Work detail

Acquisition of Costly Information in Data-Driven Decision Making

Author: Mgr. Lukáš Janásek
Year: 2021 - summer
Leaders: doc. PhDr. Jozef Baruník Ph.D.
Work type: Economic Theory
Language: English
Pages: 101
Awards and prizes: Nomination Deloitte Outstanding Thesis Award.
Abstract: This thesis formulates and solves an economic decision problem of the acquisition of costly information in data-driven decision making. The thesis assumes
an agent predicting a random variable utilizing several costly explanatory variables. Prior to the decision making, the agent learns about the relationship
between the random variables utilizing its past realizations. During the decision making, the agent decides what costly variables to acquire and predicts
using the acquired variables. The agent’s utility consists of the correctness of
the prediction and the costs of the acquired variables. To solve the decision
problem, the thesis divides the decision process into two parts: acquisition of
variables and prediction using the acquired variables. For the prediction, the
thesis presents a novel approach for training a single predictive model accepting
any combination of acquired variables. For the acquisition, the thesis presents
two novel methods using supervised machine learning models: a backward estimation of the expected utility of each variable and a greedy acquisition of
variables based on a myopic increase in the expected utility of variables. Next,
the thesis formulates the decision problem as a Markov decision process which
allows approximating the optimal acquisition via deep reinforcement learning
methods. The thesis suggests a novel formulation of reward for the training
as a net utility gain of the acquired variable. The thesis evaluates selected
methods on two medical datasets. The results show that the methods acquire
the costly variables efficiently.
January 2022




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