The Logic of Failure by Dietrich Dörner

This book was mentioned in a nuclear safety discussion forum so we figured this is a good time to revisit Dörner's 1989 tome.* Below we provide a summary of the book followed by our assessment of how it fits into our interest in decision making and the use of simulations in training.

Dörner's work focuses on why people fail to make good decisions when faced with problems and challenges. In particular, he is interested in the psychological needs and coping mechanisms people exhibit. His primary research method is observing test subjects interact with simulation models of physical sub-worlds, e.g., a malfunctioning refrigeration unit, an African tribe of subsistence farmers and herdsmen, or a small English manufacturing city. He applies his lessons learned to real situations, e.g, the Chernobyl nuclear plant accident.

He proposes a multi-step process for improving decision making in complicated situations then describes each step in detail and the problems people can create for themselves while executing the step. These problems generally consist of tactics people adopt to preserve their sense of competence and control at the expense of successfully achieving overall objectives. Although the steps are discussed in series, he recognizes that, at any point, one may have to loop back through a previous step.

Goal setting

Goals should be concrete and specific to guide future steps. The relationships between and among goals should be specified, including dependencies, conflicts and relative importance. When people don't to do this, they can become distracted by obvious or unimportant (although potentially achievable) goals, or peripheral issues they know how to address rather than important issues that should be resolved. Facing performance failure, they may attempt to turn failure into success with doublespeak or blame unseen forces.

Formulate models and gather information

Good decision-making requires an adequate mental model of the system being studied—the variables that comprise the system and the functional relationships among them, which may include positive and negative feedback loops. The model's level of detail should be sufficient to understand the interrelationships among the variables the decision maker wants to influence. Unsuccessful test subjects were inclined to use a “reductive hypothesis,” which unreasonably reduces the model to a single key variable, or overgeneralization.

Information gathered is almost always incomplete and the decision maker has to decide when he has enough to proceed. The more successful test subjects asked more questions and made fewer decisions (then the less successful subjects) in the early time periods of the sim.

Predict and extrapolate

Once a model is formulated, the decision maker must attempt to determine how the values of variables will change over time in response to his decisions or internal system dynamics. One problem is predicting that outputs will change in a linear fashion, even as the evidence grows for a non-linear, e.g., exponential function. An exponential variable may suddenly grow dramatically then equally suddenly reverse course when the limits on growth (resources) are reached. Internal time delays mean that the effects of a decision are not visible until some time in the future. Faced with poor results, unsuccessful test subjects implement or exhibit “massive countermeasures, ad hoc hypotheses that ignore the actual data, underestimations of growth processes, panic reactions, and ineffectual frenetic activity.” (p. 152) Successful subjects made an effort to understand the system's dynamics, kept notes (history) on system performance and tried to anticipate what would happen in the future.

Plan and execute actions, check results and adjust strategy

“The essence of planning is to think through the consequences of certain actions and see whether those actions will bring us closer to our desired goal.” (p. 153) Easier said than done in an environment of too many alternative courses of action and too little time. In rapidly evolving situations, it may be best to create rough plans and delegate as many implementing decisions as possible to subordinates. A major risk is thinking that planning has been so complete than the unexpected cannot occur. A related risk is the reflexive use of historically successful strategies. “As at Chernobyl, certain actions carried out frequently in the past, yielding only the positive consequences of time and effort saved and incurring no negative consequences, acquire the status of an (automatically applied) ritual and can contribute to catastrophe.” (p. 172)

In the sims, unsuccessful test subjects often exhibited “ballistic” behavior—they implemented decisions but paid no attention to, i.e, did not learn from, the results. Successful subjects watched for the effects of their decisions, made adjustments and learned from their mistakes.

Dörner identified several characteristics of people who tended to end up in a failure situation. They failed to formulate their goals, didn't recognize goal conflict or set priorities, and didn't correct their errors. (p. 185) Their ignorance of interrelationships among system variables and the longer-term repercussions of current decisions set the stage for ultimate failure.

Assessment

Dörner's insights and models have informed our thinking about human decision-making behavior in demanding, complicated situations. His use and promotion of simulation models as learning tools was one starting point for Bob Cudlin's work in developing a nuclear management training simulation program. Like Dörner, we see simulation as a powerful tool to “observe and record the background of planning, decision making, and evaluation processes that are usually hidden.” (pp. 9-10)

However, this book does not cover the entire scope of our interests. Dörner is a psychologist interested in individuals, group behavior is beyond his range. He alludes to normalization of deviance but his references appear limited to the flaunting of safety rules rather than a more pervasive process of slippage. More importantly, he does not address behavior that arises from the system itself, in particular adaptive behavior as an open system reacts to and interacts with its environment.

From our view, Dörner's suggestions may help the individual decision maker avoid common pitfalls and achieve locally optimum answers. On the downside, following Dörner's prescription might lead the decision maker to an unjustified confidence in his overall system management abilities. In a truly complex system, no one knows how the entire assemblage works. It's sobering to note that even in Dörner's closed,** relatively simple models many test subjects still had a hard time developing a reasonable mental model, and some failed completely.

This book is easy to read and Dörner's insights into the psychological traps that limit human decision making effectiveness remain useful.

* D. Dörner, The Logic of Failure: Recognizing and Avoiding Error in Complex Situations, trans. R. and R. Kimber (Reading, MA: Perseus Books, 1998). Originally published in German in 1989.

** One simulation model had an external input.