Literature-related discovery (LRD): Water purification

Literature-related discovery (LRD) is the linking of two or more literature concepts that have heretofore not been linked (i.e., disjoint), in order to produce novel, interesting, plausible, and intelligible knowledge (i.e., potential discovery). LRD has two main components that differ in their methodological approach to discovery: Literature-based discovery (LBD) produces potential discovery through analysis of the technical literature alone; Literature-assisted discovery (LAD) produces potential discovery through both analysis of the technical literature and use of selected authors of that literature. These authors generate potential discovery as proposers, workshop/panel participants, or in other active roles.The open discovery systems (ODS) component of LRD starts with a problem to be solved, and generates solutions to that problem through potential discovery. We have been using ODS LRD to identify potential treatments or preventative actions for challenging medical problems, among myriad other applications. The previous four papers in this Special Issue describe the application of ODS LRD (specifically, the ODS LBD variant) to Raynaud's Phenomenon (RP), cataracts, Parkinson's Disease (PD), and Multiple Sclerosis (MS).One goal of the present study was to determine whether LRD could be successfully applied (for the first time) to a challenging non-medical technical problem to generate potential discovery. The second goal was to explore the use of both LRD variants (LBD and LAD) to a non-medical technical problem. We selected the problem of water purification (WP) because of universal applicability and sponsor interest.We used LRD to identify purification concepts, technology components and systems that could lead to improved water purification techniques. We accessed many disparate disciplines to identify purification concepts from literatures not normally associated with water purification. We used two LBD approaches, Cluster Filtering and Latent Semantic Indexing (LSI), to search for potential discovery. We generated voluminous amounts of potential discovery, and believe we have only scratched the surface of what is possible. We also ran a short experiment using LAD to identify experts associated with potential discovery concepts, and use their expertise to generate potential discovery for water purification.     

Literature-related discovery (LRD): Lessons learned, and future research directions

Literature-related discovery (LRD) is the linking of two or more literature concepts that have heretofore not been linked (i.e., disjoint), in order to produce novel, interesting, plausible, and intelligible knowledge (i.e., potential discovery). The open discovery systems (ODS) component of LRD starts with a problem to be solved, and generates solutions to that problem through potential discovery. We have been using ODS LRD to identify potential treatments or preventative actions for challenging medical problems, among myriad other applications. The five immediately preceding papers in this Special Issue describe the application of ODS LRD to Raynaud's Phenomenon (RP), cataracts, Parkinson's Disease (PD), Multiple Sclerosis (MS), and Water Purification (WP). We describe the lessons learned from each application, and how the techniques can be improved further.Generation of much potential discovery using ODS LRD is possible when the conceptual roadblocks to discovery are removed. Some of these roadblocks include use of numerical filters that are unrelated to generating discovery, and excessive reliance on literatures directly related to the problem literature of interest. The issue of how to handle large amounts of potential discovery has not been addressed in the literature, since most ODS LRD researchers have tried to find a relatively few potential discovery items. We present a development strategy that capitalizes on the large amounts of potential discovery we have identified.     

Literature-Related Discovery (LRD): Introduction and background

Discovery in science is the generation of novel, interesting, plausible, and intelligible knowledge about the objects of study. Literature-related discovery (LRD) is the linking of two or more literature concepts that have heretofore not been linked (i.e., disjoint), in order to produce novel, interesting, plausible, and intelligible knowledge (i.e., potential discovery). LRD has two main components that differ in their methodological approach to discovery:

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Literature-based discovery (LBD) produces potential discovery through analysis of the technical literature alone.

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Literature-assisted discovery (LAD) produces potential discovery through both analysis of the technical literature and use of selected authors of that literature. These authors generate potential discovery as proposers, workshop/panel participants, or in other active roles.

