arXiv subjects

Vilhelm Verendel

Publications and source records attributed to Vilhelm Verendel.

6 recordsLinked to original sources

arXiv2022

Measuring artificial intelligence: a systematic assessment and implications for governance

Governing artificial intelligence (AI) inventions is a major policy concern, yet definitions and measurement remain contested. We compare four patent-based approaches reflecting distinct understandings of AI. Using US patents (1990-2019), we assess the degree to which each approach classifies AI as a general-purpose technology (GPT) and examine patent concentration--two central policy-relevant dimensions. The approaches overlap in just 1.37% of patents, defining between 3-17% of all US patents in 2019 as AI. All approaches confirm AI's GPT characteristics, with the smallest keyword-based set exhibiting the highest growth and generality. High GPTness indicates public good characteristics, justifying public support. Across methods, AI patents concentrate among a few firms, highlighting market power and regulatory challenges. Policy implementation, thus, requires careful consideration of multiple classification methods to ensure robust, inclusive, and effective AI governance.

econ.GN↗

arXiv2020

Deep decarbonization and the Supergrid, prospects for electricity transmission between Europe and China

Long distance transmission within continents has been shown to be one of the most important variation management strategies in renewable energy systems, where allowing for transmission expansion will reduce system cost by around 20%. In this paper, we test whether the system cost further decreases when transmission is extended to intercontinental connections. We analyze a Eurasian interconnection between China, Mid-Asia and Europe, using a capacity expansion model with hourly time resolution. The model is constrained by an increasingly tighter global cap on CO2 emissions in order to investigate the effect of different levels of reliance on variable sources. Our results show that a supergrid option decreases total system cost by a maximum of 5%, compared to continental grid integration. This maximum effect is achieved when (i) the generation is constrained to be made up almost entirely by renewables, (ii) the land available for VRE farms is relatively limited and the demand is relatively high and (iii) the cost for solar PV and storage is high. The importance of these two factors is explained by that a super grid allows for harnessing of remote wind-, solar- and hydro resources as well as management of variations, both of which are consequential only in cases where dispatchable resources are limited or very costly. As for the importance of the cost for storage, it represents a competing variation management option, and when it has low cost, it substitutes part of the role of the supergrid, which is to manage variations through long-distance trade. The cost decrease from a Eurasian supergrid was found to be between 0% and 5%, compared to the cost in the case of continental-scale grids. We conclude that the benefits of a supergrid from a techno-economic perspective are in most cases negligible, or modest at best.17 pages, f

physics.soc-ph↗

arXiv2020

An autopilot for energy models -- automatic generation of renewable supply curves, hourly capacity factors and hourly synthetic electricity demand for arbitrary world regions

Energy system models are increasingly being used to explore scenarios with large shares of variable renewables. This requires input data of high spatial and temporal resolution and places a considerable preprocessing burden on the modeling team. Here we present a new code set with an open source license for automatic generation of input data for large-scale energy system models for arbitrary regions of the world, including sub-national regions, along with an associated generic capacity expansion model of the electricity system. We use ECMWF ERA5 global reanalysis data along with other public geospatial datasets to generate detailed supply curves and hourly capacity factors for solar photovoltaic power, concentrated solar power, onshore and offshore wind power, and existing and future hydropower. Further, we use a machine learning approach to generate synthetic hourly electricity demand series that describe current demand, which we extend to future years using regional SSP scenarios. Finally, our code set automatically generates costs and losses for HVDC interconnections between neighboring regions. The usefulness of our approach is demonstrated by several different case studies based on input data generated by our code. We show that our model runs of a future European electricity system with high share of renewables are in line with results from more detailed models, despite our use of global datasets and synthetic demand.

physics.soc-ph↗

arXiv2015

Fermi's paradox, extraterrestrial life and the future of humanity: a Bayesian analysis

The Great Filter interpretation of Fermi's great silence asserts that $Npq$ is not a very large number, where $N$ is the number of potentially life-supporting planets in the observable universe, $p$ is the probability that a randomly chosen such planet develops intelligent life to the level of present-day human civilization, and $q$ is the conditional probability that it then goes on to develop a technological supercivilization visible all over the observable universe. Evidence suggests that $N$ is huge, which implies that $pq$ is very small. Hanson (1998) and Bostrom (2008) have argued that the discovery of extraterrestrial life would point towards $p$ not being small and therefore a very small $q$, which can be seen as bad news for humanity's prospects of colonizing the universe. Here we investigate whether a Bayesian analysis supports their argument, and the answer turns out to depend critically on the choice of prior distribution.

physics.pop-ph↗

arXiv2013

Evolutionary Exploration of the Finitely Repeated Prisoners' Dilemma--The Effect of Out-of-Equilibrium Play

The finitely repeated Prisoners' Dilemma is a good illustration of the discrepancy between the strategic behaviour suggested by a game-theoretic analysis and the behaviour often observed among human players, where cooperation is maintained through most of the game. A game-theoretic reasoning based on backward induction eliminates strategies step by step until defection from the first round is the only remaining choice, reflecting the Nash equilibrium of the game. We investigate the Nash equilibrium solution for two different sets of strategies in an evolutionary context, using replicator-mutation dynamics. The first set consists of conditional cooperators, up to a certain round, while the second set in addition to these contains two strategy types that react differently on the first round action: The "Convincer" strategies insist with two rounds of initial cooperation, trying to establish more cooperative play in the game, while the "Follower" strategies, although being first round defectors, have the capability to respond to an invite in the first round. For both of these strategy sets, iterated elimination of strategies shows that the only Nash equilibria are given by defection from the first round. We show that the evolutionary dynamics of the first set is always characterised by a stable fixed point, corresponding to the Nash equilibrium, if the mutation rate is sufficiently small (but still positive). The second strategy set is numerically investigated, and we find that there are regions of parameter space where fixed points become unstable and the dynamics exhibits cycles of different strategy compositions. The results indicate that, even in the limit of very small mutation rate, the replicator-mutation dynamics does not necessarily bring the system with Convincers and Followers to the fixed point corresponding to the Nash equilibrium of the game.

q-bio.PE↗

arXiv2008

Adaptive Dynamics of Realistic Small-World Networks

Continuing in the steps of Jon Kleinberg's and others celebrated work on decentralized search in small-world networks, we conduct an experimental analysis of a dynamic algorithm that produces small-world networks. We find that the algorithm adapts robustly to a wide variety of situations in realistic geographic networks with synthetic test data and with real world data, even when vertices are uneven and non-homogeneously distributed. We investigate the same algorithm in the case where some vertices are more popular destinations for searches than others, for example obeying power-laws. We find that the algorithm adapts and adjusts the networks according to the distributions, leading to improved performance. The ability of the dynamic process to adapt and create small worlds in such diverse settings suggests a possible mechanism by which such networks appear in nature.

cs.DS↗