# Design principles for a hybrid intelligence decision support system for business model validation

arXiv: https://arxiv.org/abs/2105.03356 · HTML: https://mlsanity.com/paper/2105.03356/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 120 references that are in the map.
- First posted on arXiv 7 May 2021.

## Built on by (most cited first, 4 of 4)

- [Hybrid Forecasting of Geopolitical Events](https://mlsanity.com/paper/2412.10981/) (2024)
- [Ontology in Hybrid Intelligence: a concise literature review](https://mlsanity.com/paper/2303.17262/) (2023)
- [Personalities at Play: Probing Alignment in AI Teammates](https://mlsanity.com/paper/2603.00429/) (2026)
- [FundaPod: A Multi-Persona Agent Pod Architecture with Knowledge Graph Memory for AI-Assisted Fundamental Investment Research](https://mlsanity.com/paper/2605.27864/) (2026)

## Abstract (arXiv)

One of the most critical tasks for startups is to validate their business model. Therefore, entrepreneurs try to collect information such as feedback from other actors to assess the validity of their assumptions and make decisions. However, previous work on decisional guidance for business model validation provides no solution for the highly uncertain and complex context of earlystage startups. The purpose of this paper is, thus, to develop design principles for a Hybrid Intelligence decision support system (HI-DSS) that combines the complementary capabilities of human and machine intelligence. We follow a design science research approach to design a prototype artifact and a set of design principles. Our study provides prescriptive knowledge for HI-DSS and contributes to previous work on decision support for business models, the applications of complementary strengths of humans and machines for making decisions, and support systems for extremely uncertain decision-making problems.