LRD offers the promise of large amounts of potential discovery, for the following reasons:

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the burgeoning technical literature contains a very large pool of technical concepts in myriad technical areas;

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researchers spend full time trying to cover the literature in their own research fields and are relatively unfamiliar with research in other especially disparate fields of research;

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the large number of technical concepts (and disparate technical concepts) means that many combinations of especially disparate technical concepts exist

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by the laws of probability, some of these combinations will produce novel, interesting, plausible, and intelligible knowledge about the objects of study.

This Special Issue presents the LRD methodology and voluminous discovery results from five problem areas: four medical (treatments for Parkinson's Disease (PD), Multiple Sclerosis (MS), Raynaud's Phenomenon (RP), and Cataracts) and one non-medical (Water Purification (WP)). In particular, the open discovery systems (ODS) aspect of LRD (start with problem, generate potential solution(s), or vice versa) is addressed, rather than the closed discovery systems (CDS) aspect (start with problem and potential solution(s), generate linking mechanism(s)). In the presentation of potential discovery, a ‘vetting’ process is used that insures both requirements for ODS LBD are met: concepts are linked that have not been linked previously, and novel, interesting, plausible, and intelligible knowledge is produced.The potential discoveries for the PD, MS, Cataracts, and WP problems are the first we have seen reported by this ODS LBD approach, and the numbers of potential discoveries for the ODS LBD benchmark RP problem are almost two orders of magnitude greater than those reported in the open literature by any other ODS LBD researcher who has addressed this benchmark RP problem. The WP problem is the first non-medical technical topic to have been addressed successfully by ODS LBD.In all cases, but especially the medical, we have barely scratched the surface of quantity and quality of potential discovery that could be generated with adequately resourced studies. Based on the many potential discoveries we have obtained, and the promise of far more potential discoveries with adequately resourced studies, we have generated a new paradigm relative to discovery: while the key challenge in traditional discovery is finding a needle-in-a-haystack, the key challenge in ODS LRD (used appropriately) is handling the overwhelming amount of potential discovery available.Additionally, it is our thesis, as the specific ODS LBD studies will demonstrate, that synergistic combinations of our mainly individual potential discoveries are themselves potential discoveries. We demonstrate throughout this Special Issue the synergistic effects of combining a very few potential discoveries or interesting core literature concepts, and believe that these synergistic benefits are operable at larger scales of combination. In the final lessons-learned paper of this Special Issue, we also show that providing evidence for the synergistic benefits of large numbers of potential discoveries or interesting core concepts is very difficult due to the large numbers of potential combinations involved.One variant of the LAD operational mode (identifying disparate discipline recipients for Broad Agency Announcement (BAA) notifications in order to stimulate proposals of new ideas from these disparate disciplines) is presented for WP. Other possible applications of LAD include:

1.

Recipients of solicitation announcements (other solicitations similar to BAA, journal Special Issue calls for papers, etc),

2.

Participants in Workshops, Advisory Panels, Review Panels, Roadmaps, and War Games,

3.

Points of Contact for Field Science Advisors, Foreign Field Offices, Program Officer site visits, and potential transitions.

     

Literature-Related Discovery (LRD): Potential treatments for Parkinson's Disease

Literature-Related Discovery (LRD) is the linking of two or more literature concepts that have heretofore not been linked (i.e., disjoint), in order to produce novel, interesting, plausible, and intelligible knowledge (i.e., potential discovery). The open discovery systems (ODS) component of LRD starts with a problem to be solved, and generates solutions to that problem through potential discovery. We have been using ODS LRD to identify potential treatments or preventative actions for challenging medical problems, among myriad other applications. The previous two papers in this Special Issue describe the application of ODS LRD to Raynaud's Phenomenon (RP) and to cataracts.Parkinson's Disease (PD) is a progressive neurodegenerative disorder, affecting approximately 1% of individuals older than 60 years, and is characterized by resting tremor, rigidity, bradykinesia, and postural instability. We selected the subject of PD because of its global prevalence, and its apparent intractability to all treatments except for palliative remediation mainly through drugs or surgery.Our first goal was to identify non-drug non-surgical treatments that would 1) prevent the occurrence, or 2) reduce the progression rate, or 3) stop the progression, or 4) maybe even reverse the progression, of PD. Our second goal was to demonstrate that we could again solve an ODS problem (using LRD) with no prior knowledge of any results or prior work (unlike the case of the RP problem). As in the ‘cataract’ example, we used the MeSH taxonomy of MEDLINE to restrict potential discoveries to selected semantic classes, and to identify potential discoveries efficiently. Our third goal was to generate large amounts of potential discovery in more than an order of magnitude less time than required for the RP study. The discovery generation methodology has been developed to the point where ODS LRD problems can be solved with no results or knowledge of any prior work.     

