From patterned response dependency to structured covariate dependency: categorical-pattern-matching

Data generated from a system of interest typically consists of measurements from an ensemble of subjects across multiple response and covariate features, and is naturally represented by one response-matrix against one covariate-matrix. Likely each of these two matrices simultaneously embraces heterogeneous data types: continuous, discrete and categorical. Here a matrix is used as a practical platform to ideally keep hidden dependency among/between subjects and features intact on its lattice. Response and covariate dependency is individually computed and expressed through mutliscale blocks via a newly developed computing paradigm named Data Mechanics. We propose a categorical pattern matching approach to establish causal linkages in a form of information flows from patterned response dependency to structured covariate dependency. The strength of an information flow is evaluated by applying the combinatorial information theory. This unified platform for system knowledge discovery is illustrated through five data sets. In each illustrative case, an information flow is demonstrated as an organization of discovered knowledge loci via emergent visible and readable heterogeneity. This unified approach fundamentally resolves many long standing issues, including statistical modeling, multiple response, renormalization and feature selections, in data analysis, but without involving man-made structures and distribution assumptions. The results reported here enhance the idea that linking patterns of response dependency to structures of covariate dependency is the true philosophical foundation underlying data-driven computing and learning in sciences.Comment: 32 pages, 10 figures, 3 box picture

Partial information decomposition as a unified approach to the specification of neural goal functions

In many neural systems anatomical motifs are present repeatedly, but despite their structural similarity they can serve very different tasks. A prime example for such a motif is the canonical microcircuit of six-layered neo-cortex, which is repeated across cortical areas, and is involved in a number of different tasks (e.g. sensory, cognitive, or motor tasks). This observation has spawned interest in finding a common underlying principle, a ‘goal function’, of information processing implemented in this structure. By definition such a goal function, if universal, cannot be cast in processing-domain specific language (e.g. ‘edge filtering’, ‘working memory’). Thus, to formulate such a principle, we have to use a domain-independent framework. Information theory offers such a framework. However, while the classical framework of information theory focuses on the relation between one input and one output (Shannon’s mutual information), we argue that neural information processing crucially depends on the combination of multiple inputs to create the output of a processor. To account for this, we use a very recent extension of Shannon Information theory, called partial information decomposition (PID). PID allows to quantify the information that several inputs provide individually (unique information), redundantly (shared information) or only jointly (synergistic information) about the output. First, we review the framework of PID. Then we apply it to reevaluate and analyze several earlier proposals of information theoretic neural goal functions (predictive coding, infomax and coherent infomax, efficient coding). We find that PID allows to compare these goal functions in a common framework, and also provides a versatile approach to design new goal functions from first principles. Building on this, we design and analyze a novel goal function, called ‘coding with synergy’, which builds on combining external input and prior knowledge in a synergistic manner. We suggest that this novel goal function may be highly useful in neural information processing

Partial Information Decomposition as a Unified Approach to the Specification of Neural Goal Functions

In many neural systems anatomical motifs are present repeatedly, but despite their structural similarity they can serve very different tasks. A prime example for such a motif is the canonical microcircuit of six-layered neo-cortex, which is repeated across cortical areas, and is involved in a number of different tasks (e.g.sensory, cognitive, or motor tasks). This observation has spawned interest in finding a common underlying principle, a 'goal function', of information processing implemented in this structure. By definition such a goal function, if universal, cannot be cast in processing-domain specific language (e.g. 'edge filtering', 'working memory'). Thus, to formulate such a principle, we have to use a domain-independent framework. Information theory offers such a framework. However, while the classical framework of information theory focuses on the relation between one input and one output (Shannon's mutual information), we argue that neural information processing crucially depends on the combination of \textit{multiple} inputs to create the output of a processor. To account for this, we use a very recent extension of Shannon Information theory, called partial information decomposition (PID). PID allows to quantify the information that several inputs provide individually (unique information), redundantly (shared information) or only jointly (synergistic information) about the output. First, we review the framework of PID. Then we apply it to reevaluate and analyze several earlier proposals of information theoretic neural goal functions (predictive coding, infomax, coherent infomax, efficient coding). We find that PID allows to compare these goal functions in a common framework, and also provides a versatile approach to design new goal functions from first principles. Building on this, we design and analyze a novel goal function, called 'coding with synergy'. [...]Comment: 21 pages, 4 figures, appendi

How Supervisors Influence Performance: A Multilevel Study of Coaching and Group Management in Technology-Mediated Services

This multilevel study examines the role of supervisors in improving employee performance through the use of coaching and group management practices. It examines the individual and synergistic effects of these management practices. The research subjects are call center agents in highly standardized jobs, and the organizational context is one in which calls, or task assignments, are randomly distributed via automated technology, providing a quasi-experimental approach in a real-world context. Results show that the amount of coaching that an employee received each month predicted objective performance improvements over time. Moreover, workers exhibited higher performance where their supervisor emphasized group assignments and group incentives and where technology was more automated. Finally, the positive relationship between coaching and performance was stronger where supervisors made greater use of group incentives, where technology was less automated, and where technological changes were less frequent. Implications and potential limitations of the present study are discussed

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