Palisade Risk Management Conference
Las Vegas
November 10-11, 2011
Christina I Ray
Senior Managing Director for Market Intelligence
Omnis, Inc.
McLean, VA
DECISION MAKING AND ENTERPRISE RISK
MANAGEMENT:
THE PLAUSIBLE VERSUS THE PROBABLE
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Two Worldviews:
Probability vs. Plausibility
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Two Worldviews
 Probability
The future is adequately represented by the past
 Plausibility
The future my be very unlike the past
Everything that can be reasonably be
expected to occur hasn’t yet happened!
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Two Worldviews (continued)
 Probability
Easier to model using classical statistical
models (e.g., using variance/covariance)
 Plausibility
More difficult to model; requires use of causal
models and subject matter expertise
But the latter has a high payoff!
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Inductive Reasoning and the Scientific Method
 The classical view of the
philosophy of science is that it
is the goal of science to "prove”
hypotheses or induce them
from observational data.
 But, consistent with a Bayesian
attitude, we can only have a
degree of belief in, say, “all
swans are white”.
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Popper’s Falsifiability
 Scientific hypotheses have
to be falsifiable (i.e.,
refutable)
 This doesn’t mean that
something is false; rather,
that it can be contradicted
by an observation or the
results of a direct
experiment (e.g., we
observe a black swan).
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Bayesians vs. Frequentists
 Focus on “plausibility”
 Focus on “probability”
 Explicitly acknowledge
 Implicitly assume a
breaks in the system
 Each new piece of data
affects their degree of
belief (i.e., “prior”
becomes “posterior”
knowledge)
stable system
 Each new piece of data
modifies statistical
parameters and the level
of uncertainty about
them
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Knowledge Discovery in Brief
Data
Information
Knowledge
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Two Missions: Two Worldviews
 The financial community
 The national security
often assumes system
stability, in which historical
market behavior is assumed
to be representative of
future market behavior
 Its focus is on probability
 This worldview permits the
use of classical stochastic
models
community assumes
constantly-evolving threats
in the face of adaptive
enemies, and must generate
its own future scenarios
 Its focus is on plausibility
 This worldview requires the
use of causal inference
models
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Decision-Theoretic Models:
Choice Under Uncertainty
 Decision theory is concerned with identifying the
values, uncertainties, and all other issues relevant to
identifying an optimal decision.
 Most of decision theory is prescriptive; i.e., it is
concerned with identifying the best decision to take by
a decision maker who is fully informed, able to
compute with perfect accuracy, and is fully rational.
 However, it also allows for preferences for one
outcome over another in the form of marginal utility
functions
 Decision-theoretic models require decision support
systems and tools
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Objective of Analytic Methods (All Domains)
 Objective of any analytic methods: “actionable
insights” (irrespective of the domain)
 To do so, we must have an integrated structure
describing the observations we have gathered,
but must also specify a causal structure, so we
can anticipate the consequences of actions we
have not yet taken.
 One of the largest challenges: adding complexity
and computational requirements, since most
dynamics uncovered in some domains are
probabilistic rather than deterministic in nature.
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Enterprise Risk Management
A Fused Approach
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Ultimate Objective of ERM:
Inform Management Decisions!
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Purposes of an Integrated ERM Framework
 Knowledge Unification
 Knowledge Representation &
Communication
 Reasoning
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A Framework for Information Fusion:
Inference Models
• Ability to model complex causal chain of
events
• Ability to integrate expert knowledge,
historical experience, opinions, and even
unknown unknowns
• Ability to include utility functions/preferences
• Completeness
• Efficiency
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Mitigation of Cognitive Biases
 Confirmation Bias
 Anchoring Bias
 Random Bias
 Availability Bias
 Framing Bias
 Statistical Bias
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Comparison of Attitudes About Risk
Characteristic
National Security
Community
Financial Community
Purpose of Risk
Management
Defend homeland
Profit
Attitude Toward Risk
Always a negative; to be
eliminated or mitigated
Taken deliberately
(within limits)
Types of Models
Conceptual/Causal
Primarily Stochastic
“Bayesians”
“Frequentists”
Focus
Catastrophic scenarios
Everyday and extreme
scenarios
Data
Expert opinion informed
by intelligence
Historical experience
Attitude About Model
Error
Always aware of model
error; efforts to control
Often not an integral
part of risk management
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The US as Enterprise
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Vision 2015
Persistent Threats and Emerging Missions
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Economic and Financial Warfare
“So, which [of many unconventional means],
which seem totally unrelated to war, will
ultimately become the favored minions of this
new type of war – “the non-military war
operation” – which is being waged with greater
and greater frequency throughout the world?
