Abstract not found

. In this paper, we pose and motivate a challenge, namely the need for a new science of multiagent systems. We propose that this new science should be grounded, theoretically on a richer conception of sociality, and methodologically on the extensive use of computational modelling for real-world applications and social simulations. Here, the steps we set forth towards meeting that challenge are mainly theoretical. In this respect, we provide a new model of multi-agent systems that reflects a fully explicated conception of cognition, both at the individual and the collective level. Finally, the mechanisms and principles underpinning the model will be examined with particular emphasis on the contributions provided by contemporary organisation theory. 1.

Abstract: Environmental scanning is the acquisition and use of information about events, trends, and relationships in an organization's external environment, the knowledge of which would assist management in planning the organization's future course of action. Depending on the organization's beliefs about environmental analyzability and the extent that it intrudes into the environment to understand it, four modes of scanning may be differentiated: undirected viewing, conditioned viewing, enacting, and searching. We analyze each mode of scanning by examining its characteristic information needs, information seeking, and information use behaviors. In addition, we analyze organizational knowing processes by considering the sensemaking, knowledge creating and decision

Understanding how organisations make decisions is a crucial step towards understanding organisations. Seeing

this paper, we present an algorithm that performs the partial deductive

This paper develops and tests a model of how organizational structure influences organizational performance. Organizational structure, conceptualized as the decision-making structure among a group of individuals, is shown to affect the number of initiatives pursued by organizations, and the omission and commission errors (Type I and II errors, respectively) made by organizations. The empirical setting are over 150,000 stock-picking decisions made by 609 mutual funds. Mutual funds offer an ideal and rare setting to test the theory, as detailed records exist on the projects they face, the decisions they make, and the outcomes of these decisions. The independent variable of the study, organizational structure, is coded from fund management descriptions made by Morningstar, and the estimates of the omission and commission errors are computed by a novel technique that uses bootstrapping to create measures which are comparable across funds. The findings suggest that organizational structure has relevant and predictable effects on a wide range of

I examine the role of explaining variance in the construction of explanatory theory. Explaining variance can be an insufficient basis for evaluating a theory (Lieberson, 1985). Starting with this insight, I suggest that models that provide explanations of variance do not necessarily provide good explanations of causal mechanisms. I then explore the utility of process models and theories (Mohr, 1982) relative to variance theories. I clarify the role of stochastic processes in such model building and discuss the implications of such processes for evaluating explanatory `adequacy'. Under some conditions, explaining variance may be neither a necessary nor a sufficient condition for good explanatory theory. I then identify some implications of this argument for developing and analyzing explanatory theory. These arguments are applied to two examples: (1) meta-analysis and (2) the disposition versus situation debate (a variant on the nature vs. nurture argument) to illustrate the implications of ...

We use NetLogo to design a simple multi-agent environment to explore organizational decision processes. Our work builds on the original insight of the "Garbage Can Model" of organizational choice (GCM) proposed by M. Cohen, J. March, and J. Olsen (1972). In this model organizations are viewed as crossroads of time-dependent flows of four distinct classes of objects: "problems," "solutions," "participants" and "opportunities." Collisions among the different objects generate events called "decisions." In our NetLogo-based reconstruction of the GCM, the type of decision is determined by the relative levels of energy accumulated by "participants" and "opportunities" up to the moment of collision. We make no attempt to reproduce all the features of the original model. Some features of our representation are not present in the original model and are included to cast new light on specific aspects of decision processes in organizations. The model is a highly preliminary attempt to represent organizational decision processes when agents live in a structured socio-physical space, and are capable of reproducing, i.e. of creating identical copies of themselves. In its current state of development the model serves mainly didactic purposes and as an illustration of the value of the NetLogo programming environment for representing complex organizational systems. The general learning point that the model can be used to illustrate is that dynamic complexity at the organizational level does not depend on complexity at level of individual agents. This conclusion suggests a conceptual link between aspects of social organizations and the study of emergence in natural and artificial systems.

We view information system development (ISD) and change as a socio-technical change process in which technologies, human actors, organizational relationships and tasks change. We outline a punctuated socio-technical change model that recognizes both incremental and dynamic and abrupt changes during ISD and change. The model identifies events that incrementally change the information system as well as punctuate its deep structure in its evolutionary path at multiple levels. The analysis of these event sequences helps explain how and why an ISD outcome emerged. The change constructs are integrated with a socio-technical model of ISD in which configurations in work systems, building systems and the environment and their misalignments- gaps- drive ISD change. By conceiving ISD and change as a sequence of events and states, researchers can narrate explanations of ISD outcomes. Practitioners can use the model in post mortem analyses to diagnose and learn about the effectiveness of their ISD interventions. The explanatory power of the model is demonstrated with a case study of complex ISD and change over an eight year period.

Environmental Law Institute, it may not necessarily reflect the views of the Agency and no official endorsement should be inferred.