Fast and exact search for the partition with minimal information loss

In analysis of multi-component complex systems, such as neural systems, identifying groups of units that share similar functionality will aid understanding of the underlying structures of the system. To find such a grouping, it is useful to evaluate to what extent the units of the system are separable. Separability or inseparability can be evaluated by quantifying how much information would be lost if the system were partitioned into subsystems, and the interactions between the subsystems were hypothetically removed. A system of two independent subsystems are completely separable without any loss of information while a system of strongly interacted subsystems cannot be separated without a large loss of information. Among all the possible partitions of a system, the partition that minimizes the loss of information, called the Minimum Information Partition (MIP), can be considered as the optimal partition for characterizing the underlying structures of the system. Although the MIP would reveal novel characteristics of the neural system, an exhaustive search for the MIP is numerically intractable due to the combinatorial explosion of possible partitions. Here, we propose a computationally efficient search to precisely identify the MIP among all possible partitions by exploiting the submodularity of the measure of information loss. Mutual information is one such submodular information loss functions, and is a natural choice for measuring the degree of statistical dependence between paired sets of random variables. By using mutual information as a loss function, we show that the search for MIP can be performed in a practical order of computational time for a reasonably large system. We also demonstrate that MIP search allows for the detection of underlying global structures in a network of nonlinear oscillators

Identifying dynamical modules from genetic regulatory systems: applications to the segment polarity network

BACKGROUND It is widely accepted that genetic regulatory systems are 'modular', in that the whole system is made up of smaller 'subsystems' corresponding to specific biological functions. Most attempts to identify modules in genetic regulatory systems have relied on the topology of the underlying network. However, it is the temporal activity (dynamics) of genes and proteins that corresponds to biological functions, and hence it is dynamics that we focus on here for identifying subsystems. RESULTS Using Boolean network models as an exemplar, we present a new technique to identify subsystems, based on their dynamical properties. The main part of the method depends only on the stable dynamics (attractors) of the system, thus requiring no prior knowledge of the underlying network. However, knowledge of the logical relationships between the network components can be used to describe how each subsystem is regulated. To demonstrate its applicability to genetic regulatory systems, we apply the method to a model of the Drosophila segment polarity network, providing a detailed breakdown of the system. CONCLUSION We have designed a technique for decomposing any set of discrete-state, discrete-time attractors into subsystems. Having a suitable mathematical model also allows us to describe how each subsystem is regulated and how robust each subsystem is against perturbations. However, since the subsystems are found directly from the attractors, a mathematical model or underlying network topology is not necessarily required to identify them, potentially allowing the method to be applied directly to experimental expression data

Analyzing software data bindings in large-scale systems

One central feature of the structure of a software system is the coupling among its components (e.g., subsystems. modules) and the cohesion within them. The purpose of this study is to quantify ratios of coupling and cohesion and use them in the generation of hierarchical system descriptions. The ability of the hierarchical descriptions to localize errors by identifying error-prone system structure is evaluated using actual error data. Measures of data interaction, called data bindings, are used as the basis for calculating software coupling and cohesion. A 135,000 source line system from a production environment has been selected for empirical analysis. Software error data was collected from high-level system design through system test and from some field operation of the system. A set of five tools is applied to calculate the data bindings automatically, and cluster analysis is used to determine a hierarchical description of each of the system's 77 subsystems. An analysis of variance model is used to characterize subsystems and individual routines that had either many/few errors or high/low error correction effort

Understanding and valuing the economic, social and environmental components of System Harmonisation

