R. H. Weber, Sr. P.E. The Aerospace Corporation (310) 336-5715

1. Where is the Fastest Way Ahead to Understand & Design Complex Human Systems?The Multi-Agent-based Simulation Path R. H. Weber, Sr. P.E. The Aerospace Corporation (310) 336-5715

2. System & Operations Engineering Reference Model “Opposed Systems” (1) National Utility National Utility Operations Engr. Military Utility Political Utility Economic Utility Mission Systems Design Enterprise Archit Military Mission Utility Mission Design Effects Space Control Air Offense Air & Missile Def. Dominant Maneuver Littoral Warfare Operational Archit Functional National S-A-G* Intell Performance * S-A-G Comm * S-A-G Navigation * S-A-G Surveill. & Recon Missile TW/AA Space-Based KEW Product Archit Performance System Program Level and Cost Constellation Utility Payload Spacecraft Ground Life Cycle Cost Availability Component System Component Level System Engineering Archit Design Propulsion Comm Processing Payload Software Structure Power and Cost Life Cycle Cost ADACS Launch Ground C&DH Thermal • Albert Wohlstetter, “Theory and Opposed-Systems Design”, • RAND Report D(L)-16001-1, August 1967 * S-A-G means Space--Air--Ground

3. Agent-Oriented System Engineering Design of Complex Systems System Engineering Software Engineering It from bit Complexity Science Agent-based Engineering • R. H. Weber • H/W oriented E-O syst. engineer (USAF) • S/W oriented MS&A (Aerospace Corp) • Hardened 10 yr vet. of cultural battles • Barriers to Progress: • Pardon me, but your obsolete ontology is showing • US culture of individuals, innovation, adhocracy, organized “stovepipes” • US neglect of intellectual infrastructure • Maladaptive effects of Cold War on military-industrial complex • Impact  Loss of US industrial/economic competitiveness at macro-system level • EU Airbus Consortium (Catia) & Japan Toyota (kanban, kaizen, lean/agile mfg) • GOOGLE experiment: “agent-oriented system engineering”  2 of 10 hits are US • “agent-oriented”, system, engineering, –software  3 of 10 are US

4. Recognizing & Overcoming Cultural Barriers Will Rogers: “It’s not what people don’t know that hurts them, it’s what they know that ain’t true.” Implies that education of government & industry managers deserves top priority

5. THEORETICAL EXPERIMENTAL • Network Models • RyFragile—Doyle, Willinger • Hierarchical • Scale-free • Random • Small worlds COMPLEX HUMAN SYSTEMS • Multi-scale Math • Fractal dimension • Renormalization Group • Algebraic Multi-grid • Non-commuting Geometry • Architecture Design • Codification—Alexander, Salingoros • 4-D CAD • Agent-Orient Rqmts Engr--Yu • Design for Emergence • Language of Simulation • Pedagogy of time/evol.--Wilensky • Multi-dimen. HCI--Shneiderman • Visual Programming—Alan Kay • Digital Philosophy—McKelvey • Multi-scaleScenario Design • Robocup Soccer (Grnd Chall) • Mathematica--Wolfram • Multi-Resol—Davis • UML--Booch • Multi Agent-based Models • Distributed AI • Vehicle traffic sim & control--Helbing • Social network analysis--Carley • Policy analysis—Cederman, Bankes • Autocatylic behavior--Solomon • Dynamical Systems • Continuous, Discrete, Hybrid • Chaotic, Periodic, Attractor • Turing Machine (TM) • Interaction Machine--Wegner • Control & Cybernetics • Hybrid control—The Control Revolution • Design for Robustness—Doyle, Carson • SW failure analysis—Leveson, Dornier • 2nd Order Cybernetics—v.Foerster • Appl. cognitive Science—Hawkins, Peirce • Game Theory—Pietarinen, Friedman, Camerer • Robotics • Probability & Risk • Fuzzy Sets/Logic—Zadeh, Kosko • Power Laws/Fat Tails • Epistemic Uncertainty—Taleb Map of Complexity & S-o-S Engineering