…Financial War is a form of non-military warfare
which is just as terribly destructive as a bloody
war, but in which no blood is actually shed.
Financial warfare has now officially come to
war’s center stage.”
-- Colonel Qiao Liang ad Colonel Wang Xiangsui,
Unrestricted Warfare, 1999 (Translated from
Chinese)
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One “IC” Decision Support Framework
 Likelihood/Probability
 Susceptibility/Vulnerability
 Consequences/Impact
These are all oriented toward
providing actionable intelligence!
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Causality
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Causality
“I would rather discover one causal relationship than be King of
Persia.”
Democritus (430-380BC)
“Development of Western science is based on two great
achievements: the invention of the formal logical system (in
Euclidean geometry) by the Greek philosophers), and the
discovery of the possibility to find out causal relationships by
systematic experiment (during the Renaissance).”
Albert Einstein, April 23, 1953
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Causality as a Property of Nature, Not a
Model
 “It is not reason which is the
guide of life, but custom.”
--Philosopher David Hume
 “Reality is that which, when you
stop believing in it, doesn't go
away.”
--Science Fiction Writer Philip K. Dick
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Central Theme of Causality
 Causality is, ”a computational scheme
designed to facilitate prediction of the effects
of actions”.
 I&W provide notice of actions (i.e.,
interventions) that lead to consequences!
Result = Predictive Ability!
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Basic Principals of Causality
Mechanisms = Stable Functional
Relationships (Portrayed as Equations and
Graphs)
Interventions = “Surgeries” on Mechanisms
Causation = Encoding of Behavior Under
Interventions
These principals can be neatly encapsulated
and enclosed in a mathematical object
called a causal model.
Source: Judea Pearl
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Correlation vs. Causation
Y=aX+b
Provides no information about whether
X causes Y or Y causes X!
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Graphs: The Language of Causality
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One Notation: Directed Acyclic Graphs
Nodes =
Variables
Dotted Curves =
“Confounders”
(Hidden Causes
That Affect Both
Variables)
Links =
Relationship
Between
Variables
These might be
reverseengineered to
great advantage!
Directed Links
(Arrows) Show
Temporal or
Causal Order
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Statistical Inference vs. Causal
Inference
 Perhaps the most important aspect of a
Bayesian networks is that they are direct
representations of the world, not of
reasoning processes.
 The arrows in the diagram represent real
causal connections and not the flow of
information during reasoning (as in rulebased systems and neural networks).
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Operational Example: Dynamic VaR
Created using Palisade’s Precision Tree
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Contagion and Systemic Risk
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Systemic Risk and Systems Thinking
A system is defined as a set of elements
that have one or more relationships between
them, and systems thinking is the process
by which one seeks to understand those
elements and relationships so as to be able
to understand the behavior of the system as
a whole.
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Systems Representation of the
World Economy and Markets
Source: Quantum 4D, Inc.
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Characteristics of Some Systems
 Complex
 Adaptive
 Self-Organizing
 Exhibits Emergent Behavior
 HOT (Highly Optimized Tolerance)
Global capital markets exhibit all these
characteristics.
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Definition of Systemic Risk
“Financial system instability, potentially
catastrophic, caused or exacerbated by
idiosyncratic events or conditions in financial
intermediaries. It is the risk that the financial
system fails. It is not the risk that a financial
institution fails provided that failure does not
result in system instability.”
-- Tom Daula, Chief Risk Officer, Morgan Stanley
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Example of Contagion:
Credit Default Swaps in the “PIGS”
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Office of Financial Research (OFR)
 Mandated by Dodd Frank
 Established within the Treasury
Department
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OFR’s Mission
 Mission is to create two operational
centers:
 Data Center to, “standardize, validate, and
maintain the data necessary to identify
vulnerabilities in the system as a whole”, and
 Research and Analysis Center to, “conduct,
coordinate, and sponsor research to support and
improve regulation of financial firms and markets”.
This is explicit acknowledgement of regulations as
“interveners” in a complex system!
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Contact Information
Christina Ray
Senior Managing Director for Market Intelligence
Omnis, Inc.
www.omnisinc.com
1749 Old Meadow Road
4th Floor
McLean, VA 22102
cray@omnisinc.com
(703) 790-1011 x 108 (Office)
(917) 567-8355 (Mobile)
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