The document discusses topics related to knowledge, complex systems, decisions under uncertainty, and risks. It covers how to understand and manage unpredictable change, knowledge production in chaotic systems, and tools for analyzing complex problems. The goal is to facilitate decision making on complex issues and discuss perspectives on uncertainty and risk that may be unfamiliar to non-scientists and decision makers.

1. Knowledge, Complex Systems, Decisions, Uncertaintly, Risks. Nora H Sabelli Center for Technology in Learning, SRI International Center on Learning in Informal and Formal Environments

3. A goal of this session is to facilitate decision making processes on complex issues. Central to an uncertainty and risk approach when the risks as quite substantial is the concept of different perspectives. risk-seeking, risk-accepting and risk-aversive How to evaluate uncertainty and risk are not always familiar or acceptable to non-scientists in general and to decision-makers in particular.

4. Rationale We need to understand the nature of solutions optimal efficient effective robust favorable resilient (flexible, adaptable)

5. We must distinguish between chaos (particularly deterministic chaos) uncertainty unknowability (imposibility of obtaining knowledge) And touch on the nature of innovation expertise

6. Chaos and Complexity open systems and closed systems Complexity deals with non-linear systems, instead of negative feedback (damping), positive feedback (reinforcement) can occur. Chaos can be deterministic; i.e. may not be fully predictable but may lead to a menu of predicatible behaviors. “ Edge of chaos” systems are referred to as ‘complex adaptive systems’ and are not determined, but and can, in fact, be often modeled probabilistically.

7. “ Strange attractors” “ Attractors” because their solutions are bounded “ Strange” because the system can jump from one extreme to the other.

10. The certainty trough MacKenzie, D. (1990). Inventing Accuracy: a historical sociology of nuclear missile guidance (Cambridge, Mass.: MIT).

12. The concept of “Adaptive Expertise” from Hatano & Inagaki offers an initial framework. The LIFE Center considers it as a balance between efficiency and innovation, and including the need to abandon prior ideas and procedures.. Innovation Efficiency Adaptive Expert Routine Expert Frustrated Novice Novice Optimal adaptation corridor

14. Adaptive Expertise Adaptation over Time Fault Productivity Dip Efficiency Plateau Transfer the Idea of Innovating Past an Impasse (S) (D) Learning to handle steep “faults” in adaptiveness (from Dan Schwartz)

15. Definitions from NSF Innovation and Discovery Workshop: The Scientific Basis of Individual and Team Innovation and Discovery (2006) Innovation does not necessarily imply a fundamental change in some aspect of the general environment or of the process. It can refer to changes in ways of working and thinking that are new to the individual, his or her local environment, or that coordinate in new ways the interaction between a person and his or her resources. I nnovation o i nnovation Both imply processes that are reproducible, social, cognitive and/or physical, situated simultaneously in the individual and his or her team and organization.

20. Additional reading materials: System Dynamics and Uncertainty, Risk, Robustness, Resilience and Flexibility. Erik Pruyt, Delft University of Technology www.systemdynamics.org/cgi-bin/sdsweb?P386 Fundamental uncertainty and ambiguity. David Dequech Texto para Discussão. IE/UNICAMP no. 93, mar. 2000. A complex systems approach to learning in adaptive systems. Peter Allen. International Journal of Innovation Management. Vol 5, June 2001. No. 2 pp, 149-180.