On the validity of happiness data as a macroeconomic policy target: a fMRI study
2013/11/19
研討會日期 : 2013-11-19
時間 : 14:30
主講人 : Professor Katsunori Yamada
地點 : Conference Room C103
演講者簡介 : Professor Yamada received his Ph.D. in Economics from Kyoto University in 2006. He is currently Assistant Professor at Osaka University. His research fields are Macroeconomics, Behavior economics, Neuroeonomics.
演講摘要 : Stiglitz report in 2009 initiated a debate among economists, psychologists and policy makers on the validity of subjective well-being (happiness) as a policy target of economic issues. Traditional views from economics are negative, as there is no clear conceptual link between utility and happiness. In particular, economists often conceive of utility as a cardinal and open-ended term, whilst happiness data is ordinal and bounded. To provide clues for the debate across different disciplines, we employed a new approach based on a neuroscience experiment. Subjects reported their level of utility or happiness from monetary rewards using either a positive and unbounded integer (cardinal reporting for utility) or a scale from 1 to 9 (ordinal reporting for happiness) while undergoing functional magnetic resonance imaging. In our control task subjects evaluated simple visual stimuli with the purpose of removing non-utility-related components. In both tasks, blood-oxygen-level-dependent (BOLD) signals in the bilateral posterior parietal cortex were activated more strongly with ordinal reporting than cardinal reporting. The ordinal–cardinal difference in the striatal BOLD signal was greater in the utility task than the visual perception task. These findings suggest that the parietal–striatal network is involved in translating cardinal objects into ordinal expressions, and that the reward system performs ordinal scaling for happiness reporting. Due to the existence of this additional process, we suggest conventional happiness data to be a noisy proxy of intrinsic cardinal utility, requiring further tests on the nature of potential biases of happiness data collected.