The aim of the Products and Markets component of the System Harmonisation project is to value the economic and environmental outcomes from an irrigation scheme that is operated by and in the interests of society. In this conceptual note the thinking underlying this component of the project are outlined. The aim of this note is to provide elements for debated. The nature and requirements of System Harmonisation demands that a 'systems approach' be taken throughout the project. What becomes important within this approach is how the different elements within a system are isolated and yet linked with one another. In many instances the extent and nature of irrigation systems are defined by the relevant Regional Irrigation Business Partnership (RIBP) under investigation. It is recognised that society has multiple uses for the water (agriculture, industry, households, recreation and the environment) as well as non-use (intrinsic) values for which it derives benefits from and incurs costs in distributing the water in any select manner. Further, it is assumed that the irrigation schemes are run for the benefit of society as a whole. Thus, there is a necessity to evaluate both the private and public costs and benefits associated with irrigation schemes. In order to identify what society values from an irrigation scheme, it is argued that a social matrix approach is needed. This analysis allows for a clustering of the issues people feel is important to them regarding the use of an irrigation scheme. Such an analysis will allow identification of the perceived most and least beneficial activities connected to water allocation, economic modelling of the most productive activities, evaluation of externalities and Cost Benefit Analysis. The net economic benefits that arise from irrigation need to be evaluated. The sectors where benefits are derived can be segregated into agriculture, households, the environment, recreation and industrial uses. The largest of these, by pure scale of the use of water, is agriculture. A gross margins approach is used to evaluate the returns for water in the agricultural sector. In the industrial and household sectors, a simple evaluation approach is used where the quantity of water demanded is multiplied by the price paid in each sector. Non-market valuation techniques are used to evaluate the recreational and environmental uses of water. The difficulty that arises in this analysis is how to evaluate the performance of irrigation schemes, where the outcomes are multifaceted. A 'meta' model approach is suggested in which the different elements from the project are brought together and assessed using a technique derived from the theory surrounding production possibility frontiers. This technique can be used to hypothesise a value for the ecosystem services derived from an irrigation scheme. The performance of an irrigation scheme is evaluated in terms of the suggestions raised to change it. Cost Effective Analysis is to be utilised to evaluate this performance. Then two issues need to be addressed. First, it is necessary to converse with those from other components, particularly those involved in the hydrological programs, to determine the nature of the schemes to be investigated. Second, it is necessary to implement the approach in each of the RIBPs. This work needs to commence with the evaluation of the social values in each region

Systemic capabilities: the source of IT business value

Purpose – The purpose of this paper is to develop, and explicate the significance of the need for a systemic conceptual framework for understanding IT business value. Design/methodology/approach – Embracing a systems perspective, this paper examines the interrelationship between IT and other organisational factors at the organisational level and its impact on the business value of IT. As a result, a systemic conceptual framework for understanding IT business value is developed. An example of enhancing IT business value through developing systemic capabilities is then used to test and demonstrate the value of this framework. Findings – The findings suggest that IT business value would be significantly enhanced when systemic capabilities are generated from the synergistic interrelations among IT and other organisational factors at the systems level, while the system’s human agents play a critical role in developing systemic capabilities by purposely configuring and reconfiguring organisational factors. Practical implications – The conceptual framework advanced provides the means to recognise the significance of the need for understanding IT business value systemically and dynamically. It encourages an organisation to focus on developing systemic capabilities by ensuring that IT and other organisational factors work together as a synergistic whole, better managing the role its human agents play in shaping the systems interrelations, and developing and redeveloping systemic capabilities by configuring its subsystems purposely with the changing business environment. Originality/value – This paper reveals the nature of systemic capabilities underpinned by a systems perspective. The resultant systemic conceptual framework for understanding IT business value can help us move away from pairwise resource complementarity to focusing on the whole system and its interrelations while responding to the changing business environment. It is hoped that the framework can help organisations delineate important IT investment considerations and the priorities that they must adopt to create superior IT business value

Continuity in cognition

Designing for continuous interaction requires designers to consider the way in which human users can perceive and evaluate an artefact’s observable behaviour, in order to make inferences about its state and plan, and execute their own continuous behaviour. Understanding the human point of view in continuous interaction requires an understanding of human causal reasoning, of the way in which humans perceive and structure the world, and of human cognition. We present a framework for representing human cognition, and show briefly how it relates to the analysis of structure in continuous interaction, and the ways in which it may be applied in design