Literature-related discovery (LRD): Potential treatments for Multiple Sclerosis

Literature-related discovery (LRD) is the linking of two or more literature concepts that have heretofore not been linked (i.e., disjoint), in order to produce novel, interesting, plausible, and intelligible knowledge (i.e., potential discovery). The open discovery systems (ODS) component of LRD starts with a problem to be solved, and generates solutions to that problem through potential discovery. We have been using ODS LRD to identify potential treatments or preventative actions for challenging medical problems, among myriad other applications. The previous three papers in this Special Issue describe the application of ODS LRD to Raynaud's Phenomenon (RP), cataracts, and Parkinson's Disease (PD).Multiple Sclerosis (MS) is a progressive neurodegenerative disorder (typically preceded by periods of remission and relapse), affecting mainly people in their early-mid life. MS is characterized by changes in sensation (hypoesthesia), muscle weakness, abnormal muscle spasms, or difficulty to move; difficulties with coordination and balance (ataxia); problems in speech (Dysarthria) or swallowing (Dysphagia), visual problems (Nystagmus, optic neuritis, or diplopia), fatigue and acute or chronic pain syndromes, bladder and bowel difficulties, cognitive impairment, or emotional symptomatology (mainly depression).We selected the subject of MS because of its global prevalence, and its apparent intractability to all treatments except for palliative remediation mainly through drugs or surgery. Our first goal was to identify non-drug non-surgical treatments that would 1) prevent the occurrence, or 2) reduce the progression rate, or 3) stop the progression, or 4) maybe even reverse the progression, of MS. Our second goal was to demonstrate that we could again solve an ODS problem (using LRD) with no prior knowledge of any results or prior work (unlike the case of the RP problem). As in the ‘cataract’ and PD examples, we used the MeSH taxonomy of MEDLINE to restrict potential discoveries to selected semantic classes, and to identify potential discoveries efficiently. Our third goal was to generate large amounts of potential discovery in more than an order of magnitude less time than required for the RP study. The discovery generation methodology has been developed to the point where ODS LRD problems can be solved with no results or knowledge of any prior work.     

Literature-related discovery (LRD): Potential treatments for cataracts

Literature-related discovery (LRD) is the linking of two or more literature concepts that have heretofore not been linked (i.e., disjoint), in order to produce novel, interesting, plausible, and intelligible knowledge (i.e., potential discovery). The open discovery systems (ODS) component of LRD starts with a problem to be solved, and generates solutions to that problem through potential discovery. We have been using ODS LRD to identify potential treatments or preventative actions for challenging medical problems, among myriad other applications.This paper describes the second medical problem we addressed (cataract) using ODS LRD; the first problem addressed was Raynaud's Phenomenon (RP), and was described in the third paper of this Special Issue. Cataract was selected because it is ubiquitous globally, appears intractable to all forms of treatment other than surgical removal of cataracts, and is a major cause of blindness in many developing countries.The ODS LRD study had three objectives: a) identify non-drug non-surgical treatments that would 1) help prevent cataracts, or 2) reduce the progression rate of cataracts, or 3) stop the progression of cataracts, or 4) maybe even reverse the progression of cataracts; b) demonstrate that we could solve an ODS LRD problem with no prior knowledge of any results or prior work (unlike the case with the RP problem); c) determine whether large time savings in the discovery process were possible relative to the time required for conducting the RP study. To that end, we used the MeSH taxonomy of MEDLINE to restrict potential discoveries to selected semantic classes, as a substitute for the manually-intensive process used in the RP study to restrict potential discoveries to selected semantic classes. We also used additional semantic filtering to identify potential discovery within the selected semantic classes.All these goals were achieved. As will be shown, we generated large amounts of potential discovery in more than an order of magnitude less time than required for the RP study. We identified many non-drug non-surgical treatments that may be able to reduce or even stop the progression rate of cataracts. Time, and much testing, will determine whether this is possible. Finally, the methodology has been developed to the point where ODS LRD problems can be solved with no results or knowledge of any prior work.     

