Speaker(s)

Dr. Gil Tal, Post-doctoral Researcher, UC Berkeley

Abstract

Evidence suggests that forecasts of the impacts of new policies are likely to be overly optimistic. Specifically, new travel demand management policies, the focus of this study, have raised high expectations that have seldom been met. The literature on overestimated impacts of policies explains these overestimations as the outcome of unintentional optimism bias, deliberate bias, or in some cases partially reported errors. This study explores the causes of overestimation bias in two TDM policies, carsharing and telecommuting, and the changes in this bias and its sources over time. In explaining the processes of evaluating and forecasting the impacts of new policies, I link this process to the social science method for establishing causality. Overestimation of new policies is correlated with a lack of data and knowledge, which allows only quantitative analysis of the policy potential and qualitative analysis of its limitations. The publication of lower forecasts over time, reflect increase in data and knowledge that establishes the actual impact of the policy, which is only a portion of the initial potential suggested. In this work, I demonstrate the effects of optimistic core beliefs on initial forecasts, as well as the importance of skeptical core beliefs that are triggered by the initial high forecasts, and result in lower, competing ones.