6. Multi-Scale Scenario Builder • An intentional agent at the smallest tactical or individual level may perpetrate actions that are intended to have the major effect at the strategic scale. The two WTC attacks eight years apart illustrate the difference of the first tactical scale event having only a regional effect and the second a global effect. More commonly it takes an integration of smaller scale effects within some moving time window to cause an effect at a larger scale. In addition to effects between different scales, there are possibilities of effects between two players within the same scale. Both the inter and intra-level effects can be described within the DoD paradigm of “Effects Based Operations.” Some examples of these varieties are given below. (Strategic= S, Theater=Th, Tactical = Ta) • S  Ta • --Constrain movement & check ident. at borders or checkpoints on roads • --Media reports that inhibit or encourage individual violence in public • S  Th • --Failure of a leader’s action encourages factions or coup attempts • --National oppression of ethnic or religious groups generates resistance • S  S • --National leader stimulates change in leader of rival nation regarding policy risk or priority of resource allocation • --Nations negotiate for trade leverage (OPEC) or economic embargo (UN towards Saddam) • Th  Ta • --Affiliation group leaders incite followers to riot or attack other groups • --Success of black market economy encourages size and number of gangs • --Coordinated attacks on utility networks increases disrespect for state • Ta  Th • --Siphoning off resources (oil) from state increases size of black market • Ta  Ta • --Witnesses to violence have agent state change from policy of avoid enemy to revenge • Ta  S • --Public violence triggers govt. tighter movement constraints & violence towards public in streets

7. Why Agent-Oriented M&S Needed for Design? • a priori argument– it’s required for conceptual modeling • When “complex system” being designed involves “humans in the loop” for operations or processes involving that system (intention, goal conflicts, human/soft factors & cognitive limitations) • When “complex system” has autocatalytic or autonomous control subsystems with discrete, multi-modal adaptive responses to environment (hybrid control theory, behavior of ecology and of adaptive life forms) • When complex physical phenomena involve moderate number of heterogeneous objects and symmetry breaking or phase change boundaries (eg. far from equilibrium condensed matter physics) • a posteriori argument—case history of successful design impact • Comm & computer network management via distributed control • Designing protocols for use of anti-biotics In such cases equation-based models don’t pass face validity!

8. M&S Needs Agents That are Discrete & Autocatalytic “….The discrete character of the individuals turns out to be crucial for the macroscopic behavior of complex systems….the slightest microscopic granularity insures the emergence of … localized macroscopic collective objects with adaptive properties…. The exact mechanism by which this happens depends crucially on the other unifying concept appearing ubiquitously in complex systems: auto-catalyticity. The dynamics of a quantity is said auto-catalytic if the time variations of that quantity are proportional (via stochastic factors) to its current value. It turns out that as a rule, the "simple" objects responsible for the emergence of most of the complex collective objects in nature have auto-catalytic properties. Autocatalyticity insures that the behaviour of the entire system is dominated by the elements with the highest auto-catalytic growth rate rather than by the typical or average element. This has profound implications on the very concept of scientific explanation: the fact that the dynamics is dominated by the exceptional individual/events (that enjoyed fortuitously the fastest stochastic growth factor)invalidates "reasonable" arguments based on the … 'average' or 'representative' case.This in turn generates the conceptual gap separating...disciplines: in conditions in which only a few exceptional individuals dominate… in the emergence of nuclei from nucleons, molecules from atoms, DNA from simple molecules, humans from apes, there are always the un-typical cases…that carry the day. This is the challenge of complexity:understanding the basic objects(e.g. cells)in one science(biology)in terms of the collective dynamics of objects(molecules) belonging to another science(chemistry).Moreover the mandate of complexity is touncover the determinism that hides behind the systematic and fateful recurrence in various sciences of seemingly fortuitous autocatalytic accidents.The conceptual and practical rewards for such a trans-disciplinary effort are inestimable." Ref: http://www.giacs.org/expertreport3

9. Roughly Three Regimes of Problems Law of Large Numbers e ~ ( n ) ** 1/2 MACRO 2) Unorganized Complexity (Aggregates) MESO 3) Organized Complexity (Systems) Quantity of Objects MICRO Law of Medium Numbers is… Murphy’s Law Randomness 1) Organized Simplicity (Machines) Combinatoric Exponential Explosion Complexity How many Types of Objects & Interactions? From: G.M. Weinberg, An Introduction to General Systems Thinking, John Wiley & Sons, New York, 1975, p 18.