Literature-related discovery (LRD): Potential treatments for Raynaud's Phenomenon

Literature-related discovery (LRD) is the linking of two or more literature concepts that have heretofore not been linked (i.e., disjoint), in order to produce novel, interesting, plausible, and intelligible knowledge (i.e., potential discovery). The open discovery systems (ODS) component of LRD starts with a problem to be solved, and generates solutions to that problem through potential discovery. We have been using ODS LRD to identify potential treatments or preventative actions for challenging medical problems, among myriad other applications.Raynaud's Phenomenon (RP) is a condition in which small arteries, most commonly in fingers and toes, contract and cause the skin to turn pale or a patchy red to blue. We selected the subject of RP for analysis by LRD because of RP's global prevalence, and its apparent intractability to all treatments except for palliative remediation mainly through drugs or surgery. Our main goal was to identify non-drug non-surgical treatments that would 1) prevent the occurrence, or 2) reduce the progression rate, or 3) stop the progression, or 4) maybe even reverse the progression, of RP. Our secondary goal was to compare our ODS LRD approach to the RP problem with other investigators who have addressed the RP problem since Swanson's pioneering 1986 ODS LRD paper on potential RP treatments [D.R. Swanson, Fish oil, Raynauds syndrome, and undiscovered public knowledge, Perspectives in Biology and Medicine 30 (1) (1986) 7-18].We used Medline from 1965-1985 to identify potential discovery for RP. We differ from all the other authors who have addressed this problem in two major respects: we make no numerically-based filtering assumptions, and we generate substantial potential discovery (∼ 130 potential discoveries). Further, we believe our reported results are the tip of the iceberg. Much more potential discovery is possible with an adequately resourced study using the lessons learned from this demonstration study and the other demonstration studies that follow in this Special Issue.     

Literature-related discovery: Potential treatments and preventatives for SARS

Literature-related discovery (LRD) is the linking of two or more previously disjoint concepts in order to produce novel, interesting, plausible, and intelligible connections (i.e., potential discovery). LRD has been used to identify potential treatments or preventative actions for challenging medical problems, among myriad other applications.Severe acute respiratory syndrome (SARS) was the first pandemic of the 21st century. SARS was eventually controlled through increased hygienic measures (e.g., face mask protection, frequent hand washing, living quarter disinfection), travel restrictions, and quarantine. According to recent reviews of SARS, none of the drugs that were used during the pandemic worked.For the present paper, SARS was selected as the first application of LRD to an infectious disease. The main goal of this research was to identify non-drug non-surgical treatments that would 1) prevent the occurrence, or 2) reduce the progression rate, or 3) stop/reverse the progression of SARS. The MeSH taxonomy of Medline was used to restrict potential discoveries to selected semantic classes, and to identify potential discoveries efficiently. To enhance the volume of potential discovery, databases were used in addition to Medline. These included the Science Citation Index (SCI) and, in contrast to previous work, a full text database. Because of the richness of the full text, ‘surgical’ queries were developed that targeted the exact types of potential discovery of interest while eliminating clutter more efficiently.     

