12/19 神經經濟學演講 Ian Krajbich - 經濟

神經經濟學家 Ian Krajbich (Ohio State 心理系與經濟系助理教授) 來台訪問，
12/19 在陽明大學神研所(10:10-11:30am)與台大經濟系(1:45-3:15pm)各有一場演講。
以下是演講邀請函，歡迎大家參加。

Joseph

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Dear Colleagues,

The upcoming neuroeconomics colloquium series will feature Ian Krajbich,
Assistant Professor in the Department of Psychology and Department of
Economics at Ohio State University.

Dr.Krajbich is a young neuroeconomist who studies decision-making using
computational models and/or economic modelling, as well as neuroscience
tools including eyetracking, fMRI, tDCS and brain-damaged patients.
He has published in neuroscience journals including Science, PNAS,
Psychological Science, Nature Neuroscience, Journal of Neuroscience, as
well as economic journals including the American Economic Review:
https://economics.osu.edu/people/krajbich
http://faculty.psy.ohio-state.edu/krajbich/

Ian will give 2 public lectures. Please see below for more detailed
information. Please do not hesitate to contact us if you wish to meet
with Dr. Krajbich.

Sincerely,

黃貞穎、王道一、郭文瑞、吳仕煒、葉俊毅



演講一
Title: A Common Mechanism Underlying Value-based Decision Making
(各種用價值來做的決策背後的共通機制)
Date and Time: 12/19 (四) 10:10-11:30am
Location: 陽明大學活動中心第三會議室

Abstract:
People make numerous decisions every day including perceptual decisions
like walking through a crowd, decisions over primary rewards like what
to eat, and social decisions that require balancing own and others’
benefits. The unifying principles behind choices in various domains are,
however, still not well understood. Mathematical models that describe
choice behavior in specific contexts have provided important insights
into the computations that may underlie decision making in the brain.
However, a critical and largely unanswered question is whether these
models generalize from one choice context to another. Here I show that a
model adapted from the perceptual decision-making domain and estimated
on choices over food rewards accurately predicts choices and reaction
times in three independent sets of subjects making social decisions. The
robustness of the model across domains provides strong evidence for a
common decision-making process in perceptual, primary reward, and social
decision making. I also provide evidence that visual attention is a
critical variable in this process.



演講二
Title: Benefits of Neuroeconomic Modeling: New Policy Interventions and
Predictors of Preference (神經經濟理論的價值：政策建議與偏好預測)
Date and Time: 12/19 (四) 1:45-3:15pm
Location: 台大經濟系經研大樓二樓(社科院第2教室)

Abstract:
From its inception, neuroeconomics has promised to use knowledge from
neuroscience and psychology to improve models of economic decision
making. Here we deliver on this promise by introducing a
biologically-plausible (drift-diffusion) model of decision making that
is able to predict behavior, with fixed parameters, in random groups of
individuals across very different tasks. The model also goes beyond the
traditional economic approach because it is able to predict not only
choices but reaction times as well. But why should economists care about
modeling reaction times? For one, as predicted by the model, we
consistently observe that people spend too much time on irrelevant
choices for which they are nearly indifferent. In a world of time
constraints, time spent on unimportant decisions represents an
opportunity cost, namely time not spent on more important decisions. To
help subjects overcome this inefficient allocation of time, we designed
an intervention that puts a time limit on subjects’ choices. This simple
intervention substantially improved subjects’ welfare in a
time-constrained choice experiment. Second, the precise relationship
between reaction times and choice probabilities implies that reaction
times can be used to predict indifference points and the strength of
preferences. We demonstrate this ability using data from an Ultimatum
Game experiment. Overall, we show that better understanding the process
by which people make decisions can explain the basis for stochastic
choice behavior and help us do both normative and positive economics.

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