Rationalizing Outcomes: Interdependent Learning in Competitive Markets

Author:

Menon Anoop1ORCID,Yao Dennis2ORCID

Affiliation:

1. Eisengard AI, San Francisco, California 94108;

2. Strategy Unit, Harvard Business School, Boston, Massachusetts 02163

Abstract

In this article, we use simulation models to explore interdependent learning in competitive markets. Such interactions require attention to both the mental representations held by the management of the focal firm as well as the beliefs of that management about the representations held by rival management. When jointly determined outcomes are the primary input to the learning process, understanding rival beliefs—what we call strategic empathy—becomes a crucial factor driving performance. To illustrate these processes, we focus on mental representations that a manager has regarding market demand. In our simulation models, learning occurs through market observations, which recalibrate a manager’s representation about demand. But the flexibility allowed by this recalibration is also a way through which managers rationalize incorrect representations. We find these processes sometimes lead to distortions of initially correct representations and may result in superior relative performance by the firm whose manager holds less correct representations. Finally, we explore how superior knowledge of a rival’s demand representations can improve own performance through market actions that intentionally shape how a rival learns. Supplemental Material: The online appendix is available at https://doi.org/10.1287/stsc.2018.0083 .

Publisher

Institute for Operations Research and the Management Sciences (INFORMS)

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