Reframing Decision Problems: A Graph-Grammar Approach

One fundamental requirement in the expected utility model is that the preferences of rational persons should be independent of problem description. Yet an extensive body of research in descriptive decision theory indicates precisely the opposite: when the same problem is cast in two different but normatively equivalent “frames,” people tend to change their preferences in a systematic and predictable way. In particular, alternative frames of the same decision-tree are likely to invoke different sets of heuristics, biases and risk-attitudes in the user's mind. The paper presents a modeling environment in which decision-trees are cast as attributed-graphs, and reframing operations on trees are implemented as graph-grammar productions. In addition to the basic functions of creating and analyzing decision-trees, the environment offers a natural way to define a host of “debiasing mechanisms” using graphical programming techniques. Some of these mechanisms have appeared in the decision theory literature, whereas others were directly inspired by the novel use of graph-grammars in modeling decision problems. The modeling environment was constructed using NETWORKS, a new model management system based on a graph-grammar formalism. Thus, a second objective of the paper is to illustrate how a general-purpose modeling environment can be used to produce, with relatively little effort, a specialized decision support system for problems that have a strong graphical orientation.