- ### __Bias : __ - Well travelled road effect - ### __Definition : __ - Underestimation of the duration taken to traverse oft-traveled routes and overestimation of the duration taken to traverse less familiar routes. - ### __OODA Class : __ - Decide Phase - ### __OODA Subclass : __ - Complexity - ### __Classification Reasoning : __ - After having ranked alternatives, the next step is to choose one for implementation. The tendency is to choose an option with which one is familiar with since the complexity involved is relatively lesser. An unfamiliar option is always associated with higher complexities leading to steep learning curve and exaggerated timelines. - ### __Example : __ - : Selection of a chatbot framework: 2017 has been the so-called year of chatbots. Natural language processing tools have made plenty of progress with options such as DialogFlow, Watson, Lex and so on available in the market. All the above-mentioned technologies are quite similar in nature with subtle differences in terms of certain extra features or under the hood implementation differences. - ### __Impact : __ - : Let us assume that the person in charge of making the selection for a new project is familiar with DialogFlow having worked with it previously. Due to familiarity with DialogFlow, the person would be inclined to choosing it again. This has an impact on the time estimates for implementation as it would be generally be lesser when using DialogFlow due to the feeling of it being a well-travelled road as opposed to when the decision is to go with Watson. - ### __Debiasing Techniques : __ - In case of a familiar technology, then assign a buffer time of 10%. In case of unfamiliar technologies, try to get an estimate from people well versed in working with those technologies and limit the buffer period to 10%. - ### __Related Biases : __ - [[Complexity Bias]], [[Time-saving bias]]