Collaborative search and autonomous task allocation in organizations of learning agents

TL;DR

This paper models organizations with static or dynamic structures, showing how collaborative search and adaptive task allocation influence performance and organizational fit in learning agents.

Contribution

It introduces a model of organizational structures with static and dynamic configurations, highlighting the role of collaborative search and emergent task allocation.

Findings

Collaborative search can mitigate inefficient task allocations.

Emergent task allocation may not reflect technical interdependencies.

Dynamic organizations can achieve high performance despite mismatched task allocation.

Abstract

This paper introduces a model of multi-unit organizations with either static structures, i.e., they are designed top-down following classical approaches to organizational design, or dynamic structures, i.e., the structures emerge over time from micro-level decisions. In the latter case, the units are capable of learning about the technical interdependencies of the task they face, and they use their knowledge by adapting the task allocation from time to time. In both static and dynamic organizations, searching for actions to increase the performance can either be carried out individually or collaboratively. The results indicate that (i) collaborative search processes can help overcome the adverse effects of inefficient task allocations as long as there is an internal fit with other organizational design elements, and (ii) for dynamic organizations, the emergent task allocation does not necessarily mirror the technical interdependencies of the task the organizations face, even though the same (or even higher) performances are achieved.