Dropping Science on Your Developer Ecosystem - lessons from Ecosystem Management
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Dropping Science on Your Developer Ecosystem - lessons from Ecosystem Management

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    Dropping Science on Your Developer Ecosystem - lessons from Ecosystem Management Dropping Science on Your Developer Ecosystem - lessons from Ecosystem Management Presentation Transcript

    • Dropping Science on Your Developer Ecosystem - lessons from Ecosystem Management@thesteve0Steven Citron-PoustyPaaS Dust SpreaderOpenShift – Red Hat
    • Slide with Tech ecosystem
    • Science!! chase_elliott from flickr
    • Slide with High School picture• If we go back to High School science Deserttrumpet on flickr
    • Ecosystems are real• Well they are actually a model – but with the good and the bad
    • And Conservation Biologists use This Model
    • They had a problem that needed to move beyond individual species at best Single species = emergency room Mass Energy and Env Affairs on flickr
    • Which Animal Forced the Issue
    • Yellowstone – satellite
    • Yellowstone - map
    • Yellowstone – ecosystem
    • Grizzly bear
    • Wolf
    • Grizzly Denali picture
    • So what are some of the ideas that I will focus on today
    • 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
    • Values and Goals• You get this from social, economic, and political• Most important• Not science or quantitative but drives everything
    • Science!! chase_elliott from flickr
    • Keystone• Keystone species – otter• Bottom of the food chain – menhanden
    • Who are the keystones in yourecosystem?
    • Who are your menhaden?
    • Planned (some forethought) vs NaturalExperiments (need long-termmonitoring before)
    • OpenShift ExampleAdaptive management and plannedexperiments
    • No differencedata: responses out of sentsample estimates:prop A prop B0.04800000 0.0533333395 percent confidence interval:-0.02169480 0.01102814X-squared = 0.3396, df = 1, p-value = 0.5601alternative hypothesis: two.sided Type I = saying there is a difference when there isn’t Type II = saying there is no difference when there is
    • What action can you turn into anexperiment?
    • Natural Experiment
    • OpenShift Example
    • What monitoring are you doing?
    • What adaptations can you make based on knowledge gained?
    • 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
    • r vs. K life history strategies and you
    • Where are you on the curve?