Hierarchical decision-making produces persistent differences in learning performance

Thorbjørn Knudsen,Massimo Warglien,Davide Marchiori

doi:10.1038/s41598-018-34128-w

Thorbjørn Knudsen, Massimo Warglien + Show 1 more

Open Access

https://doi.org/10.1038/s41598-018-34128-w

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Abstract

Human organizations are commonly characterized by a hierarchical chain of command that facilitates division of labor and integration of effort. Higher-level employees set the strategic frame that constrains lower-level employees who carry out the detailed operations serving to implement the strategy. Typically, strategy and operational decisions are carried out by different individuals that act over different timescales and rely on different kinds of information. We hypothesize that when such decision processes are hierarchically distributed among different individuals, they produce highly heterogeneous and strongly path-dependent joint learning dynamics. To investigate this, we design laboratory experiments of human dyads facing repeated joint tasks, in which one individual is assigned the role of carrying out strategy decisions and the other operational ones. The experimental behavior generates a puzzling bimodal performance distribution–some pairs learn, some fail to learn after a few periods. We also develop a computational model that mirrors the experimental settings and predicts the heterogeneity of performance by human dyads. Comparison of experimental and simulation data suggests that self-reinforcing dynamics arising from initial choices are sufficient to explain the performance heterogeneity observed experimentally.

Highlights

Human organizations are commonly characterized by a hierarchical chain of command that facilitates division of labor
In a behavioral study and a hybrid model of multi-level learning, we examine the experimental behavior of hierarchical dyads, in which the high-level agent ( how supervised (L-agent) and unsupervised (H-agent)) carries out strategy decisions, and the low-level agent ( L-agent) ongoing operational decisions
Three main assumptions of our design make the task of the L- and H-agents non-trivial: (i) input components vary with respect to the informative value that they provide to the L-agent; (ii) the L-agent evaluates each input only based on the knowledge of a proper subset of input components that the H-agent makes available to her; (iii) the informative value of the input components is unknown to both agents: The H-agent must learn to disclose the most informative input components solely based on the feedback from the L-agent’s performance

Summary

Introduction

Human organizations are commonly characterized by a hierarchical chain of command that facilitates division of labor. Higher hierarchical levels set decision premises that guide, constrain, and focus the operational tasks carried out by lower-level employees. This scheme, a cornerstone in modern organization theory, is adopted by most human organizations, independent of ownership, size, and purpose. Authors of focus on individual hierarchical decision processes that can be categorized into (high-level) strategy and (low-level) operational decisions, operating at different timescales. They show that, because of the difficulty in disambiguating the source of decision errors, critical two-way interactions occur between choices at the operational level and the selection of strategies at the higher level. Three main assumptions of our design make the task of the L- and H-agents non-trivial: (i) input components vary with respect to the informative value that they provide to the L-agent; (ii) the L-agent evaluates each input only based on the knowledge of a proper subset of input components that the H-agent makes available to her; (iii) the informative value of the input components is unknown to both agents: The H-agent must learn to disclose the most informative input components solely based on the feedback from the L-agent’s performance

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Conclusion