Bernardo A. Bastien-Olvera & Frances C. Moore
Nature Sustainability (2020)
Climate change is damaging ecosystems throughout the world with serious implications for human well-being. Quantifying the benefits of reducing emissions requires understanding these costs, but the unique and non-market nature of many goods provided by natural systems makes them difficult to value. Detailed representation of ecological damages in models used to calculate the costs of greenhouse gas emissions has been largely lacking. Here, we have expanded a cost–benefit integrated assessment model to include natural capital as a form of wealth. This brings benefits to people through non-use existence value and as an input into the production of ecosystem services and market goods. In our model, using central estimates for all parameters, optimal emissions reach zero by the year 2050, limiting warming to 1.5 °C by the year 2100. We used Monte Carlo analysis to examine the influence of several key uncertain model parameters, and examined the effect of adaptive investments in natural systems that partially offset climate damages. Overall, we show that accounting for the use and non-use value of nature has large implications for climate policy. Our analysis suggests that better understanding climate impacts on natural systems and associated welfare effects should be a high priority for future research.
David Fajardo-Ortiz, Stephan Hornbostel, Maywa Montenegro-de-Wit, Annie Shattuck
CRISPR/Cas has the potential to revolutionize medicine, agriculture, and the way we understand life itself. Understanding the trajectory of innovation, how it is influenced and who pays for it, is essential for such a transformative technology. The University of California and the Broad/Harvard/MIT systems are the two most prominent academic institutions involved in the research and development of CRISPR/Cas. Here we present a model of co-funding networks for CRISPR/Cas research at these institutions, using funding acknowledgments to build connections. We map papers representing 95% of citations on CRISPR/Cas from these institutions grouped by the stage each represents in the research translation process (as a biological phenomenon, as a research tool, as a set of technologies, and applications of that technology), and use a novel technique to analyse the relationships between the structures of the co-funding networks, the phase of research, and funding sources. The co-funding subnetworks were similar in that US government research funding played the decisive role in early stage research. Research at Broad/Harvard/MIT is also strongly supported by philanthropic/charitable organizations in later stages of the translation process, clustered around certain topics. Applications for CRISPR technologies were underrepresented, which bolsters findings on the preponderance of the US private sector in developing applications, and the disproportionate number of Chinese institutions filing patents for industrial and food systems applications. These network models raise fundamental questions about the role of the state in supporting breakthrough innovations, risk, reward, and the influence of the private sector and philanthropy over the trajectory of transformative technologies.
Explanations of human technology often point to both its cumulative and combinatorial character. Using a novel computational framework, where individual agents attempt to solve problems by modifying, combining and transmitting technologies in an open-ended search space, this paper re-evaluates two prominent explanations for the cultural evolution of technology: that humans are equipped with (i) social learning mechanisms for minimizing information loss during transmission, and (ii) creative mechanisms for generating novel technologies via combinatorial innovation. Here, both information loss and combinatorial innovation are introduced as parameters in the model, and then manipulated to approximate situations where technological evolution is either more cumulative or combinatorial. Compared to existing models, which tend to marginalize the role of purposeful problem-solving, this approach allows for indefinite growth in complexity while directly simulating constraints from history and computation. The findings show that minimizing information loss is only required when the dynamics are strongly cumulative and characterised by incremental innovation. Contrary to previous findings, when agents are equipped with a capacity for combinatorial innovation, low levels of information loss are neither necessary nor sufficient for populations to solve increasingly complex problems. Instead, higher levels of information loss are advantageous for unmasking the potential for combinatorial innovation. This points to a parsimonious explanation for the cultural evolution of technology without invoking separate mechanisms of stability and creativity.
To tame urban traffic, the computer scientist Carlos Gershenson finds that letting transportation systems adapt and self-organize often works better than trying to predict and control them.