Purpose: A closer examination of risky health-related decisions (to continue smoking, to stop taking prescribed medication, to ignore a breast lump) reveals that these decisions result from more than knowledge gaps or denial. This poster demonstrates a methodology for analyzing decisions to engage in health risk behavior to describe their complexity and understand their resilience. Methods: Using data from an in-depth ‘talk aloud' interview study, we demonstrate argument and heuristic (A&H) analysis techniques by mapping a woman's decision to delay diagnosis of a self-discovered breast lump. We demonstrate the argument strands identified in the complete argument to delay, evaluate the soundness of each argument strand, and identify the use of and reliance on heuristic reasoning strategies to support the judgment to delay. Finally we map the overall argument, demonstrating the dominance structure supporting and sustaining the judgment to delay. Results: This case analysis demonstrates the numbers of arguments made both in support of and in opposition to the decicion to delay. Delay can be seen to be supported by 1) arguments that relied on false information and poorly drawn inferences, and the abandonment of usually compelling arguments that challenge the decision to delay, and false claims about breast cancer and its control; and 2) the use of heuristic thinking strategies such as availability (recall of vivid breast symptom stories where another woman's delay was inconsequential or even beneficial), representativeness (‘This lump is just like a benign lump my friend had.'), the zero out tendency (‘My breast cancer risk is low so it is zero.”), satisficing (‘I'll wait until my next scheduled visit and then check out this lump.'), simulation (visualizing failure to achieve disease control as a result of seeking diagnosis). The argument map demonstrates the numerous bolstering arguments for delay that provide a dominance structure around the judgment to delay. Well positioned sound (but at times irrelevant) arguments support this woman's belief that she has thought well about this decision. Conclusions: This example of A&H analysis shows that complete decision maps such as these can improve our understanding of the complexity and resilience of decisions to engage in high risk health behaviors. Understanding why high risk judgments feel well conceived to those who risk their health could improve the design of intervention studies.