1

Evolution of 21sdt Century “Killer” Risk: Terrorism, Transnational Threats & Complexity

Society for Risk Analysis -- 2007 John A. Marke

ctc9808@aol.com636-458-1917

2

The Song of the Lakota Sioux Kit Fox Society

I am the Kit FoxI live in uncertaintyIf there is anything difficultIf there is anything dangerous to doThat is mine

3

Threats

• Terrorism

• Organized crime

• Infectious disease

• Corruption

• Import safety/food safety

• Financial market meltdown

• Natural disasters

• Industrial disasters

• WMD

• 4th generation warfare

These threats are not “new” but certain catalysts have ratcheted up the vulnerability and consequences to make them look like threats on steroids.

We are all more connected and inter-dependent than ever before.

4

Evolution of Risk

Characteristics of 21 Century Emerging Risk

From… To- National to Transnational…..changes constitutional relationships- Tactical to Strategic…..magnifies incidents into crises- Linear to Non-Linear….magnifies consequences, changes scope of responses - Predictable to Indeterminate….obsoletes certain methods & tools - Epidemic to Pandemic….changes scope of crises, nature of threat- Conventional to WMDs…changes scope of crises, nature of threat- Stable to Unstable Climate...changes scope of crises, nature of threat- Stable to Cascading….changes scope of crises/response- Independent to Interdependent….changes scope of crises/response- Unconnected to Inter-connected….changes scope of crises/response- Transparent to Opaque…changes scope of crises/response- Near Real Time to Real Time….reduces options, increases vulnerability- Nation-State to Non-Nation State Actor….changes portfolio of responses

5

Primary Catalysts

Fall of USSR Moore’s Law Metcalfe’s Law Gilder’s LawClimate Change

Why?

Outcomes/Secondary Catalysts

- Proliferation of WMD’s- Global Trade- 24 X 7 global communications- Emergence of PRC as a capital power- Emergence of India as a capital power- Shift from hierarchical to network organization- Environmental “surprises”- Global financial system- Destabilization of nation-state system

6

Significance of a Changing Environment?

Uncertainty – Magnifies “the problem of induction” as new variables are introduced to the problem mix making historical data (problematic under the best of circumstances) irrelevant.

Complexity – can be measured in a number of ways, e.g. the amount of code and/or how long it takes for an algorithm to execute, to the number of components in a system (static complexity) to the number of ways components can be linked (dynamic complexity). And we have just scratched the surface

7

High

Low

Deterministic

ModeratelyStochastic

Stochastic

IndeterminateU

nce

rtai

nty

Defining Uncertainty

8

High

Low

For any given set of starting conditions, here is only one event which may be assigned a significant probability of occurrence. There is no randomness.

Examples: In the physical sciences Newtown’s Laws of Motion. Also, some schools of Islamic thought dictate that the world is determined.

Deterministic

Defining Uncertainty --- Deterministic

9

High

Low

For any given set of starting conditions, a limited number of qualitatively similar events must be assigned significant probabilities of occurrence. System structural and relational properties are, for the most part, invariant.

Examples: Sales forecasts in stable industries, demographics, actuarial science.

Deterministic

Defining Uncertainty --- Moderately Stochastic

ModeratelyStochastic

10

High

Low

For any given set of starting conditions, a large number of qualitatively different events may be assigned significantly high probability of occurrence. Examples: Kentucky Derby, Republican and Democratic Primaries.

Deterministic

Defining Uncertainty --- Stochastic

ModeratelyStochastic

Stochastic

11

High

Low

For any given set of starting conditions, there is no event that can be assigned a significant probability of occurrence; thus the high probability that some outcome that we have not been able to specify will occur.

Causal and structural properties are either empirically inaccessible or unallegorizable; state change is independent of prior state. Examples: “Black swans,” opportunistic phenomena, terrorism, phenomena of open/complex systems

Deterministic

Defining Uncertainty --- Indeterminate

ModeratelyStochastic

Stochastic

Indeterminate

12

Defining Uncertainty ---

High

Low

Deterministic

ModeratelyStochastic

Stochastic

Indeterminate

Generates a single solution for any given set of predicates, i.e. linear programming and optimization

Generates a range of possible solutions or single estimates indexed with a probability of accuracy, i.e. predict-then-act

For any set of predicates it will generate an array of cause-effect alternatives, contingency and game-based

Generation of heuristics or learning-based paradigms

13

Defining Uncertainty ---

Deterministic Moderately Stochastic IndeterminateModel Type Stochastic

Optimization

Predict-Then-Act

Contingency

Heuristic

Ineffective due to too little

resolution power

Ineffective use of analytical

resources

Instrumentation – Best Fit

Source: Sutherland, p 157

14

Tool/Instrument Nature of the Solution Level of Uncertainty

Optimize, linear programming and cost/benefit analysis

Generates a single, best fit, solution

Deterministic, there is no uncertainty

Classic decision theory. Projects a likely set of solutions or a point solution with a certain confidence level

