1. Becoming Visual--Representation of Complex Systems Andrew Koehler, PhD LA-UR-03-6767

2. 2 My Interest in Systems Analysis and Representation PhD in Public Policy from UC Berkeley in the study of hazardous/complex systems operations and analysis (NPP, Aircraft Carriers, etc) At Los Alamos I work primarily on engineered system reliability and process operations—this has led to work on tool development for complex systems representation, simulation modeling

3. 3 Overview 1) Why Does Complex System Representation Matter? 2) Methods and Trade-offs in Thinking About Complex System Representation 3) Complex System Representation Methods 4) Lessons and Conclusion

4. 4 1)Why Does Representation Matter? Understanding how a complex systems function depends upon detective work. –System participants often themselves don’t understand how the system operates –As an outsider you won’t either Accordingly, you will almost never start the complex system analysis process with the information needed to develop a mathematical representation Complex Systems are Complex!

5. 5 Complex System Representation and Multidisciplinary Perspectives Complex systems are typically characterized by uncertainties stemming from “multi-disciplinary sources” seismic analysis of a nuclear power plant, involves structural, hydraulic, geology, operational and regulatory constraints analysis of futures market for coffee, requiring consideration of agricultural, meteorological, transportation, economic variables

6. 6 Representation and Complex System Modeling As a mathematical scientist you want a mathematical system representation: What you start with is generally a mess: Theory Computational results Disciplinary literature Data

7. 7 Getting from where you start, to a quantitative model Elicitation of Complex System Characteristics Development of a Mathematical Model for Complex System Performance Some kind of process to distill the salient features of the system Variety of disciplinary names for this process: --system definition (SE) --information modeling (IT) --others

8. 8 Representation is not just pictures, but a form of disciplined modeling Representation is a key component in making sense of a complex system. Inevitable that cognitive models of a system are created, hence formal representation necessary to ensure that these cognitive models are: –Verifiable –Shared –Accurate

9. 9 Diagrams from Bivot and Chen, “Energetics and the Establishment of Eukaryotic Mitochondria,” New England Complex Systems Institute, MIT, Jan. 6- 10 2003 Getting to a Mathematical Representation Being able to create something like this, makes crafting these much easier:

10. 10 2) Methods and Tradeoffs in thinking about representation Getting a useful representation depends critically upon what is the purpose of the representation and what facets of the system will be analyzed. Suppose you are trying to model the effects of volcanic activity at Mt. Fuji

11. 11 Representations of the same thing: Which has all the information you need?

12. 12 Representations of the same thing: Which has all the information you need? (part 2) All have pieces of useful information, however no single representation can adequately completely describe this complex system What is needed is set of visual models, each describing different facets of the same complex system so that all relevant information can be captured and used. We can’t discard information because we don’t know what matters yet to define a mathematical model.

13. 13 Representations of the same thing: Which has all the information you need? (part 3) Complex system representation requires use of different kinds of visualizations, linked together by a common taxonomy, for example: Conceptual graphs (perhaps to describe Mt. Fuji as a set of interwoven ideas and relevant variables) Influence/dependency diagram (perhaps to describe those variables developed in the CG relating potential lava flow to economic, population movement effects) Directed Acyclic Graphs (Chain Graph) (perhaps to describe covariance relationships between seismic activity, geologic measures, and eruption likelihood)

14. 14 3) System Representation Taxonomies and Methods An Example: Suppose the NRC wants to know what it would mean for the nuclear power industry in the US if an earthquake disrupts an NPP in California? Note: all data presented is fictitious in this example

15. 15 An Example: How to approach this as a representation issue? A line of Graphical Inquiry could include the following: a) A concept graph or scratch net to define what the major concepts mean: how does an earthquake differ from other ways the plant could be disrupted? (the ethnographer’s view of the system) b) A network graph to explore how the CA NPP fits into the electrical grid (the social network view of the system) c) A plant component hierarchy to better understand how plant systems are the same/differ from those elsewhere (the systems engineering view of the system) d) A plant process graph to better understand how the plant functions and what modes of operation and failure may exist (the plant operators view of the system)

16. 16 An Example: Part I A concept graph or scratch net defines the major ideas that make up a complex system and the relationships between these ideas.

17. 17 An Example: Part II A Network Graph helps explore how complex system parts are interconnected and how they communicate

18. 18 An Example: Part III A component hierarchy helps describe how subsystems are related to one another

19. 19 An Example: Part IV A plant process graph shows what functions the plant must perform in order to produce power and hence (if disrupted) what must be done to return to service

20. 20 Maintaining Traceability of Ideas Across these Perspectives These different perspectives on the complex system under study need to be maintained in one place, under one organized structure—all contain useful information about how the NPP, the earthquake, and the outside world interrelate. How to do this well is a primary research challenge.

21. 21 Trade-offs in the Design of Visual Representations: The factors that make this an interesting problem for research Clarity, abstraction and fidelity Compatibility with audience practice Dimensionality Color and design Analytic use Visual rules: rigidity versus flexibility

22. 22 4). Lessons and Conclusions The importance of design as part of qualitative modeling Complex systems cannot be reduced to single dimensional problems for purposes of analysis, neither can representations of complex systems You need to capture as much as you can about the system, because you don’t know what will be important in order to specify the model properly