This document proposes a novel paradigm of hybrid intelligence and decision support systems to guide complex entrepreneurial decisions. It argues that combining artificial and human (collective) intelligence can help address the challenges of unpredictable dynamics, risk, and ecosystem influences in entrepreneurial decision making. Two naive approaches - crowdsourcing/collective intelligence alone and statistical/AI methods alone - are insufficient. Instead, it presents hybrid intelligence as a new design paradigm that leverages the strengths of humans and AI systems learning from each other. Key contributions include design principles for hybrid intelligence decision support systems, a cognitive rationale for collective intelligence guidance, and requirements for crowdsourcing complex entrepreneurial decisions. Future research opportunities involve empirical validation, incentive models, and issues of trust and

1. accelerating entrepreneurial decision-
making through hybrid intelligence
ICIS, San Francisco
Dominik Dellermann, darwin

2. A LONG STORY SHORT
✔ entrepreneurial decisions are hard
✔ combination of AI and human (collective) intelligence
✔ novel paradigm of decisional guidance for complex
problems

3. THE SITUATION
2017 2018 2019
✔ Decision-Making under Risk and Uncertainty
✔ Unpredictable Dynamics
✔ Crucial Role of Ecosystem (Investors, Users,
Stakeholders) in Guiding Entrepreneurial
Decisions
(Huang and Pearce 2015;Alvarez et al. 2013; Knight 1921)

4. THE SITUATION
(CB Insights 2018; PwC 2016)

5. THE GAP
design
paradigms and
principles for
decisional
guidance
✖ Limited Investigation of
Scalable Mechanisms for
Decisional Guidance with
Ecosystem Integration
✖ Limited Investigation of
Decisional Guidance and DSS for
Complex Decision-Making under
Uncertainty and Risk

6. 24.02.19 6THE BODY OF KNOWLEDGE
(Alvarez et al. 2013; Silver 1991; Malone et al. 2009)
DSS and Decisional
Guidance
Crowdsourcing
and Collective
Intelligence
Entrepreneurial
Decision
Making

7. NAÏVE APPROACH I
COLLECTIVE INTELLIGENCE &
CROWDSOURCING

8. 24.02.19 8LIMITATIONS NAÏVE APPROACH I
BUT:
CROWDSOURCING MECHANISMS LACK
REQUIREMENTS FOR COMPLEX CLASS
OF PROBLEM

9. NAÏVE APPROACH II
STATISCTICAL METHODS & AI

10. 24.02.19 10LIMITATIONS NAÏVE APPROACH I
BUT:
PREDICTING THE FUTURE FROM THE
PAST AND ON WHAT KIND OF DATA?!

11. 24.02.19 11HYBRID INTELLIGENCE
“[…] the ability to accomplish complex goals by
combining human and artificial intelligence to collectively
achieve superior results than each of them could have
done in separation and continuously improve by learning
from each other […]”
(Dellermann et al. 2019a: 3)

12. 24.02.19 12HYBRID INTELLIGENCE
(Dellermann et al. 2019a and b)
?

13. 24.02.19 13HYBRID INTELLIGENCE
(Dellermann et al. 2019a and b)
?

14. 24.02.19 14HYBRID INTELLIGENCE
(Dellermann et al. 2019a and b)
?

15. THE IMPLEMENTATION
(Dellermann et al. 2018b)

16. THE IMPLEMENTATION
(Dellermann et al. 2018b)

17. THE IMPLEMENTATION
(Dellermann et al. 2018b)

18. THE IMPLEMENTATION
(Dellermann et al. 2018b)

19. THE DESIGN PRINCIPLES
(Dellermann et al. 2018b)
1 Profile Ontology for Mutual Understanding
2 Expertise Matching
3 Human Suggestive Guidance

20. THE DESIGN PRINCIPLES
(Dellermann et al. 2018b)
4 Meritocratic Crowd-based Classifier
5 Digital Twin
6 Guidance Representation

21. CONTRIBUTION
Hybrid Intelligence
as Guidance
Design Paradigm
Design Principles
for HI-DSSs
Cognitive Rational
of Collective
Intelligence for
Guidance
Requirements of
Crowdsourcing for
Guiding
Entrepreneurial
Decisions

22. THE FUTURE
RESEARCH
1. EMPIRICAL VALIDATION OF
EFFECTS
2. INCENTIVE MODELS
3. TRUST AND
INTERPRETABILITY

23. accelerating entrepreneurial decision-
making through hybrid intelligence
Dominik Dellermann, darwin