Based on a probability and sustained by statistical data

Moderately stochastic, some random behavior

Bayesian statistics Based on subjective probabilities

More stochastic, little reliable data on which to base a decision

Strategic flexibility, options theory, hedging, simulation

Fluid, contingent, not data dependent

Severely stochastic

Heuristic – Rule of Thumb

Keep afloat until we can learn more and make intelligent decisions

Indeterminate

15

Defining Complexity --- Systems Properties

Complexity refers to 3 systems properties to consider:

1) The number of irreducible component subsystems of the processes

2) The how the components are connected to form the system, called structural or static complexity (a Boeing 747 exhibits high static complexity but relatively low dynamical complexity)

3) The dynamical complexity around how the system behaves over time (ecological systems exhibit high dynamical complexity as the adapt and change over time).

16

Static Complexity Dynamical complexity

Defining Complexity --- Systems Properties

• Are components put together in an intricate way?

• What is the hierarchical structure? Often the number of hierarchical levels represents a rough measure of complexity

• What is the connective patter?

• Closed systems, enthropatic

• Decomposable

• Do parts of the system change at different time frequencies and scales?

• Static complexity influences dynamical complexity, but the reverse is not true, i.e. a system with low static complexity may behave in a highly complex, dynamical way.

• The more complex the more difficult to predict behavior.

• Open systems

• Non-decomposable

17

Implications for Management Sciences? - J. Rosenhead 1989

Rosenhead, J. (1989) ‘Introduction: old and new paradigms of analysis’ in Rosenhead, J. (ed) Rational Analysis for a Problematic World: Problem structuring methods for complexity, uncertainty and conflict, Chichester, Wiley & Sons.

18

“If I hold a rock, but want it to change, to be over there, I can simply throw it. Knowing the weight of the rock, the speed at which it leaves my hand, and a few other variables, I can reliably predict both the path and the landing place of a rock. But what happens if I substitute a [live] bird? Knowing the weight of a bird and the speed of launch tells me nothing really about where the bird will land. No matter how much analysis I do in developing the launch plan … the bird will follow the path it chooses and land where it wants”. Attributed to Richard Dawkins (Plsek, 2001): ‘‘Why won’t the NHS do as it is told?” Plenary Address, NHS Confederation Conference, 6 July 2001 as cited in System failureWhy governments must learn to think differently ,http://www.idea.gov.uk/idk/aio/5770191

“Under the most rigorously controlled conditions of pressure, temperature, volume, humidity, and other variables the organism will do as it damn well pleases.” The Harvard Law, cited in http://www.murphys-laws.com/murphy/murphy-technology.html

Examples – Uncertainty Meets Complexity?

19

• There is no definitive formulation of a wicked problem.

• Solutions to wicked problems are not true-or-false, but better or worse.

• There is no immediate and no ultimate test of a solution to a wicked problem.

• Every solution to a wicked problem is a "one-shot operation"; because there is no opportunity to learn by trial-and-error, every attempt counts significantly.

• Every wicked problem is essentially unique.

• Every wicked problem can be considered to be a symptom of another problem.

See: Jeff Conklin, Wicked Problems & Social Complexity, Wiley, 2006; and

Examples?• The lower 9th Ward in New Orleans• Global warming• 4th Generation Warfare, e.g. Iraq, Vietnam• Terrorism

Wicked Problems

20

The Most Perfect Example of a Complex-Adaptive-System

• The whole is not only greater than the sum of the parts, it is different from the sum of the parts.

• A true adaptive agent – it is all about learning

• Defines the word “surprise.”

• Exhibits emergent properties – everyday it seems!

• Highly connected and interdependent.

• Exhibits self-organized criticality, e.g., meltdowns.

• Not random but also not predictable

• She exists in that “interesting space” between “simple” and “chaotic” i.e. the complex.

21

Instrumentation?

Reductionist, deterministic• Cost/benefit analysis• Linear Programming• Economic Analysis/Financial Analysis• Classic physics• Planning, Programming & Budgeting

Reductionist, non-deterministic• IDEF Methodologies

Holistic • Network Theory• Complex Adaptive Systems• Boydian Loop

Ethnography/Participant Observation• Negotiation• Socio-psychology•

Heuristic• Capabilities-Based Planning• Hedging• Real Options• Simulation

Bayesian

Game Theoretic

Classic Statistics

?

22

Instrumentation?

High

Complexity

Hig

h

Uncertain Risk is absorbed or “dampened” by resilient designs, options theory, and hedging strategies capabilities-based planning.

Intractable - Wicked Risk is emerging and depends on negotiated meaning among stakeholders, highly political, discursive. Try to reduce complexity and uncertainty.

Complex Risk management is recursive and robust solutions are based on flexibility, “organizational learning,” and network theory. This is a net-centric world.

Low

?