10. Law of Large Numbers e ~ ( n ) ** 1/2 Algorithmic, homogeneous statistical models support this region (eg., Boltzmann for gases or Poisson telephone traffic) Legacy campaign simulations are misfit of algorithmic, homogeneous statistical models to represent complex adaptive systems 2) Unorganized Complexity (Aggregates) X 3) Organized Complexity (Systems) 1) Organized Simplicity (Machines) Randomness X Multi-Agent-Based interaction models support this region Combinatoric Exponential Explosion Complexity Equation-Based, deterministic science & engineering models support this region (eg., linked spreadsheets for concurrent engineering trades) Matching Analysis to Types of M&S From: G.M. Weinberg, An Introduction to General Systems Thinking, John Wiley & Sons, New York, 1975, p 18.

11. Limits of Equations: More Specifics “...there are also a number of quite concrete limitations to mathematical representation…. The difficulties of such a representation fall into two complementary classes: those caused by an unrealistic treatment of time and those resulting from an attempt to represent multiple agency as an ordered sequence of individual actions. These difficulties are complementary because the unrealistic treatment of time is both a consequence and a partial cause of the unrealistic treatment of multiple agency…. The modeler who arranges an equation system to guarantee its solubility does so because he or she must solve it sequentially, it is not feasible for certain processes to be carried out ``in the background'' or for the actions of several agents to be revised at once. Thus only one agent can act at a time in such models. Everyone else must freeze while this action is taking place. The richness of the environment is thus restricted to suit the attention of the modeler. This is plainly unrealistic.” Edmund Chattoe , “Why Are We Simulating Anyway? Some Answers from Economics,” ESRC Project L122-251-013, Nov 95 http://www.sociology.ox.ac.uk/people/chattoe.html

12. Equation-based Models in Social Sciences are… …frequently the tools of charlatans. “…in economics, and the social sciences, engineering has been the science of misplaced and misdirected concreteness.Perhaps old J.M. Keynes had the insight of the problem when he wrote:‘To convert a model into a quantitative formula is to destroy its usefulness as an instrument of thought.’ ….Marshall, Allais and Coase used the term charlatanism to describe the concealment of a poor understanding of economics with mathematical smoke. Philosophers of science used the designation charlatanism in a the context of a theory that does not lend itself to falsification (Popper) or gradual corroboration (the Bayesians).” Against Value-at-Risk: Nassim Taleb Replies to Philippe Jorion, 1997. http://www.fooledbyrandomness.com/jorion.html

13. Interactive Models more Powerful than Algorithmic • “The irreducibility of object behavior to that of algorithms has radical consequences for both the theory and the practice of computing…. • The negative result that interaction cannot be modeled by algorithms leads to positive principles of interactive modeling by interface constraints that support partial descriptions of interactive systems whose complete behavior is inherently unspecifiable. The unspecifiability of complete behavior for interactive systems is a computational analog of Goedel incompleteness for the integers…. • "Incompleteness is a key to expressing richer behavior shared by empirical models of physics and the natural sciences. Interaction machines have the behavioral power of empirical systems, providing a precise characterization of empirical computer science.” Peter Wegner, OOPSLA'95 Tutorial http://www.cs.brown.edu/people/pw/

14. Legacy M&S (Equation- based animation) Controller MOEs & MOOs Coupled Diff Eq Plant Sensor Weight Factors Environ Factor Numb. of Wpns Numb. of Wpns Sensor Weight Factors Agent-based Simulation Decentralized Control Perturbation by Environment Sensor Feedback Conflict • Blue Goals • Strategic • Theater • Tactical MOEs & MOOs Plant • Red Goals • Strategic • Theater • Tactical Sensor Feedback Control Theory View of Conflict

15. Math Modeling Resources (J. Doyle) • Networks of distributed sensing, computation, comms, and actuation will depend on all: • Thermodynamics (Carnot) • Communications (Shannon) • Control (Bode) • Computation (Turing/Gödel, • Of these, only control addresses dynamics, latency, and real-time issues • Claim: control must be the foundation for any network capacity theory that deals with real time • Focus initially on integrating comms and controls Rhw amendments P. Wegner) • Cognition (Peirce, von Foerster, Kahneman, Schelling, Taleb, Hawkins, ) --True only if cognition is regarded a subset of “Control”

