Risk attitude and perception is reflected in brain reactions during RPID experiments. Given the fMRI data, an important research question is how to detect risk related regions and to investigate the relation between risk preferences and brain activity. Conventional methods are often insensitive...

Decision making can be a complex process requiring the integration of several attributes of choice options. Understanding the neural processes underlying (uncertain) investment decisions is an important topic in neuroeconomics. We analyzed functional magnetic resonance imaging (fMRI) data from...

Both mathematical modelling and simulation methods in general have contributed greatly to understanding, insight and forecasting in many fields including macroeconomics. Nevertheless, we must remain careful to distinguish model-land and model-land quantities from the real world. Decisions taken...

This chapter examines the relationship between agent-based modeling and economic decision-making experiments with human subjects. Both approaches exploit controlled “laboratory” conditions as a means of isolating the sources of aggregate phenomena. Research findings from laboratory studies...

This paper investigates the behaviour in repeated decision situations. The experimental study shows that subjects show low or no risk-aversion, but put very high value on the opportunity to sell the lottery in every stage of the decision problem. There is evidence that risk attitudes depend on...

Aim/purpose - The COVID-19 pandemic generated a new communication universe with numerous actors, including conspiracy theory (CT) promoters who spread skepticism about the authenticity of the pandemic and the necessity of health emergency regulations. This study explores the dissemination of...

Under the threat of earthquakes, long-term policy makers need tools to decide optimally on the economic trajectories that maximize the society welfare. Tools should be flexible and account for the consequences of earthquakes, incorporating the best estimate of their frequency and intensity. In...

We study Markov decision problems where the agent does not know the transition probability function mapping current states and actions to future states. The agent has a prior belief over a set of possible transition functions and updates beliefs using Bayes' rule. We allow her to be misspecified...

This paper is the 8th of a series of 9 lecture notes in Algorithmic and Advanced Programming in Python that are part of the Syllabus of Dauphine PSL's Master in Computer Science, Decision Making & Data. It taught advanced data structure in python with a focus on core and practical details to...