Abstract
We analyze the ordering decisions of a manager who acquires demand information to make inventory decisions. Human beings' cognitive ability in information acquisition is imperfect by nature. We adopt the rational inattention theory to model the information acquisition activity. By reformulating the rational inattention decision problem, we find that rational inattention theory resembles the free energy principle (Friston 2009, 2010) that is used to model brain activities. Therefore, rational inattention theory gets a neurocognitive foundation. We obtain closed-form solutions of the optimal action strategies, which is a discrete distribution although the states are continuous. The process of resulting the optimal action strategies shows that the essence of the process, as a neurocognitive activity, is to think until nothing to think. The optimal action strategies themselves show that an effective thinking is hard to obtain unless think one more step. We also study the impact on decisions once the primitive information is contaminated. If the primitive information is contaminated by, for example, data sampling errors, the acquired information will be eclipsed by the contamination and exhibit certain features that mistaken the ordering decisions by acquiring information if the primitive information were clean. The specific forms of the well-known one-to-one correspondence between action and signal are also restricted by the contamination. We propose an empirical approach to identify the optimal ordering quantities, and the approach works only if the information contamination is separated from the estimation of the unconditional optimal ordering strategies.
Highlights
In this paper, we study a rational inattention decision problem in an inventory decision-making context
Based on the findings of our model, we suggest that decision makers can refine their information acquisition result into a binary form of posterior distribution as long as they can, and think one more step based on the result that the optimal ordering quantity in the binary posterior that is chosen with lower likelihood is just the one that is closer to the state
In our paper, based on the insights derived from the study of behavioral decision biases, we further study how the contamination to primitive information by technical errors affect the inventory decision making by information acquisition
Summary
We study a rational inattention decision problem in an inventory decision-making context. The effectiveness failure means given the information acquired about a certain state, the optimal ordering quantity that is most close to the state is not chosen with highest likelihood with generality. It happens not just for binary posterior distributions, and for general forms of discrete posterior distributions. Based on the findings of our model, we suggest that decision makers can refine their information acquisition result into a binary form of posterior distribution as long as they can, and think one more step based on the result that the optimal ordering quantity in the binary posterior that is chosen with lower likelihood is just the one that is closer to the state.
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