A Supervised Goal Directed Algorithm in Economical Choice Behaviour: An Actor-Critic Approach
This work addresses a specific problem in neuroeconomics by proposing an incremental algorithmic modification for simulating choice processes under uncertainty.
The paper tackles the challenge of predicting and explaining economic choice behavior under uncertainty by modifying the Actor-Critic learning method to incorporate neurological insights on rewards and beliefs, aiming to resolve issues like lack of inheritance or hierarchy in continuous-time tasks.
This paper aims to find an algorithmic structure that affords to predict and explain economical choice behaviour particularly under uncertainty(random policies) by manipulating the prevalent Actor-Critic learning method to comply with the requirements we have been entrusted ever since the field of neuroeconomics dawned on us. Whilst skimming some basics of neuroeconomics that seem relevant to our discussion, we will try to outline some of the important works which have so far been done to simulate choice making processes. Concerning neurological findings that suggest the existence of two specific functions that are executed through Basal Ganglia all the way up to sub- cortical areas, namely 'rewards' and 'beliefs', we will offer a modified version of actor/critic algorithm to shed a light on the relation between these functions and most importantly resolve what is referred to as a challenge for actor-critic algorithms, that is, the lack of inheritance or hierarchy which avoids the system being evolved in continuous time tasks whence the convergence might not be emerged.