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.     

An experimental analysis of team production in networks

Experimental and empirical evidence highlights the role of networks on social outcomes. This paper tests the properties of exogenously fixed networks in team production. Subjects make the same decisions in a team work environment under four different organizational networks: the line, the circle, the star, and the complete network. In all the networks, links make information available to neighbors. This design allows us to analyze decisions across networks and a variety of subject types in a standard linear team production game. Contribution levels differ significantly across networks and the star is the most efficient incomplete network. Moreover, our results suggest that subjects act as conditional cooperators with respect to the information received from the network.     

Welfare reducing licensing

In this paper, we characterize situations where licensing a cost reducing innovation to a rival firm using two-part tariff contracts (a fixed fee plus a linear per unit of output royalty) reduces social welfare. We show that it occurs if (i) the firms compete in prices, (ii) the innovation is large enough but not drastic, and (iii) the goods are close enough substitutes. Moreover, we show that, regardless of the type of competition, first, the optimal contract always includes a positive royalty and, second, even drastic innovations are licensed whenever the goods are not homogeneous.     

Growth Empirics in Panel Data Under Model Uncertainty and Weak Exogeneity

This paper considers panel growth regressions in the presence of model uncertainty and reverse causality concerns. For this purpose, my econometric framework combines Bayesian model averaging with a suitable likelihood function for dynamic panel models with weakly exogenous regressors and fixed effects. An application of this econometric methodology to a panel of countries over the 1960–2000 period highlights the difficulties in identifying the sources of economic growth by means of cross‐country regressions. In particular, none of the nine candidate regressors considered can be labeled as a robust determinant of economic growth. Moreover, the estimated rate of conditional convergence is indistinguishable from zero. Copyright © 2014 John Wiley & Sons, Ltd.     

ORGANIZATIONAL CONTEXT AND THE INTERPRETATION OF STRATEGIC ISSUES: A NOTE ON CEOS’ INTERPRETATIONS OF FOREIGN INVESTMENT*

This paper explores the relationship between organizational context and the interpretation of strategic issues by examining the hypothesis that CEOs' interpretations of foreign investment in the USA are influenced by the organizational context in which they are embedded. Three aspects of organizational context - the global business experience of the firm; the firm's level of organizational inertia (as represented by firm age and size); and the resources available for responding - are examined as predictors of CEOs' perceptions of foreign investment as a threat or an opportunity. Analysis of data from 320 organizations, controlled by industry, shows that global business experience, firm size, and perceived capability are significant predictors of the perception of threat and opportunity. the discussion addresses the implications of these findings for future research on issue interpretation and organizational context.     

Spanning tests in return and stochastic discount factor mean–variance frontiers: A unifying approach

We propose new spanning tests that assess if the initial and additional assets share the economically meaningful cost and mean representing portfolios. We prove their asymptotic equivalence to existing tests under local alternatives. We also show that unlike two-step or iterated procedures, single-step methods such as continuously updated GMM yield numerically identical overidentifying restrictions test, so there is arguably a single spanning test. To prove these results, we extend optimal GMM inference to deal with singularities in the long run second moment matrix of the influence functions. Finally, we test for spanning using size and book-to-market sorted US stock portfolios.     

EXCHANGE RATE FUNDAMENTALS,FORECASTING, AND SPECULATION: BAYESIAN MODELS IN BLACK MARKETS

Although speculative activity is central to black markets for currency, the out‐of‐sample performance of structural models in those settings is unknown. We substantially update the literature on empirical determinants of black market rates and evaluate the out‐of‐sample performance of linear models and non‐parametric Bayesian treed Gaussian process (BTGP) models against the random walk benchmark. Fundamentals‐based models outperform the benchmark in out‐of‐sample prediction accuracy and trading rule profitability measures given future values of fundamentals. In simulated real‐time trading exercises, however, the BTGP achieves superior realized profitability, accuracy and market timing, while linear models do no better than a random walk. Copyright © 2013 John Wiley & Sons, Ltd.     

Modeling population dynamics and economic growth as competing species: An application to CO2 global emissions

Since the beginning of the last century the world is experiencing an important demographic transition, which will probably impact on economic growth. Many demographers and social scientists are trying to understand the key drivers of such transition as well as its profound implications. A correct understanding will help to predict other important trends of the world primary energy demand and the carbon emission to the atmosphere, which may be leading to an important climate change. This paper proposes a set of coupled differential equations to describe the changes of population, gross domestic product, primary energy consumption and carbon emissions, modeled as competing species as in Lotka-Volterra prey-predator relations. The predator-prey model is well known in the biological, ecological and environmental literature and has also been applied successfully in other fields. This model proposes a new and simple conceptual explanation of the interactions and feedbacks among the principal driving forces leading to the present transition. The estimated results for the temporal evolution of world population, gross domestic product, primary energy consumption and carbon emissions are calculated from year 1850 to year 2150. The calculated scenarios are in good agreement with common world data and projections for the next 100 years.     

TFP growth and its determinants: a model averaging approach

Total Factor Productivity (TFP) accounts for a sizable proportion of the income differences across countries. Two challenges remain to researchers aiming to explain these differences: on the one hand, TFP growth is hard to measure empirically; on the other hand, model uncertainty hampers consensus on its key determinants. This paper combines a non-parametric measure of TFP growth with Bayesian model averaging techniques in order to address both issues. Our empirical findings suggest that the most robust TFP growth determinants are time-invariant unobserved heterogeneity and trade openness. We also investigate the main determinants of two TFP components: efficiency change (i.e., catching up) and technological progress.