16. This tradeoff is a law: Biological complexity is dominated by evolution of mechanisms to more finely tune this robustness/fragility tradeoff. log|S |  Robust Yet fragile Stabilizer • Benefits • attenuate disturbance • as negative as possible • Costs • amplifies high freq disturb • as small as possible Robustness—Fragility Trade-off (J. Doyle)

17. Critique of NRC Study on Defense M,S,&A Constructive-- • Recommendation 4: DoD should establish a comprehensive and systematic approach for developing the MS&A capabilities to represent network-centric operations: • Enhance and sustain collaborations among the various parties developing network-centric MS&A capabilities “…the committee found little evidence of significant interaction and cross-fertilization across the application communities ….collaboration might be facilitated by a DoD-sponsored series of workshops…leading to a…report synthesizing the views of the different communities and identifying opportunities for cross-fertilization.” • Continue and extend the development of existing approaches to modeling network-centric operation. “Since the basic architecture and functioning of traditional models reflect a pre-network perspective on military operations, those models are not adequate…. Attention should be given to the use of complex agents with sizable rule sets governing behavior to provide quantitative models and to the continued coupling of agent-based models with the techniques of dynamic network analysis….” • Establish a new mathematical basis for models describing network-centric operation, drawing on an array of approaches, particularly complex, adaptive systems research.

18. Critique of NRC Study on Defense M,S,&A Misguided & Subject to Misinterpretation— “Exploratory analysis is arguably best accomplished with a good aggregate-level model that can cover the entire possibility space clearly, albeit at low resolution. Such a model might have 6 to 10 variables….If one does such a synoptic exploration and finds that only two or three of the variables are particularly important, then with MRM or a suitable family of models, one can zoom to higher resolution on those variables.”

19. How Will M-ABMs Improve System Design? • Ability to address & apply Wohlstetter’s “Opposed System Design Theory” for systems with goal conflicts in quantitative simulation • Explicitly represent C2 system design & policy (CONOPS) factors & integrate with physical system engineering • Factor in aspects of near real-time situation awareness in context of scenarios with asymmetric, adaptive opponents • Allows Exploratory Analysis as a form of “stochastic engineering” (see N. Taleb in The Edge World Question Center) to produce more sustainable/adaptable systems & address the “Robust yet Fragile” conundrum at lower cost by showing how far adaptive C2 will allow relaxing constraints or MOP levels on other high-cost system elements (eg., comm bandwidth)

20. Nassim Taleb’s Vision for “Stochastic Science” “Rigorous reasoning applies less to the planning than to the selection of what works. I also call these discoveries positive "Black Swans": you can't predict them but you know where they can come from and you know how they will affect you. My optimism in these domains comes from both the continuous increase in the rate of trial and error and the increase in uncertainty and general unpredictability. The world is giving us more "cheap options", and options benefit principally from uncertainty…. Butif the success rate is very low, the more we search, the more likely we are to find things "by accident", outside the original plan— or the more an unspecified original "plan" is likely to succeed….I see the sign of fractal randomness in these payoffs from the fact that results are more linear to the number of investments than they are to quantities invested — thus favoring the multiplication of small bets. All the while institutional science is largely driven by causal certainties, or the illusion of the ability to grasp these certainties; stochastic tinkering does not have easy acceptance. Yet we are increasingly learning to practice it without knowing — thanks to overconfident entrepreneurs, naive investors, greedy investment bankers, and aggressive venture capitalists brought together by the free-market system. I am also optimistic that the academy is losing its power and ability to put knowledge in straightjackets and more out-of-the-box knowledge will be generated Wiki-style.” Nassim Taleb, “The Birth of Stochastic Science”, in The Edge World Question Center, 2007 http://edge.org/q2007/q07_5.html#taleb