Stable “Risk Is Optimized” and quantified, e.g. linear programming, cost-benefit, econometric and accounting theory

Un

cert

ain

ty

Low

IDEF decomposition methodology Static complexity is managed as subsystems are grouped into interrelated components and subjected to Integrated Definition Methodologies (IDEF) or other functional modeling techniques

Classic statistics – predict-then-act, stable data. environment

Multi-Dimensional Risk solutions are contingent and Bayesian

23

Assignment: Secure the homeland.

Your job: Financial steward…you write the checks

Focus: Critical transportation infrastructures, airports, bridges, etc.

Begs questions?

• Uncertainty? Fat tailed threat – low probability/.high consequence Falls into the “Indeterminate” range.

• Complexity? • Number? Probably under 500• Static? Moderate• Dynamical? High

Case: Infrastructure – Asset Management

24

25

Alton Lock & Dam – A Dynamic, Network-Based Problem

• Destroy Alton and all river traffic north of St. Louis stops• Changes values of all other nodes!

Concepts?

Transportation network in the USA is a “scale free: network. • Some nodes are more highly connected than others.• Structure and dynamics are independent of the number of nodes, i.e. the network works the same regardless of the size, hence scale free.• Degree distribution, i.e. how many links or edges a node has, follows a power law relationship where the probability P(k) of a node connecting to other nodes is proportional to k-y. “y” is a coefficient that, for most real world networks, will vary between 2 or 3.

Data and Models?Oak Ridge National Labs

26

“Quantification lifts up and preserves those aspects of a phenomena which can most easily be controlled and communicated to other specialists. Further, it imposes order on hazy thinking by banishing unique attributes from consideration and reconfiguring what is difficult or obscure such that it fits the standardized model.”

In short – anything but measurable, quantifiable outcomes were irrelevant. Further, there was an economically rational linkage between inputs (e.g. soldiers, weapons systems) of war and the outputs (e.g. dead VC). US leaders thought they could predict the probability of and timing of victory based on the numbers of US troops in Vietnam.

success/inconclusive/collapseFor the year 1966: .2 .7 .1For the year 1967: .4 .45 .15For the year 1968: .5 .3 .2

(source: The Pentagon Papers, Vol.III, p. 484 and “Contradictions Between Representation and Reality: Planning, Programming and Budgeting and the Vietnam War,” Michele Chawstaik, U. of New Mexico, 2001).

An Accountant’s View of War – Too Many Paid The Price

Robert McNamara, advocated an efficiency-based brand of “systems analysis” called Planning Programming, & Budgeting. His staff focused on qualities that could be quantified.

“Truth” was equated with that which could be counted. As a result, the options most reducible to quantification became the ones that received the most attention in Vietnam. Grrrr!

27

McNamara pursued a war fighting paradigm where the United States fought a war of attrition against an enemy that (we assumed) shared a propensity for economic rationality. Sadly that was not the case. It wasn’t then and it most certainly isn’t now.

Remember…………….

1. Never fight a “war of attrition” if the enemy has more 18 to 25 year old males than you.

2. War is never “rational” because you are asking men to trade their lives for an ideal.

3 Stay out of the red circle!!!

War, Like Terrorism, Is Never Rational

28

• Total KIA: 58,156

• Total WIA: 303,704

• MIA: 2,338

“He who forgets the past is condemned to repeat it." George Santayana

29

References

Uncertainty and epistemology: A General Systems Philosophy for the Social and Behavioral Sciences, John W. Sutherland, 1973. Sutherland is brilliant but not easy, but worthwhile things rarely are. The uncertainty slides are his work. I merely connected a few of the “dots.”

Complexity: Drawn from John Casti’s Connectivity, Complexity and Catastrophe in Large Scale Systems, published by the International Institute for Applied Systems Analysis, 1979. Casti sets some of the basic definitions for static and dynamic complexity.

21St Century Challenges: Phillip Bobbitt’s Shield of Achilles.

Networks: Take your choice, lots out there.

Further contact: You are welcome to call me at 636-458-1917 or e-mail me at CTC9808@AOL.COM to further discuss any of this stuff. I will also be happy to send you a copy of the slides.

30

John Marke has over 28 years experience in consulting to government and civilian clients. He is a former naval intelligence officer with a background in unconventional warfare, and was also a special agent with the NCIS. John has a BA in Political Science from Tulane, and MBA from Golden Gate, and PhD (abd) from St. Louis University. He held senior positions with Deloitte, BearingPoint, DiamondCluster, Logica. His clients included ENRON (before the fall), Harvard University, AEP, GoldmanSachs, Bracewell & Giuliani, LLP, and various federal agencies. He also advised the Giuliani Campaign on risk and homeland security issues.

“Mickey” is John’s “Karelian Bear Dog” and inspiration for the logo. Karelian’s are bred to annoy (not attack) bears with their high pitched and incessant barking driving them away. No bears have been reported in the vicinity of his Missouri farm – so far, so good.