Distributed Decision Processes Research Articles

Contrasting roles of measurement knowledge systems in confounding or creating sustainable change

Sustainable change initiatives are often short-circuited by failures in modelling. Unexamined assumptions about measurement and numbers push modelling into the background as a presupposition rarely articulated as an explicit operation. Even when models of system dynamics are planned components of a sustainable change effort, the key role of measurement is typically overlooked. The crux of the matter concerns the distinction between numeric counts and measured quantities. Mistaking the former for the latter confuses levels of complexity and fundamentally compromises communications. Reconceiving measurement as modelling multilevel distributed decision processes offers new alternatives aligned with historically successful efforts in creating sustainable change. Five conditions for successful sustainable change are contrasted from the perspectives of single-level vs multilevel modelling: vision, plans, skills, resources, and incentives. Omitting any one of these from efforts at creating change result, respectively, in confusion, treadmills, anxiety, frustration, and resistance. The shortcomings of typically implemented single-level approaches to measurement result in the widespread experience of these negative consequences. Results show that new potentials for creating sustainable change can be expected to follow from implementations of multilevel distributed decision processes that effectively counteract organizational amnesia by embedding new learning in an externally materialized knowledge infrastructure incorporating a shared cultural memory.

The Strategic Garbage Can

Cohen, March and Olsen’s Garbage Can Model of Organizational Choice is generally thought to describe organizations characterized by extremely distributed decision processes, to the point that their members can at most hope to exploit serendipitous opportunity windows that eventually appear on the scene. By analyzing a little-known report on American college presidents published by Cohen and March in 1974 we show that garbage cans were meant also as a tool to neutralize opponents by making them busy with irrelevant issues. We analyze the strategic usage of garbage cans and discuss this concept within the scholarly and the business literature.

Counting votes in coupled decisions

We consider scenarios with distributed decision processes, e.g., coupled majorities and personal union in parliament chambers, supranational decisions and supervisory boards. When computing the adoption rate for reaching a decision in these scenarios, multiple linear inequality restrictions in combinatorial countings are present. These rates cannot be computed in closed form. We introduce a general method for incorporating multiple inequality conditions in multiple majority decisions, which significantly reduces the number of involved summations and removes restrictions on the summation indices. Exact solutions are provided through (a) integral representations which can be evaluated numerically, and (b) unrestricted, contracted sums over discrete events. Further, we provide methods to reduce the number of necessary summations by splitting or recurring the original problem to easier sub-problems. For five dedicated scenarios, full results are given which indeed require a single unrestricted summation only.

A decentralized optimization algorithm for multiagent system–based watershed management

A watershed can be simulated as a multiagent system (MAS) composed of spatially distributed land and water users (agents) within a common defined environment. The watershed system is characterized by distributed decision processes at the agent level with a coordination mechanism organizing the interactions among individual decision processes at the system level. This paper presents a decentralized (distributed) optimization method known as constraint‐based reasoning, which allows individual agents in an MAS to optimize their behaviors over various alternatives. The method incorporates the optimization of all agents' objectives through an interaction scheme, in which the ith agent optimizes its objective with a selected priority for collaboration and forwards the solution and consequences to all agents that interact with it. Agents are allowed to determine how important their own objectives are in comparison with the constraints, using a local interest factor (βi). A large βi value indicates a selfish agent who puts high priority on its own benefit and ignores collaboration requirements. This bottom‐up problem‐solving approach mimics real‐world watershed management problems better than conventional “top‐down” optimization methods in which it is assumed that individual agents will completely comply with any recommendations that the coordinator makes. The method is applied to a steady state hypothetical watershed with three off‐stream human agents, one in‐stream human agent (reservoir), and two ecological agents.

Supporting distributed decision processes using an evolution model

In recent years, emerging Information and Communication Technologies have changed the nature and process of decision making. Decision processes are often distributed, heterogeneous and subject to change. Business process modelling is a key technology for analysing, representing and executing business processes. It can be used to study distributed decision processes and improve decision making practices. A theoretical model of the decision process has been developed in order to better integrate the decision concept with models of business processes. The decision model is executed using a process support system that provides a distributed Web user interface. The enacted decision model evolves as the decision process progresses and supports decision makers if and when needed. The case study of an actual decision process undertaken in a not-for-profit organisation is presented to highlight the use, execution and validation of the decision model.

Adapt@Agent.Hospital

There are several continuing challenges within the health care domain. On the one hand, there is a greater need for individualized, patient-oriented processes in diagnostics, therapy, nursing, and administration, and on the other hand, hospitals have extremely distributed decision processes and strong local (individual) autonomy with a high degree of situational dynamics. This research focuses on developing an information system that can substantially increase the efficiency of hospital process management. We present a system for agent-based simulation and support for clinical processes. In particular, we describe the architecture of the system and its functionalities and the integration of existing Foundation for Intelligent Physical Agents (FIPA), DICOM, and HL7 standards. We discuss an example scenario for clinical trials to illustrate how the system supports distributed clinical process management. This system interacts with other multiagent systems within the Agent.Hospital framework and hospital information systems (HISs) in the eHealth Lab. This research is part of the German Priority Research Program (SPP) 1083, Intelligent Agents and Their Application in Business Scenarios.

A transformed geometric distribution in stochastic modelling

The analytical concepts of infinite divisibility and (0) unimodality are fundamental to the study of probability distributions in general and to discrete distributions in particular. In this paper, a one-one correspondence is established between these two important properties which will permit any infinitely divisible discrete distribution (with finite mean value) to be transformed into a (0) unimodal discrete distribution. When this transformation is applied specifically to the geometric distribution, the result is a novel distribution, which can be fully and explicitly specified and whose factorial moments can be expressed in closed forms. This transformed geometric distribution is found to apply to underreported geometrically distributed decision processes, embedded renewal processes with logarithmically distributed components, and M/ M/1 queues in which the service mechanism has been uniformly improved.

A‐pool: An agent‐oriented open system shell for distributed decision process modeling

An agent‐oriented open system shell, A‐Pool, for distributed decision process modeling in the Internet domain is presented. Unlike most decision support systems, A‐Pool provides a testbed for modeling and understanding the cognitive aspects of distributed decision processes themselves rather than for domain‐specific problem solving. This is achieved with a pool of virtual agents and a pool of cognitive maps of the agents at each A‐Pool node. The virtual agent scheme extends object‐oriented programming to the Internet domain and supports different communication and collaboration protocols with virtual communities, virtual sessions, and virtual conferences. The cognitive map scheme supports perspective sharing and various conflict integration and resolution strategies through cognitive map composition, derivation, and focus generation. Thus each A‐Pool node provides an architecture for modeling interdependencies and for ensuring global coherence; in addition, the communication is asynchronous and the contro...