21. What this Workshop Can Do Microscale • Instigate new interactions & persistent collaboration among individuals Mesoscale • Provide new guidance for Community of Practice • Share materials to educate management on Complexity Science via website • Propose new collaborative R&D projects (eg. ) • Plan successor workshop events with narrower focus that varies annually Macroscale • Platform for organizing response to Europe’s GIACS (General Integration of the Applications of Complexity in Science) & growing a global collaboration language for design of Complex Systems • Propose professional society recommendation for Chief Simulation Officer of each engineering corporation above 1000 employees • Follow-up on the more compelling recommendations of NRC Report:Defense Modeling, Simulation & Analysis: Meeting the Challenge

22. Second Mover Contribution • First movers generally sacrifice peripheral vision in favor of focus & drive • Building intellectual capital & infrastructure for architecture design involves collaboration & integration of “best of breed” concepts & language that gain dominant “mindshare” of the technical community (eg., why we use Leibnitz notation for calculus rather than Newton’s & VHS rather than Betamax format for video tape) • Those who follow also serve…

23. Backups

24. Overcoming Fear of Modeling: 3 Stages • Each has own undocumented or unconscious “mental models” - Concept design arguments based on often conflicting assumptions no context for resolving conflict • Each has own software models - Quantitative outputs based on assumptions & algorithms invisible to all but model developer no std models--little context for resolving conflict • Analysts & Decisionmakers use common software models - Shared experience of running models with assumptions, algorithms & data bases visible to all Barrier 1 Barrier 2

25. Co-evolution (re: Brooks Turing Lecture-99) • Model of co-evolution from Maher & Cross • The effective problem space evolves as the solution space evolves by being explored. P1 P2 PROBLEM THREAD S1 S2 SOLUTION THREAD

26. “Outside-In” Mental Modeling By far the most common way to deal with something new is by trying to relate the novelty to what is familiar…: we think in terms of analogies and metaphors. The only feasible way of coming to grips with really radical novelty is orthogonal to the common way of understanding: it consists in consciously trying not to relate the phenomenon to what is familiar from one’s accidental past, but approach it with a blank mind and to appreciate it for its internal structure. The latter way of understanding is far less popular that the former one, as it requires hard thinking. (And as Bertrand Russell has pointed out, “Many people would sooner die than think—in fact they do.”) It is beyond the abilities of those—and they form the majority—for whom continuous evolution is the only paradigm of history: unable to cope with discontinuity, they cannot see it and will deny it when faced with it.” Edsger W. Dijkstra, Mathematicians & Computing Scientists: The Cultural Gap, ABACUS, vol. 4 no. 4, Summer 1987.

27. Interactionist Approach to Architecture & Design “When computer chips outnumber humans on this earth…their mediation can fundamentally alter how people interact. Engineers, psychologists, ethnographers, architects, and cultural geographers have only begun to grasp the consequences of all this mediation….Much of what has passed for design has been an unconstrained accumulation of features, or at best, interfaces for measurable first-time usability. The new field of interaction design raises this work to a cultural level. As the study not only of how people deal with technology, but also how people deal with each other through technology, interaction design brings notions of premise, appropriateness, and appreciation to the conception of digital systems. The more that pervasive computing challenges designers to bring such notions to physical contexts, the more interaction design shares with architecture…. pervasive computing challenges us to re-express all that we value most about embodiment in persistent structures….Now architecture incorporates interactivity; and increasingly, interaction design affects architectural experience. Malcolm McCullough, Visiting Associate Professor, School of Architecture & School of Design, Carnegie Mellon University http://www-personal.umich.edu/~mmmc/# Interactionist approach to Design is another trend supporting multi-agent based simulation

28. a priori vs a posteriori Aggregation • System Dynamics Models (SDM)--mature, best for pure physics with homogeneous elements • Uses equations which represent observables averaged over time & space • Architecture follows equations & simplest way to model flow rates & levels • No explicit model of spatial relationships with ODE, can do with PDEs but then no way to differentiate between physical space & network topology • Not good for behavioral discontinuities • Assumes homogeneity at the entity level • Single level of aggregated detail & validation, no “generative” or “atomic” behavior • Multi Agent-Based Model (MA-BM)--new, best for cases of heterogeneous elements and human C2/policy issues • Begins with object/agent behavior rules governing interactions and aggregate observables “emerge” (multi-resolution model) • Natural modularity follows the types of objects (real world analog) • Can distinguish between physical space & interaction topology • Handles large heterogeneity of objects • Behavioral validation at both object and aggregate levels Refs: http://www.erim.org/cec/projects/dasch.htm

