Adaptive Bayesian agents: Enabling distributed social networks

Abstract

This article brings together two views of organisations: resource-based theories (RBT) and social network analysis (SNA). Resource-based theories stress the importance of tangible assets, as well as less tangible ones, in the competitive advantage and success of organisations. However, they provide little insight into how resources are brought together by an organisation to generate core competencies that provide a source of differentiation that cannot easily be reproduced or substituted. In contrast SNA provides insight into the complexity of organisations and the interaction between the people within them, taking account of uncertainty and complexity. However, neither perspective gives significant insight into how organisations evolve over time, and how their competitive position is sustained or eroded. Our view is that integrating these two perspectives gives deeper insight into the basis of competitive advantage, and how it can evolve over time. ‘Complementary resource combinations’ (CRCs), bundles of related resources, can provide a basis for differentiation but only when these are embedded in a complex web of social interactions specific to the organisation. The ‘socially-complex resource combinations’ (SRCs) enable competitive advantage that is not readily reproduced or substituted, and which evolves over time in an uncertain and complex way. They are the basis of distinctive organisational competencies that enable the organisation to be a player in the marketplace, and in some cases to sustain competitive advantage. To understand how competitive advantage can be sustained, it is necessary to understand how these SRCs evolve over time, based on the interactions in social networks. To do this, we use Bayesian networks and topic maps, making hidden social relationships tangible. We use dynamic agents to observe local and global behaviours to model the SRCs. In this, we use the concept of ‘agencies’ that are networks of individual agents and which can solve problems and adapt in ways that are too complex for individual agents. The article outlines how this approach can be used to model complex social networks over time, recognising uncertainty and complexity, hence giving the ability to predict changes that will occur in the SRCs.