REFRAMING DECISION PROBLEMS: A GRAPH-GRAMMAR APPROACH

Abstract

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 computational model 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 model 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.