This document discusses lessons from ecosystem management that can be applied to technology ecosystems. Some key points covered include: - Ecosystems are complex with permeable boundaries and require a holistic, adaptive approach focused on overall integrity. - Monitoring is important to collect data and inform adaptations over time through both planned experiments and taking advantage of natural experiments. - Identifying keystone components and factors that influence the whole system is important for management. - Values and goals drive management more than facts and should incorporate social, economic, and political considerations. - Analogies to natural ecosystem management can provide insights for nurturing diversity and resilience in technology ecosystems.

1. Dropping Science on Your
Technology Ecosystem -
lessons from Ecosystem
Management
@thesteve0
Steven Citron-Pousty
PaaS Dust Spreader
OpenShift – Red Hat

3. Slide with Tech ecosystem

4. Science!!
chase_elliott from flickr

7. Science!!
chase_elliott from flickr

10. Ecosystems are real
• Well they are actually a model – but with the
good and the bad
AA model of the world

11. BUT, Conservation Biologists use This
Model

12. They had a problem that needed to
move beyond individual species at best
Single species = emergency room
Mass Energy and Env Affairs on flickr

13. Where did it really start

14. Yellowstone - map

15. Yellowstone – satellite

16. Yellowstone – ecosystem

17. Grizzly bear

18. Wolf

20. Grizzly Denali picture

21. Main ideas of ecosystem management
• Ecosystems are multi-dimensional
• Boundaries are only as real as you want them to be
• Manage for overall integrity
• Always collect and synthesize primary data
• Engage in monitoring
• Inter-Agency cooperation
• Humans embedded in nature
• Adaptive Management – experiment and learn
• Open to organizational change as fits the system
• Values are more important than facts and logic

22. Values and Goals
• You get this from social, economic, and
political
• Most important
• Not science or quantitative but drives
everything

23. Science!!
chase_elliott from flickr

26. Keystone
• Keystone species – otter
• Bottom of the food chain – menhanden

27. Who are the keystones in your
ecosystem?

31. Who are your menhaden?

32. Planned (some forethought) vs Natural
Experiments (need long-term
monitoring before)

33. OpenShift Example
Adaptive management and planned
experiments

34. No difference
data: responses out of sent
sample estimates:
prop A prop B
0.04800000 0.05333333
95 percent confidence interval:
-0.02169480 0.01102814
X-squared = 0.3396, df = 1, p-value = 0.5601
alternative hypothesis: two.sided
Type I = saying there is a difference when there isn’t
Type II = saying there is no difference when there is

35. What action can you turn into an
experiment?

36. Natural Experiment

39. OpenShift Example

41. What monitoring are you doing?

42. What adaptations can you make based
on knowledge gained?

43. Take homes
• Be more quant
• Do experiments don’t just do
• Take advantage of natural experiments
• Manage your ecosystem for key indicators
• Diversity is important
• Take the analogy of ecosystems farther and
learn from them

45. r vs. K life history strategies and you

46. Where are you on the curve?