team believes to be best converges almost surely to a local optimum under five conditions: finite framing, local ideation, non-summable discovery rates, unbiased repeated testing, and retention of tested prototypes. Bounded test bias or criteria drift degrades the guarantee by a bounded amount, and multiple stakeholders yield convergence to a locally Pareto-optimal set. Finite-horizon simulations across three landscape classes (additive valuation, NK rugged landscapes, and clustered modular architectures) show that asymptotic limits are rarely reached in practice. Teams face an optimizer's curse, overestimating their chosen concept by 4-8% and frequently discarding superior prototypes. Testing every prototype once proves to be the worst allocation; optimal performance requires building one to three prototypes per five tests and repeatedly re-testing leaders. Pre-screening concepts yields greater performance gains than tuning testing policies.

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