In Pursuit for Impeccable Veracity

I study the institution of avoiding hiring one’s own Ph.D. graduates for assistant professorships. I argue that this institution is necessary to create better incentives for researchers to incorporate new information in studies, facilitating the convergence to asymptotic learning of the studied fundamentals.

Learning and organization in the knowledge-based information economy: Initial findings from a participatory action research case study

This paper reports on an ongoing, multiphase, project-based action learning and research project. In particular, it summarizes some aspects of the learning climate and outcomes for a case study company In the software industry, Using a participatory action research approach, the learning company framework developed by Pedler et al, (1997) is used to initiate critical reflection in the company at three levels: managing director, senior management team and technical and professional staff. As such, this is one of the first systematic attempts to apply this framework to the entire organization and to a company in the knowledge-based learning economy. Two sets of issues are of general concern to the company: internal issues surrounding the company's reward and recognition policies and practices and the provision of accounting and control information in a business relevant way to all levels of staff; and external issues concerning the extent to which the company and its members actively learn from other companies and effectively capture, disseminate and use information accessed by staff in boundary-spanning roles. The paper concludes with some illustrations of changes being introduced by the company as a result of the feedback on and discussion of these issues.

Exploiting a priori time constant ratio information in difference equation two-thermocouple sensor characterisation

The characterization of thermocouple sensors for temperature measurement in varying-flow environments is a challenging problem. Recently, the authors introduced novel difference-equation-based algorithms that allow in situ characterization of temperature measurement probes consisting of two-thermocouple sensors with differing time constants. In particular, a linear least squares (LS) lambda formulation of the characterization problem, which yields unbiased estimates when identified using generalized total LS, was introduced. These algorithms assume that time constants do not change during operation and are, therefore, appropriate for temperature measurement in homogenous constant-velocity liquid or gas flows. This paper develops an alternative ß-formulation of the characterization problem that has the major advantage of allowing exploitation of a priori knowledge of the ratio of the sensor time constants, thereby facilitating the implementation of computationally efficient algorithms that are less sensitive to measurement noise. A number of variants of the ß-formulation are developed, and appropriate unbiased estimators are identified. Monte Carlo simulation results are used to support the analysis.

Selection Power and Selection Labor for Information Retrieval

This study examines the relation between selection power and selection labor for information retrieval (IR). It is the first part of the development of a labor theoretic approach to IR. Existing models for evaluation of IR systems are reviewed and the distinction of operational from experimental systems partly dissolved. The often covert, but powerful, influence from technology on practice and theory is rendered explicit. Selection power is understood as the human ability to make informed choices between objects or representations of objects and is adopted as the primary value for IR. Selection power is conceived as a property of human consciousness, which can be assisted or frustrated by system design. The concept of selection power is further elucidated, and its value supported, by an example of the discrimination enabled by index descriptions, the discovery of analogous concepts in partly independent scholarly and wider public discourses, and its embodiment in the design and use of systems. Selection power is regarded as produced by selection labor, with the nature of that labor changing with different historical conditions and concurrent information technologies. Selection labor can itself be decomposed into description and search labor. Selection labor and its decomposition into description and search labor will be treated in a subsequent article, in a further development of a labor theoretic approach to information retrieval.

Fusion rules for merging uncertain information

In previous papers, we have presented a logic-based framework based on fusion rules for merging structured news reports. Structured news reports are XML documents, where the textentries are restricted to individual words or simple phrases, such as names and domain-specific terminology, and numbers and units. We assume structured news reports do not require natural language processing. Fusion rules are a form of scripting language that define how structured news reports should be merged. The antecedent of a fusion rule is a call to investigate the information in the structured news reports and the background knowledge, and the consequent of a fusion rule is a formula specifying an action to be undertaken to form a merged report. It is expected that a set of fusion rules is defined for any given application. In this paper we extend the approach to handling probability values, degrees of beliefs, or necessity measures associated with textentries in the news reports. We present the formal definition for each of these types of uncertainty and explain how they can be handled using fusion rules. We also discuss the methods of detecting inconsistencies among sources.

A split-combination approach to merging knowledge bases in possibilistic logic

In this paper, we propose an adaptive approach to merging possibilistic knowledge bases that deploys multiple operators instead of a single operator in the merging process. The merging approach consists of two steps: one is called the splitting step and the other is called the combination step. The splitting step splits each knowledge base into two subbases and then in the second step, different classes of subbases are combined using different operators. Our approach is applied to knowledge bases which are self-consistent and the result of merging is also a consistent knowledge base. Two operators are proposed based on two different splitting methods. Both operators result in a possibilistic knowledge base which contains more information than that obtained by the t-conorm (such as the maximum) based merging methods. In the flat case, one of the operators provides a good alternative to syntax-based merging operators in classical logic.

Information used in detecting upcoming collisions.

In four experiments we examined the nature of the information used in judging whether events would or would not give rise to a collision in the near future. Observers were tested in situations depicting approaches between two objects (lateral approaches) and approaches between an object and the point of observation (head-on approaches), with objects moving according to constant deceleration or sinusoidal deceleration patterns. Judgments were found to be based, to a large extent, on the (in)sufficiency of current deceleration to avoid upcoming collision, as specified optically by tau-dot (tau over dot). However, the information specified, by tau (tau), that is the current (first-order) time remaining until contact, was also found to play a significant role. We deduce that judgment of upcoming collision is based on the detection Of T and its evolution over time, suggesting that observers are sensitive to Deltatau rather than to tau over dot itself.

Dynamic Knowledge Creation Through Value Management Teams

With the intention of introducing unique and value-added products to the market, organizations have become more conscious of how to best create knowledge as reported by Ganesh Bhatt in 2000 in 'Information dynamics, learning and knowledge creation in organizations'. Knowledge creation is recognized as having an important role in generating and sustaining a competitive advantage as well as in meeting organizational goals, as reported by Aleda Roth and her colleagues in 1994 in 'The knowledge factory for accelerated learning practices.' One of the successful ingredients of value management (VM) is its utilization of diverse knowledge resources, drawing upon different organizational functions, professional disciplines, and stakeholders, in a facilitated team process. Multidisciplinary VM study teams are viewed as having high potential to innovate due to their heterogeneous nature. This paper looks at one of the VM workshop's major benefits, namely, knowledge creation. A case study approach was used to explore the nature, processes, and issues associated with fostering a dynamic knowledge creation capability within VM teams. The results indicate that the dynamic knowledge creating process is embedded in and influenced by managing team constellation, creating shared awareness, developing shared understanding, and producing aligned action. The catalysts that can speed up the processes are open dialogue and discussion among participants. This process is enhanced by the use of facilitators, skilled at extracting knowledge.

Description and search labor for information retrieval

Selection power is taken as the fundamental value for information retrieval systems. Selection power is regarded as produced by selection labor, which itself separates historically into description and search labor. As forms of mental labor, description and search labor participate in the conditions for labor and for mental labor. Concepts and distinctions applicable to physical and mental labor are indicated, introducing the necessity of labor for survival, the idea of technology as a human construction, and the possibility of the transfer of human labor to technology. Distinctions specific to mental labor, particular between semantic and syntactic labor, are introduced. Description labor is exemplified by cataloging, classification, and database description, can be more formally understood as the labor involved in the transformation of objects for description into searchable descriptions, and is also understood to include interpretation. The costs of description labor are discussed. Search labor is conceived as the labor expended in searching systems. For both description and search labor, there has been a progressive reduction in direct human labor, with its syntactic aspects transferred to technology, effectively compelled by the high relative costs of direct human labor compared to machine processes.