Biases in Decision Making
The Biases Paradigm
A widely accepted research paradigm in psychology (e.g., Kahneman, Slovic, and Tversky) has taken Bayesian decision theory as the standard by which reasoning is to be judged, and has identified pervasive patterns of error, called biases, in laboratory performance. According to these researchers, unaided decision processes employ rules of thumb (or "heuristics") that under many (but not all) conditions lead to "severe and systematic errors"

Evaluating Decisions and Decision Making

CTI has investigated decision-making errors in a variety of real-world contexts, such as submarine command decision making, Air Force in-flight route replanning, and Army battlefield command staff.planning. This work has been accompanied by a thorough review and analysis of psychological research on decision making, and by normative research on uncertainty, inference, and belief revision. The result is a view of biases based more solidly on both data and theory.

The point of view we take is naturalistic, that is, rooted in study of the processes used by real-world decision makers and their real-world outcomes. From this point of view, formal consistency (e.g., with Bayesian probability theory) is only one among the factors that should be considered in evaluating a decision making process. For example:

  1. Effort versus accuracy in strategy choice: Do decision makers select strategies that appropriately balance accuracy against time and effort?
  2. Cost and benefits of thinking more: Do decision makers appropriately determine how long to keep options open versus committing to action, as a function of the dynamic character of the environment, the costs of delay, and the opportunity to obtain new information? (This is the control process we call the Quick Test in the Recognition / Metacognition model.)
  3. Strategy monitoring: Do decision makers monitor the use of a strategy to guard against its potential shortcomings in specific contexts? (This is the critiquing process identified in the Recognition / Metacognition model.)
  4. Incremental improvement: Do decision makers take opportunties to incrementally improve their understanding of relevant beliefs, preferences, and options as a problem evolves? (This is the correcting process in the Recognition / Metacognition model.)
  5. Do decision makers approriately evaluate their own knowledge or expertise in a domain?
  6. Do decision makers persist in holding to a belief or decision after a significant accumulation of negative reasons, and in the presence of a better alternative? (This is part of the process of building and evaluating mental models.)
  7. Do decision makers abandon a belief or decision too readily in the face of negative reasons that can be explained relatively easily, or in the absence of a better alternative?

The Confirmation Bias

Points 6 and 7 bear on the confirmation bias, a tendency to interpret negative evidence as favoring a preferred hypothesis, or mental model. Contrary to much of the literature, this tendency to explain away inconsistent evidence is NOT necessarily an error. In many situations that we have observed, no familiar pattern fits all the evidence; there are data that conflict with every plausible hypothesis. The traditional view of the confirmation bias either paralyzes decision makers or forces them to settle for an unrealizable statistical average of the possibilities.

Our research suggests that experienced decision makers try to make sense of conflicting data by constructing coherent stories, i.e., by revising and improving their mental models through a process of critiquing and correcting. They then step back and evaluate the plausibility of the story. If it requires too many unreliable assumptions, they try to construct a different story, step back to evaluate it, and so on. This iterative process is part of what we set out to capture in the Recognition / Metacognition model of critical thinking about mental models. It is also reflected in our normative work on assumption-based reasoning and connectionist models that integrate relfexive and reflective reasoning.

Finally, this more dynamic and adaptive view of decision biases underlies the Personalized and Prescriptive approach to decision aiding.

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