29. Attributes of Major M&S Types System Dynamics Models (SDM/EBM) --Macro/aggregate observables generated by equations Multi-Agent-Based Models (M-ABM) --Agent states change via local interaction rules ( includes but not limited to equations ) NON-Isomorphic Model Partially Isomorphic Model Population of AGENTS & Interaction Rules MICRO INPUT MACRO INPUT Equations MACRO OUTPUT aggregate data (m1 , m2 , …)t1 , t2 , … Output agent histories (s1 , s2 , …)t1 , t2 , … MACRO OUTPUT Data Analysis of Aggregate Observables limited to theory implied by generative equations Analyst aggregates micro-level, Agent Observables into macro-level Populations Descriptive only--does not allow “emergent” effects nor help understand their causal mechanism Possible to discover of macro-level Effects which can be explained by micro-level Causes Parunak, Savit & Riolo. “Agent-Based Modeling vs. Equation-Based Modeling: A Case Study and Users’ Guide,” presented at Modeling Agent Based Systems, 1998. http://www.erim.org/~vparunak/papers.htm

30. Simulations as Generative or Descriptive “Instead of being restricted to representing mathematical models of social processes, there is no reason why simulation should not enable us to represent the processes themselves. It seems appropriate to refer to simulations of this sort as generative and contrast them with the process of instrumental simulation discussed at the beginning of this section.” Edmund Chattoe , “Why Are We Simulating Anyway? Some Answers from Economics,” ESRC Project L122-251-013, Nov 95 Multi- Resolution

31. Science Limited by Infrastructure of Eq.s Q: “What’s the Story behind this new kind of science?” A: “Around 1980, I had become interested in several really different questions—galaxy formation and how brains work….the real problem was with the basic infrastructure of science. For about 300 years, most of science has been dominated by…using mathematical equations to model nature. That worked really well for Newton…but it’s never really worked with more complicated phenomena in physics….in biology it’s been pretty hopeless.” Q: “If equations aren’t the right infrastructure for modeling the world, what is?” A: “Simple programs….systems in nature had better follow definite rules. But why should those rules be based on the constructs of human mathematics?….now you can think of them as being like computer programs.” Stephen Wolfram, Interview in “New Scientist.com” http://www.newscientist.com/opinion/opinterview.jsp?id=ns230516

32. Open questions (J. Doyle) Nonlinear/uncertain hybrid/stochastic etc. Complex networked systems ? Complexity of dynamics Single Agent ? Flocking/synchronization consensus Multi-agent systems Complexity of interconnection

33. Disturbance - e=d-u d Remote Sensor Plant Control Channel Sensor Channel Control Encode Control Model Integrates Bode & Shannon (J. Doyle) Should also include data fusion/cognition--rhw Nuno C Martins and Munther A Dahleh, Feedback Control in the Presence of Noisy Channels: “Bode-Like” Fundamental Limitations of Performance. (Submitted to the IEEE Transactions on Automatic Control) Abridged version in ACC 2005 Fundamental Limitations of Disturbance Attenuation in the Presence of Side Information Nuno C. Martins, Munther A. Dahleh and John C. Doyle (Submitted to the IEEE Transactions on Automatic Control) Abridged version in CDC 2005 http://www.glue.umd.edu/~nmartins/

34. Van Riper LGen (ret) Msg to CJCS Dec 2005 "Systems can be complex based on the numbers of elements they have:the greater the number of elements, the greater the complexity.  This is structural complexity.  Systems can also be complex in the ways that their elements interact:the greater the degrees of freedom of each element, the greater the complexity….Of the two, the latter can generate greater levels of complexity -- by orders of magnitude…. • Within interactively complex systems it is usually extremely difficult, if not impossible, toisolate individual cases and their effects…. • Reductive analysis will not work with such systems:  the very act of decomposing the system changes the dynamics of the system… • Most social systems, such as economies, governments, diplomacy, culture, and war, exhibit rich interactive complexity."