Combinatorial innovation and research strategies: theoretical framework and empirical evidence from two centuries of patent data

I develop a knowledge production function where new ideas are built from combinations of pre- existing elements. Parameters governing the connections between these elements stochastically determine whether a new combination yields a useful idea. Researchers use Bayesian reasoning to update their beliefs about the value of these parameters and thereby improve their selection of viable research projects. The optimal research strategy is a mix of harvesting the ideas that look best, given what researchers currently believe, and performing exploratory research in order to obtain better information about the unknown parameters. Moreover, this model predicts research productivity in any one field declines over time if new elements for combination or new information about underlying parameters are not discovered. I investigate some of these properties using a large dataset, consisting of all US utility patents granted from 1836 to 2012. I use fine-grained technological classifications to show that optimal research in my model is consistent with actual innovation outcomes, and that the model can be used to improve the forecasting of patent activity in different technology classes

Combinatorial innovation, evidence from patent data, and mandated innovation

This dissertation explores the implications of a new model of knowledge production. In my model, researchers have access to a set of primitive knowledge elements that can be combined to form ideas, where a new combination is a new idea. Underlying parameters governing the connections between elements stochastically determine whether a given combination yields a useful idea (some elements tend to work well together, and others do not). These underlying parameters are unknown to researchers, but as they attempt to combine elements and create ideas, they observe signals which they use to improve their beliefs via Bayesian updating. I embed this production function into a simple model of research incentives, where a firms receive a reward for discovering new and useful combinations, but pay a cost to conduct research. I investigate empirically these predictions using a large dataset on US utility patents: all 8.3 million utility patents granted between 1836 and 2012. From this analysis, I find that the probability a pair of knowledge “elements” (now proxied by technology classifications assigned by patent examiners) will be combined in any given year is increasing in the number of past combinations, decreasing over time, and increasing when both elements in the pair are also used with many other elements. These predictions are consistent with my model. The same work also predicts that patenting activity is positively correlated with changes in researcher knowledge about the connections between elements, and negatively correlated with time. Using panel data on 429 technology classes, I find the growth rate of patents is falling over time, but that increases can be forecast from positive changes in connections between elements 1-5 years earlier, even after controlling for numerous other factors. In my second paper, I examine the characteristics of the optimal research strategy for a forward-looking researcher using the above framework. To characterize the optimal strategy, I examine two special cases that permit analytic solutions, as well as a set of 100 numerically solved cases. The optimal research strategy reproduces many stylized facts about the innovation process, including the initial dominance of applied research relative to basic research. The third paper of my dissertation examines the impact of environmental policy choice on innovation, when research is characterized by unobservable (to the policy-maker) variance in technological opportunity. I assume there exist two types of energy, clean and dirty, that are perfect substitutes but for their production costs and a negative externality from dirty energy. Innovators are expected profit maximizers, and their decision to expend resources on R&D depends on technological opportunity, as well as the policy of the government. We show the policy-maker’s decision to use quota or price based incentives matters. Price based incentives such as a carbon tax are characterized by disperse outcomes, with more R&D resources expended when technological opportunity is high, and reduced amounts when technological opportunity is low. Quotas such as mandates, in contrast, lead to a more consistent level of R&D spending across differences in technological opportunity. Thus, price-based systems are more likely to deliver great technological advances or none at all, while mandates are more likely to deliver consistent incremental gains. Moreover, we also show an optimal carbon tax is likely to outperform any mandate in expected welfare terms, and has less information requirements

Mandates and the Incentive for Environmental Innovation

Mandates are policy tools that are becoming increasingly popular to promote renewable energy use. In addition to mitigating the pollution externality of conventional energy, mandates have the potential to promote R&D investments in renewable energy technology. But how well do mandates perform as innovation incentives? To address this question, we develop a partial equilibrium model with endogenous innovation to examine the R&D incentives induced by a mandate, and compare this policy to two benchmark situations: laissez-faire and a carbon tax. Innovation is stochastic and the model permits an endogenous number of multiple innovators. We find that mandates can improve upon laissez faire, and that the prospect of innovation is essential for their desirability. However, mandates suffer from several limitations. A mandate creates relatively strong incentives for investment in R&D in low-quality innovations, but relatively weak incentives to invest in high-quality innovations, so that the dispersion of realized innovation quality is comparatively low. Moreover, a mandate achieves lower welfare than a carbon tax, and its optimal level is more sensitive to the structure of the innovation process

Promoting biogas and biomethane production: Lessons from cross country studies. ESRI WP630, July 2019

While a rich body of literature has looked at greenhouse gas emissions in biogas production systems and the potential impacts of biogas production on food supply, broader issues relating to the economic, environmental and social pillars of sustainability need to be carefully considered. Drawing upon experiences from European countries, we identify key outcomes associated with large-scale implementation of biogas and biomethane production. Topics of particular interest include policy instruments, farm intensification, and supply chain risks. We conclude by recommending policy directions for countries such as Ireland that are at earlier developmental stages for biogas and biomethane deployment

Between-microcycle variability of external soccer training loads through the evaluation of a contemporary periodisation training model ‘CUPs’