Systematic acceleration of radical discovery and innovation in science and technology

A systematic two-component approach (front-end component, back-end component) to bridging unconnected disciplines and accelerating potentially radical discovery and innovation (based wholly or partially on text mining procedures) is presented. The front-end component has similar objectives to those in the classical literature-based discovery (LBD) approach, although it is different mechanistically and operationally. The front-end component will systematically identify technical disciplines (and their associated leading experts) that are directly or indirectly-related to solving technical problems of high interest. The back-end component is actually a family of back-end techniques, only one of which shares the strictly literature-based analysis of the classical LBD approach. The non-LBD back-end techniques (literature-assisted discovery) make use of the human experts associated with the disparate literatures (disciplines) uncovered in the front-end to generate radical discovery and innovation.Specifically, in the literature-assisted discovery operational mode, these disparate discipline experts could be used as:

1. Recipients of solicitation announcements (BAA, SBIR, MURI, journal Special Issue calls for papers, etc.),

2. Participants in Workshops, Advisory Panels, Review Panels, Roadmaps, and War Games,

3. Points of Contact for Field Science Advisors, Foreign Field Offices, Program Officer site visits, and potential transitions.

Learning-by-doing or habit formation?

In a recent paper, Chang et al. [2002. Learning-by-doing as a propagation mechanism. American Economic Review 92 (5) 1498–1520] extend the standard real business cycle (RBC) model to allow for a learning-by-doing (LBD) mechanism whereby current labor supply affects future productivity. They show that this feature magnifies the propagation of shocks and improves the matching performance of the standard RBC model. In this paper, we show that the LBD model is nearly observationally equivalent to an RBC model with habit formation in labor (or, equivalently, in leisure). Under the same calibration of the parameters, the two models share the same equilibrium paths of output, consumption, and investment, but have different implications for hours worked. Using Bayesian techniques, we investigate which of the LBD and Habit models fits the US data better. Our results suggest that the Habit specification is more strongly supported by the data.     

Productivity Growth and Input Demand: The Effect of Learning by Doing in a Gold Mining Firm in a Developing Economy

This paper analyzes the effect of learning by doing (LBD) on the firm’s productivity growth and its input demand decisions. The results indicate that LBD is an important determinant of the firm’s productivity growth. The contribution of LBD to the firm’s productivity growth is about 5.6%. Another observation is that LBD has a decreasing effect on the firm’s cost of production – a finding which is consistent with the results of many studies. Also, an increase in LBD measured by cumulative production increases the firm’s demand for capital, and decreases the firm’s demand for labor. Lastly, LBD has a significant effect on the firm’s elasticity of scale. A fundamental message derived from the study is the confirmation that the firms should invest in more large capital equipment, embark on new processing techniques, and create an environment that is conducive to on-the-job learning.     

Organizational form and output quality

This paper re‐examines the relationship between a firm's organizational form, not‐for‐profit versus for‐profit, and its output quality. The Arrow‐Hansmann theory of hidden action on the part of providers predicts higher quality for not‐for‐profit suppliers. This prediction has a puzzling lack of support in the empirical literature. We propose a theory that resolves the empirical puzzle and generates additional testable implications. The theory starts with the traditional assumptions of hidden action and supplier altruism. It then incorporates two additional features of real‐world markets: hidden information on supplier ability to provide high quality and a variation across buyers in the degree of informational asymmetry. The central prediction of the theory is that quality has a higher variance across for‐profits than across not‐for‐profits. Preliminary evidence from the US market for hospital care is consistent with this prediction.     

FDI and productivity growth: the role of inter‐industry linkages

Abstract There is a large literature on the effects of foreign direct investment (FDI) on productivity through inter‐industry economic linkages. This paper contributes to the literature by focusing on the developed economy of Canada. It finds that FDI generates strong effects on total factor productivity (TFP) growth through both forward and backward inter‐industry linkages, and increase in an industry's absorptive capacity raises the effects of FDI on TFP growth through forward inter‐industry linkages. For R&D intensive industries, the effects of FDI on TFP growth through inter‐industry linkages are small, but imports turn out to be an important source for TFP growth.     

