A System Approach to Describing, Analysing and Control of the Behaviour of Agents in MAS

The Petri nets (PN)-based analytical approach to describing both the single agent behaviour as well as the cooperation of several agents in MAS (multi agent systems) is presented. PN yield the possibility to express the agent behaviour and cooperation by means of the vector state equation in the form of linear discrete system. Hence, the modular approach to the creation of the MAS model can be successfully used too. Three different interconnections of modules (agents, interfaces, environment) expressed by PN subnets are introduced. The approach makes possible to use methods of linear algebra. Moreover, it can be successfully used at the system analysis (e.g. the reachability of states), at testing the system properties, and even at the system control synthesis.

Knowledge Cybernetics

Knowledge cybernetics is part of complex systems, and a post-normal science approach principally concerned with the development of agents like autonomous social collectives that survive through knowledge and knowledge processes. Deriving from epistemological antecedents created by Stafford Beer and explored through notions of ontology by Eric Schwarz, a new form of knowledge management arises that is connected with the notions of Marshall and her new radical classifications for knowledge. These ideas can be closely associated with concepts of lifeworld and the ideas of communicative action by Habermas, and leads to a useful knowledge cybernetic framework. This has the capacity to relate to and develop a variety of what might be thought of as otherwise disparate theories that can ultimately be expressed in terms of knowledge.

The Dynamic Usage of Models (DYSAM) as a Theoretically-Based Phenomenological Tool for Managing Complexity and as a Research Framework

In this paper, after recalling some fundamental concepts used in the science of complexity, we focus on theoretical and applicative cases of interest for the science of management of complex systems, where processes of emergence occur with the acquisition of new properties. The tool proposed is the DYnamical uSAge of Models (DYSAM). Within this framework we then focus upon a) the theoretical difference between growth and development; b) the sustainability of development rather than of growth as originally introduced in the literature; c) the concept of long tail (when, after initial large volume sales, low-revenue and infrequent buying may become a very important percentage of the entire business) as in telecommunications and management of long-tailed systems; d) non-reductionist management of complexity not reduced to solutions, and e) a future line of research to model processes of emergence.

Metatheorising Transformational Management

Corporate management is facing a world full of transformational challenges. How might theory development contribute to a more transformative vision of management? While there have been attempts by theorists to move beyond conventional conceptualizations, more innovative and, in particular, more integrative theoretical frameworks are still needed. Conventional and new paradigm management theories take contending sides in the change debate and often define their approaches in terms of dichotomous oppositions. Using an integrative approach to metatheory building, this article proposes that the application of a relational lens overcomes many common polarities and oppositions present within current theorisings. The relational qualities that emerge from this metatheoretical approach are presented as useful guides for developing innovative theories that address the operational and transformational challenges of 21st century management. The metatheoretical analysis not only provides an integrative framework for exploring more visionary conceptualisations of management it also shows that metatheorising has powerful critical capacities for assessing scientific theories in the social sciences.

Selection of the Best Subset of Variables in Regression and Time Series Models

The problem of variable selection is one of the most pervasive model selection problems in statistical applications. Often referred to as the problem of subset selection, it arises when one wants to model the relationship between a variable of interest and a subset of potential explanatory variables or predictors, but there is uncertainty about which subset to use. Several papers have dealt with various aspects of the problem but it appears that the typical regression user has not benefited appreciably. One reason for the lack of resolution of the problem is the fact that it is has not been well defined. Indeed, it is apparent that there is not a single problem, but rather several problems for which different answers might be appropriate. The intent of this chapter is not to give specific answers but merely to present a new simple multiplicative variable selection criterion based on the parametrically penalized residual sum of squares to address the subset selection problem in multiple linear regression analysis, where the objective is to select a minimal subset of predictor variables without sacrificing any explanatory power. The variables, which optimize this criterion, are chosen to be the best variables. The authors find that the proposed criterion performs consistently well across a wide variety of variable selection problems. Practical utility of this criterion is demonstrated by numerical examples.

Co-Construction of Learning Objects

It is an unsettled issue between research traditions how we should negotiate the implied rather than acknowledged dispute between individual agency, i.e. the ability/need/urge of a manager to act on/in the world on the one hand and the functioning of a structured social system where management and employee initiatives, relations and reactions are crucial for the main activity. This chapter resolves parts of the debate by drawing on general activity theory and a digital tool for facilitating job-related group interaction.

The Structure of Theory and the Structure of Scientific Revolutions

From a Kuhnian perspective, a paradigmatic revolution in management science will significantly improve our understanding of the business world and show practitioners (including managers and consultants) how to become much more effective. Without an objective measure of revolution, however, the door is open for spurious claims of revolutionary advance. Such claims cause confusion among scholars and practitioners and reduce the legitimacy of university management programs. Metatheoretical methods, based on insights from systems theory, provide new tools for analyzing the structure of theory. Propositional analysis is one such method that may be applied to objectively quantify the formal robustness of management theory. In this chapter, I use propositional analysis to analyze different versions of a theory as it evolves across 1,500 years of history. This analysis shows how the increasing robustness of theory anticipates the arrival of revolution and suggests an innovative and effective way for scholars and practitioners to develop and evaluate theories of management.

Decision Integrity and Second Order Cybernetics

The rational worldview of management science has come to dominate decision theory. This chapter proposes that, despite its evident successes, this view of decision making is decreasingly effective in a global world which turns out to be unruly and unpredictable in critical areas. The result is an escalation of unintended consequences in business, public affairs and human ecology. Despite its success in some fields of management, we need to question the rational view which disconnects the observer from the observed. Decision integrity is proposed as a reflexive theory of decision making that incorporates the decision maker as part of the decision field. It requires stepping out of the observer/object paradigm of classical science and into the alternative paradigm of second order cybernetics. The decision maker is not simply an observer but also a participant who cannot abdicate from personal ethical considerations and ultimate responsibility even in the face of uncertainty.

Exploring the Implications of Complexity Thinking for the Management of Complex Organizations

This article is an attempt to explore the implications of the emerging science of complexity for the management of organizations. It is not intended as an introduction to complexity thinking, but rather an attempt to consider how thinking ‘complexly’ might affect the way in which managers do their jobs. This is achieved in a rather abstract way with some theory, but I hope the general message that there is no one way to manage comes through loud and clear, and that management is as much an art as it is a science (and always will be). In a sense complexity thinking is about limits, limits to what we can know about our organizations. And if there are limits to what we can know, then there are limits to what we can achieve in a pre-determined, planned way.

Systemic Paradoxes of Organizational Change

This chapter explores systemic challenges surrounding a highly disruptive type of organizational change—the implementation of advanced manufacturing technology (AMT). To unpack the intricate, multi-layered systems surroundings AMT implementation, the author applies an inductive method that relies on multiple paradigm lenses to highlight varied elements and contrasting understandings of the change process. Using Burrell & Morgan’s (1979) typology, she constructs four accounts of AMT implementation. These accounts share a theme of paradox, yet each also accentuate different tensions and vicious cycles. To accommodate disparate paradigm insights, the proposed metaframework offers a more holistic, systemic view, depicting change as a multidimensional cycle swirling around cognitive, action and institutional paradoxes. The concluding discussion addresses implications of the metaframework for managing change paradoxes and future research.