Variation in training load is consistently demonstrated within weekly microcycles in soccer, yet less is known of load variations between the same week-day sessions across different microcycles. Our study aim was to examine between-microcycle variability in key measures of external training load. Methods: Thirty-seven professional soccer players participated in this observational study which took place across the clubs initial 8-week in-season mesocycle of the 2022/23 season. During this mesocycle, each 1-week microcycle consisted of four distinct classifications of training session (Matchday (MD) -4, MD-3, MD -2, and MD-1, and one match (Saturday). External load data (total distance, high-intensity (>5.5m.s) distance, high-intensity accelerations (>3 m/s2), and percentage (%) of maximal speed attained) were collected across 564 training sessions (MD-4 = 123, MD-3 = 148, MD-2 = 130, MD-1 = 163). Data were analysed with mixed linear modelling. Results: When compared to the first microcycle, substantial week-to-week variation was evident for each of the four training session classifications, ranging from 1244 m to 2248 m for total distance, 80 m to 197 m for high-intensity distance, 11 to 25 for high-intensity accelerations, and 10.2 percentage points to 15.4 percentage points for % maximal speed. Conclusion: Our data show that despite training sessions having a consistency of planned training stimulus across an 8-week mesocycle, external load varied between microcycles. Nevertheless, within-player variability on the same day relative to match-day indicated a more consistent stimulus for key training variables relevant to specific training days

Spectral imaging of thermal damage induced during microwave ablation in the liver

Induction of thermal damage to tissue through delivery of microwave energy is frequently applied in surgery to destroy diseased tissue such as cancer cells. Minimization of unwanted harm to healthy tissue is still achieved subjectively, and the surgeon has few tools at their disposal to monitor the spread of the induced damage. This work describes the use of optical methods to monitor the time course of changes to the tissue during delivery of microwave energy in the porcine liver. Multispectral imaging and diffuse reflectance spectroscopy are used to monitor temporal changes in optical properties in parallel with thermal imaging. The results demonstrate the ability to monitor the spatial extent of thermal damage on a whole organ, including possible secondary effects due to vascular damage. Future applications of this type of imaging may see the multispectral data used as a feedback mechanism to avoid collateral damage to critical healthy structures and to potentially verify sufficient application of energy to the diseased tissue.Comment: 4pg,6fig. Copyright 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

Among-site variability in the stochastic dynamics of East African coral reefs

Coral reefs are dynamic systems whose composition is highly influenced by unpredictable biotic and abiotic factors. Understanding the spatial scale at which long-term predictions of reef composition can be made will be crucial for guiding conservation efforts. Using a 22-year time series of benthic composition data from 20 reefs on the Kenyan and Tanzanian coast, we studied the long-term behaviour of Bayesian vector autoregressive state-space models for reef dynamics, incorporating among-site variability. We estimate that if there were no among-site variability, the total long-term variability would be approximately one third of its current value. Thus among-site variability contributes more to long-term variability in reef composition than does temporal variability. Individual sites are more predictable than previously thought, and predictions based on current snapshots are informative about long-term properties. Our approach allowed us to identify a subset of possible climate refugia sites with high conservation value, where the long-term probability of coral cover <= 0.1 was very low. Analytical results show that this probability is most strongly influenced by among-site variability and by interactions among benthic components within sites. These findings suggest that conservation initiatives might be successful at the site scale as well as the regional scale.Comment: 97 pages, 49 figure

Effects of human land use and temperature on community dynamics in European forests

Climate change and human land use are thought to play a dominant role in the dynamics of European central-latitude forests in the Holocene. A wide range of mathematical and statistical models have been used to study the effects of these variables on forest dynamics, including physiologically-based simulations and phenomenological community models. However, for statistical analysis of pollen count data, compositional data analysis is particularly well suited, because pollen counts give only relative information. We studied the effects of changes in human land use and temperature on European central-latitude forest dynamics at 7 sites over most of the last , using a stochastic model for compositional dynamics of pollen count data. Our approach has a natural ecological interpretation in terms of relative proportional population growth rates, and does not require information on pollen production, dispersal, or deposition. We showed that the relative proportional population growth rates of Fagus and Picea were positively affected by intensified human land use, and that those of Tilia and Ulmus were negatively affected. Also, the relative proportional population growth rate of Fagus was negatively affected by increases in temperature above about . Overall, the effects of temperature on the rate of change of forest composition were more important than those of human land use. Although there were aspects of dynamics, such as short-term oscillations, that our model did not capture, our approach is broadly applicable and founded on ecological principles, and gave results consistent with current thinking

Biogas: a real option to reduce greenhouse gas emissions. ESRI Research Bulletin 2019/18

Biogas is a versatile fuel that can be used for multiple purposes such as electricity and heat production. Biogas is produced via anaerobic digestion (AD), which occurs when microorganisms in the absence of oxygen break down organic materials, such as food waste and agricultural feedstocks, producing gases such as methane and carbon dioxide. By removing carbon dioxide and other impurities, the upgraded biogas, namely biomethane, has similar chemical properties to fossil gas and can be fed directly into existing gas grids or dispensed as a vehicle fuel at fuelling stations. Replacing fossil fuels with biogas generated from sustainable sources helps reduce the net flow of greenhouse gases (GHG) to the atmosphere. Europe is the world leader in biogas production, with total production doubling since 2010 and increasing by more than 700% since 2000. This study reviews how European countries are developing their biogas and biomethane industries, eliciting key learnings for countries such as Ireland seeking to expand the biogas sector and reduce GHG emissions