Empirical Exploration of Complexity in Human Systems: Data Collection & Interpretation Techniques

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

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

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

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

12. Abduction Part 2: Observation Informs Theory Theory becomes both plausible & more completeBoth 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

13. Complexity in Human Systems1) 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!