REAL EXCHANGE RATE,MERCANTILISM AND THE LEARNING BY DOING EXTERNALITY

This paper examines the degree to which the learning by doing (LBD) externality calls for an undervalued exchange rate. We obtain mixed results. For an economy where the LBD externality operates in the traded sector, real exchange rate undervaluation may be used to internalize this externality, if the LBD calls for subsidizing employment in the traded sector. If the LBD externality is embodied in aggregate investment, the optimal policy calls for subsidizing the cost of capital in the traded sector, and there is no room for undervalued exchange rate policy.     

Learning-by-doing and input demand of a rate-of-return regulated firm

The significance of learning by doing to input demand of a cost-minimizing rate-of-return-regulated firm is examined. Using a panel data, the results indicate that the firm's cost and input demand decisions are both influenced by learning-by-doing. The firm's cost and the rate-base (capital) input requirements decline as learning-by-doing measured by cumulative production expands. However, LBD may have different effects on the non-rate-base inputs (labor and fuel) considered in this study. While LBD ambiguously reduces fuel usage, it moderately increases labor employment. In addition to changing input intensity, LBD also influences returns to scale and elasticity of substitution.     

Resource discovery and the politics of fiscal decentralization

If the central government is a revenue maximizing Leviathan then resource discovery and democratization should have discernible impacts on the degree of fiscal decentralization. We systematically explore these effects by exploiting exogenous variation in giant oil and mineral discoveries and permanent democratization. Using a global dataset of 77 countries over the period 1970–2012 we find that resource discovery has very little effect on revenue decentralization but induces expenditure centralization. Oil discovery appears to be the main driver of centralization and not minerals. Resource discovery leads to centralization in locations which have not experienced permanent democratization. Tax and intergovernmental transfers respond most to resource discovery shocks and democratization whereas own source revenue, property tax, educational expenditure, and health expenditure do not seem to be affected. Higher resource rent leads to more centralization and the effect is moderated by democratization.     

An Incomplete Markets Explanation of the Uncovered Interest Rate Parity Puzzle

A large literature attributes failure of uncovered interest rate parity (UIP) to the existence of a time‐varying risk premium. This paper presents a mechanism in a simple two‐country two‐good endowment economy with incomplete markets that generates sizeable deviations from UIP. In a parameterization where international wealth effects are important, liquidity constraints on an internationally traded bond and agents’ strong resulting precautionary motives successfully generates a time‐varying risk premium: countries that have accumulated large outstanding external positions have, being closer to the constraints, stronger precautionary motives and their asset carries a risk premium.     

Financing Public Investment through Foreign Aid: Consequences for Economic Growth and Welfare*

The paper develops a theoretical framework for understanding the mechanism through which foreign aid affects macroeconomic performance. The authors find that the long‐run impact of an aid program and the nature of the transitional dynamics it generates depend crucially on (i) the elasticity of substitution in production, (ii) whether the aid flow is tied to investment activity or not, (iii) how the recipient government chooses to react to the flow of external assistance, and (iv) whether the aid program is permanent or temporary. Structural characteristics of the recipient are important in determining the extent to which external assistance can aid growth and welfare.     

Demand regimes and income distribution reconsidered in an open economy portfolio balance framework

A large body of neo-Kaleckian literature has debated the distributional determinants of demand and growth. One general conclusion has been that open economy considerations weaken the potential for a wage-led growth regime. However, this literature has largely ignored asset portfolio considerations and the stock and flow interactions that result from the feedback from savings to wealth and from wealth to the current account. This study develops a theoretical framework that specifies a fuller system of (instantaneous) flow equilibria embedded in a medium-run framework with stable steady-state stocks of real and financial assets. The balance-of-payments constraint that results ensures that simply raising the wage does not yield a higher stock of real capital. A lower markup may increase the steady-state stock of capital but only through the relative price channel. These results are much stronger than those derived in the existing literature, and more important, emerge regardless of whether the demand regime is wage-led or profit-led in the absence of international trade.