Using Bayesian Agents to Enable Distributed Network Knowledge: A Critique

Resource based theory (RBT) states that there are dynamic relationships between individual-resource interactions, which ultimately determine an organisation’s global behaviour in its environment. When combining in idiosyncratic, functional ways to enable an organisation’s global behaviour, we call them complementary resource combinations (CRCs), and socially complex resource combinations (SRCs) when referring to only the complex web of social interactions of these resources. Casual ambiguity refers to the inherent uncertainty when the global behaviour is both tangibly evident and known, but the way in which the unique local interactions between SRCs amongst themselves and the environment ultimately contribute to the global behaviour is often unclear. Thus, in order to understand social complexity and causal ambiguity of an organization, the SRCs emergent behaviours and the causal local interactions must be observed over time, and the inter-relationships must be identified and made tangible. In our research, we use simple agents to observe the local and global behaviours, to mine the inter-relationships and to model the SRCs. These agents are organized into two types of agencies: Bayesian agencies and competence agencies. The Bayesian agencies are the observers – they collectively implement specialised, distributed Bayesian networks, which enable the agencies to collectively mine relationships between emergent global behaviours and the local interactions that caused them to occur. The competence agencies are the actors – they use the beliefs of selected Bayesian agencies and perform dynamic network analysis. In dynamic network analysis, temporal data is used to predict changes that will occur in the SRCs. Most importantly, the Bayesian agencies observe and mine temporal patterns in various metrics over time, and the competence agencies evolve the SRCs. Relationships discovered and maintained by Bayesian agencies and competence agencies are integrated into cutting-edge, resource-based topic maps (ISO 13250:2002), which provide a way of modelling the SRCs

Adaptive Bayesian agents: Enabling distributed social networks

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

Developing a Basis for Knowledge Management: A Bayesian Network Approach

Knowledge Management (KM) is an evolving field that attempts to maximise and sustain the competitive advantage of a company through leveraging its knowledge resources. KM practises are often built on a foundation of knowledge transfer and knowledge sharing. Recently there has been an increase on the reliance of automated tools to perform these functions. Typical components of these tools include: querying large datasets, user profiling, user interfaces and recommender systems. Traditionally, these components have been implemented using different technologies. This paper describes an approach to building these components using a flexible architecture based on Bayesian Network technology. Finally the paper considers some of the advantages to adopting the latter approach