p.5 The field of judgment and decision making is mainly concerned
with action and with judgments that are useful in decision making about action. Thus the field should tell us a great
deal about practical reasoning.
p.77,78 Research suggests that people hardly ever make conscious decisions about which heuristic to use,
but that they quickly and unconsciously tend to adapt heuristics to changing environments, provided there is feedback (Payne
et al., 1993)... A good heuristic needs to be robust. Robustness is the ability to make predictions about
the future or new events
p.80 The general lesson is that in judgments under uncertainty, one has to ignore information in
order to make good predictions. The art is to ignore the right kind. Heuristics that promote simplicity, such
as using the best reason that allows one to make a decision and ignore the rest, have a good chance of focusing on the information
that generalizes.
p.112-113 A core idea of the information-processing approach is that conscious attention is the
scarce resource for decision makers (Simon, 1978). Thus, people are generally highly selective about what
information is attended to and how it is used. Understanding what drives selective attention in decision making
is a critical task for decision research... Importantly, people may be unaware that their attention has been focused
on certain aspects of the task environment, and that their decisions consequently have been influenced... If attention
is the scarce resource of the decision maker, then helping individuals manage attention is critical for improving decisions...
The distinction between the cost of processing an item of information and the cost of acquiring information is related to
the idea of attention as the scarce resource... An increase in the cognitive (or emotional) cost of processing an item of
information, like the cost of acquiring an item of information, will lead to greater use of simplification mechanisms that
minimize information processing.
p.120 if a decision maker wants to achieve both a reasonably high level of accuracy and low effort (by using
heuristics), he or she must use a repertoire (toolbox) of heuristic strategies, with selection contingent upon situational
demands.
p.220 The ability to accurately perceive relationships in the environment is an essential component
of adaptive behavior, as it allows powers of explanation, control, and prediction.
p.274 Forecasting involves making predictions about an unknown question or issue. My focus
here is on why uncertainty and complexity pose special challenges in forecasting and what to do about this... The
LH case (low uncertainty- high complexity) typically requires the formulation of a deterministic model in which the key variables
of interest are explicitly related to each other.
p.281 When constructing
models, in order to combine the multiple factors that matter, people often harbor simplistic notions about cause and effect
(Tversky & Kahneman, 1980; Einhorn & Hogarth, 1986). They may ignore feedback loops or secondary interactions (Sterman,
1989, 1999). Surprisingly, intuitive predictions involving multiple variables are usually outperformed by linear regression
models based on those very judgments (known as bootstrapping). Combining human and mechanical predictions typically beats
either alone (Blattberg & Hoch, 1990; Hoch, 1994, 2001). The challenge is to discover the valid component of intuitive
judgment amid the extensive noise that surrounds it (Whitecotton, Sanders, & Norris, 1998). Recent approaches in statistics
have tried to address the LH case [Low uncertainty - high complexity] using vector autoregression (Tiao, 2001).
p.283 Tools to Improve Prediction [section title] Low uncertainty- high complexity (LH case) In this case,
the number of variables involved, and their relationships, will require a more systematic analysis. LH cases are typically
amenable to some form of deterministic modeling. A simple approach would be to start with assumption analysis
(Mason & Mitroff, 1981) since this may identify gaps or distortions in the fabric of presumed interrelationship. Next,
an influence diagram could be constructed and/or an entire system dynamic model (see Sterman, 2000) to fully
appreciate causal linkages over time (Einhorn & Hogarth, 1986). Another approach to consider is
discovery-driven planning (McGarth & MacMillan, 1995), in which one works backward to see what assumptions
and interim achievements would be required to achieve a particular end state. This technique will also help identify key drivers
that should be monitored closely so that timely adjustments can be made in the forecasts if needed. In addition to these more
holistic approaches, more focused techniques exist to improve the quality of judgments when matters get complex. Reason generation,
role-playing, imagining unusual outcomes (outliers), comparisons against past cases, and panel techniques like Delphi polling
can all improve judgments about input variables or causal relationships (see Armstrong, 2001).
p.301,302 Experts can use their detailed mental models, coupled with their understanding of the
current state of the situation, to construct simulations of how the situation is going to develop in the future,
and thereby generate predictions and expectations... Experts spend relatively more time analyzing the situation than
deliberating about a course of action, whereas non-experts show the reverse trend.. The richer mental models of experts
enable them to identify atypicalities and therefore adjust the story they are developing to explain events... Expert
decision makers tend to use their mental models to fill gaps with assumptions, to mentally simulate and project into
the future, to formulate information seeking tactics.
p.486 Game theory is a branch of mathematics that provides a framework for modeling and predicting
behavior in social situations of cooperation, coordination, and conflict.
p.496 Strategic thinking in games requires players to form beliefs about what opponents will do.
