1. Empirical Exploration of Complexity in Human Systems: Data Collection & Interpretation Techniques Introduction Jim Hazy, Facilitator

2. Panelists Jim Hazy (Facilitator) Pierpoalo Andriani Dave Snowden Max Boisot

3. Key Complexity Lessons We don’t know what we don’t know (nor who else might know something we don’t know) Stability/predictability vs anomaly/uncertainty

4. Key Complexity Challenge Tentative rigor: Objective is to Evolve theories Wary vigilance: Any observation may surprise and contain new information (but we might not be able to see it, or we may just ignore it)

5. Our Logic for Empirical Research Research when uncertainty is a certainty (Hazy) Cumulative advantage - models of solutions to interacting nonlinear dynamical systems (Andriani) Preserve weak signals in micro-narratives (Snowden) Abductive logic (Boisot)

6. Today’s schedule Introductions (10 min) Set-up (10 min) Brief talks (40 min) –Jim Hazy –Pierpaolo Andriani –Dave Snowden –Max Boisot Abductive reasoning exercise (20 min) Analysis/hands-on activity Usage & protocols Journal E:CO Special Issue Discussion Concluding Thoughts

7. Empirical Exploration of Complexity in Human Systems: Data Collection & Interpretation Techniques Abductive Reasoning Exercise Jim Hazy, Facilitator

8. Traditional Methods Induction & deduction simplify or ignore complexity and intend precise prediction as end state ObservationsDataHypothesesResults of tests Theory Support or Refute Observations DataHypothesesResults of tests Theory Replaced by Replaced by Induction Deduction Focus Rightly On Experiment Design & Results Analysis In contrast, abduction uses continuous feedback and acknowledges uncertainty Support or Refute Richness in Data Is Lost

9. Abduction & Continuous Feedback Abduction embraces complexity, accepts limits to observation and experiment design & assumes probabilities & uncertainty as end state Perspective-limited Observations Rich Data Set Hypotheses Analysis Includes Search Evolved Theory Embedded In Indexing; Data Set Maintained Tentative Theory Enhanced Observations for Weak Signals in Data and New Hypotheses Rich Info Remains in Data Set Models Used for Anticipatory Awareness that Accepts Uncertainty Abduction describes a process where continuous feedback is used under uncertainty Continuous feedback enhances theoretical perspective and enriches observation

10. Empirical Exploration of Complexity in Human Systems: Data Collection & Interpretation Techniques Concluding Thoughts Jim Hazy, Facilitator

11. Micro- Narrative: Story, Document, Photo, or video Why important? Organizations Leadership Organizations Motivation Emotions Innovation, etc. Title Mission Values Behaviors Communication Traits Relation And so on… Data as Richly Preserved/Semantically Indexed Artifacts “Tagging” that is informed by theory provides index Abduction Part 1: Theory Informs Observation Signifying is not just keywords; signifiers are “soft hypotheses” used to “test” theories

12. Theory becomes both plausible & more complete Both prediction & surprise are equally valued Nonlinearity using rigorous linear approximation techniques – minimize implicit assumptions New statistical techniques to find and analyze fat tails Clarify separation between initial conditions, boundary conditions or constraints and “mechanisms” Identify mechanisms Abduction Part 2: Observation Informs Theory

13. Complexity in Human Systems 1) Implies that New Research Methods are needed & 2) Provides the tools to develop them

14. Thank You CFP Special Issue Emergence: Complexity & Organization Please sign our email list & join LinkedIn Group when invited!