There are many reasons why the broad-brush
A New Set of Tools
This methodology doesn’t
rely on one silver bullet that
works in some cases but
not in others. It employs
innovations from many
different fields in a way
that complements existing tools in the study of
Ultimately, this approach
tells a story unique to your
organization about where
the latent risk is lurking, before statistical methods can
Agent-based modeling approach
Philosophically, the 20th century was the
era of optimization. It was the age of Homo economicus, the fabled rational economic agent who makes decisions based on what would maximize some ethereal benefit
function. These methods have sometimes provided useful approximations; however, the drawbacks of classical economics
are attracting increasing attention from social scientists and
practitioners alike (See “Rethinking Rationality,” Page 26).
There are many reasons why the broad-brush assumptions of
classical analysis have often failed.
It may be that the incentives of managers or employees are
not exactly aligned with what’s best for the organization. Alternatively, realistic optimization of day-to-day decisions may be
deceptively complicated. If the external environment is changing
quickly, yesterday’s optimal decision may be redundant today.
Also, the human brain is not very good at subjectively evaluating risk. An individual’s subjective experience with risky
situations biases his response to new situations. To complicate
matters, risk preferences change over time at different rates
with different individuals.
Even when the decision framework is fairly straightforward,
people might not have the right information at the right time.
People tend to form simpler decision rules that are intended to
approximate optimized behavior over time according to trial
and error. Often these decision rules closely resemble what
Homo economicus would do. But often enough, they don’t.
This observation has given rise to an approach known as
agent-based modeling. Simply put, it’s fairly easy to write little
bits of computer code, each with its own simple decision rules,
and put many of them together in a program to interact. Such
models enable relatively simple assumptions about human behaviors to combine and generate complex resulting behaviors
that are both more intuitive and more closely aligned to ob-
served human behavior. In other words, even when individuals
have well-defined roles and try to do their jobs in good faith,
risk can arise when, in the multiplicity of interactions, slight
miscommunications and mistakes are amplified.
A corporate culture can be seen as the amalgamation of these
individual decision rules. Because they can create so much risk,
it’s valuable to understand the unique decision rules followed
by people in your company. A second approach to behavioral
risk management closely resembles an in-house focus group
with live clients (in this case, your employees).
Each employee interacts with the corporate environment
and forms his or her own personal understanding of it. But
rarely, if ever, do employees combine perspectives in a way
that connects their perception of their respective roles with
the intended direction of the company. Often this combination
of perspectives reveals missing or misaligned elements in the
implementation of strategic objectives.
Consultants will conduct a series of telephone interviews or
personal meetings to gauge the psychology and attitudes of different employees. The questions may be open-ended and range
from business strategy to the many factors that may hinder the
achievability of this strategy, but they are designed to flesh out
the perspectives that employees have toward their interactions
with other employees or with clients. Often this can be accomplished with simply one or two dozen people from different
functions within the business.
By combining these internal perspectives, you can use tools
such as cognitive mapping to create a visualization of the risk
exposure of your organization. The best of these products includes an analysis of these maps, which can unveil potential
flash points where the group is collectively overlooking an important factor. At one extreme, the group may miss internal
risks, and at the other, managers may be too focused on optimizing internal functions to consider how changing external
conditions may affect their strategy.
Although behavioral risk management might be called a psy-chology-based theory of risk, not all of its tools rely on peeking
inside the human mind to tell the story of complex interactions.
Forecasts for complex and adaptive systems like the weather
cannot remain accurate too far ahead, but the ability to spot
emerging patterns and to make well-informed assessments of
likely outcomes is proven ground. Some of the tools developed
in the complexity sciences are also effective in describing the
stability of social and economic systems.
Complexity measures can be constructed that offer a barometer of organizational stability, much like financial market
metrics but unique to your company. The data sources might