Models of Cooperation, Learning and Catastrophic Risk

Our world presents us with dangers and opportunities. Some of these dangers and opportunities are easier to handle if two or more individuals learn to cooperate. This thesis contributes five papers about cooperation, learning and catastrophic risk. In papers I-II, we consider the Finitely Repeated Prisoners' Dilemma, a model for where cooperation between two players is particularly hard to achieve. We introduce and model strategies that attempt to convince others to cooperate when backward induction can be used to eliminate cooperation for a number of steps from the end. We find that in a population with these strategies, cooperation can become recurrent, and we examine the conditions for this. Recurrent cooperation is possible in an evolutionary model (paper I) as well as in a population of players that are near-perfect Bayesian expected utility-maximizers (paper II). In paper III, we consider a bargaining model of climate negotiations where players negotiate emissions and sudden catastrophic damage occurs if emissions exceed a threshold amount. We introduce and model a mechanism of strategic reasoning, where players predict the emission bids of others, and consider how this affects the possibility of reaching agreements preventing catastrophic damage. We find that the effect of higher levels of strategic reasoning makes it harder to reach agreements in the model. This effect can be partially mitigated by restricting the range of initial bids in the bargaining process. In paper IV, we consider the arguments by Hanson and Bostrom about the Great Filter as an attempt to explain the Fermi Paradox. According to these arguments, finding extraterrestrial life on one planet should lower our expectations for humanity's prospects to progress far beyond our current technological capabilities. We model this claim as a Bayesian learning problem and examine the effect a single observation of life has in the model. We find that the conclusion of the argument depends critically on the choice of prior distribution. In paper V, we consider a model of agricultural markets and land-use competition between food and bioenergy crops. Agents in the model represent farmers who decide which crop to grow depending on predictors that give future price expectations. We model agents who can switch among predictors to make their decisions. We find that some predictor types can be concentrated on key parcels of land, which reduces volatility in crop prices for the system. We also examine several mechanisms that can bring price fluctuations in the system down and closer to a stable state

Tracking artificial intelligence in climate inventions with patent data

Artificial intelligence (AI) is spreading rapidly in many technology areas, and AI inventions may help climate change mitigation and adaptation. Previous studies of climate-related AI mainly rely on expert studies of literature, not large-scale data. Here I present an approach to track the relation between AI and climate inventions on an economy-wide scale. Analysis of over 6 million US patents, 1976 to 2019, shows that within climate patents, AI is referred to most often in transportation, energy and industrial production technologies. In highly cited patents, AI occurs more frequently in adaptation and transport than in other climate mitigation areas. AI in climate patents was associated with around 30–100% more subsequent inventions when counting all technologies. Yet AI-climate patents led to a greater share of citations from outside the climate field than non-AI-climate patents. This suggests the importance of tracking both increased invention activity and the areas where subsequent inventions emerge

Measuring Traffic in Cities Through a Large-Scale Online Platform

Online real-time traffic data services could effectively deliver traffic information to people all over the world and provide large benefits to the society and research about cities. Yet, city-wide road network traffic data are often hard to come by on a large scale\ua0over a longer period of time. We collect, describe, and analyze traffic data for 45 cities from HERE, a major online real-time traffic information provider. We sampled the online platform for city traffic data\ua0every 5\ua0min during 1\ua0year, in total more than 5 million samples covering more than 300 thousand road segments.\ua0Our aim is to describe some of the practical issues surrounding the data that we experienced in working with this type of data source, as well as to explore the data patterns and see how this data source provides information to study traffic in cities. We focus on data availability to characterize how traffic information is available for different cities; it measures the share of road segments with real-time traffic information at a given time for a given city. We describe the patterns of real-time data availability, and evaluate methods to handle filling in missing speed\ua0data for road segments when\ua0real-time information\ua0was not available. We conduct a validation case study based on Swedish traffic sensor data and point out challenges for future validation. Our findings include (i) a case study of validating the HERE data against ground truth available for roads and lanes in a Swedish city, showing that real-time traffic data tends to follow dips in travel speed\ua0but miss instantaneous higher speed measured in some\ua0sensors, typically at times\ua0when there are fewer vehicles on the road; (ii) using time series clustering, we identify four clusters of cities with different types of measurement patterns; and (iii) a k-nearest neighbor-based method consistently outperforms other methods to fill in missing real-time traffic speeds. We illustrate how to work with this kind of traffic data source that is increasingly available to researchers, travellers,\ua0and city planners. Future work is needed to broaden the scope of validation, and to apply these methods to use online data for\ua0improving\ua0our knowledge of traffic in cities

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

Computing the power profiles for an Airborne Wind Energy system based on large-scale wind data

Airborne Wind Energy (AWE) is a new power technology that harvests wind energy at high altitudes using tethered wings. Studying the power potential of the system at a given location requires evaluating the local power production profile of the AWE system. As the optimal operational AWE system altitude depends on complex trade-offs, a commonly used technique is to formulate the power production computation as an Optimal Control Problem (OCP). In order to obtain an annual power production profile, this OCP has to be solved sequentially for the wind data for each time point. This can be computationally costly due to the highly nonlinear and complex AWE system model. This paper proposes a method how to reduce the computational effort when using an OCP for power computations of large-scale wind data. The method is based on homotopy-path-following strategies, which make use of the similarities between successively solved OCPs. Additionally, different machine learning regression models are evaluated to accurately predict the power production in the case of very large data sets. The methods are illustrated by computing a three-month power profile for an AWE drag-mode system. A significant reduction in computation time is observed, while maintaining good accuracy

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. We also perform a detailed analysis of how the evolutionary behaviour depends on payoffs, game length, and mutation rate

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. We also perform a detailed analysis of how the evolutionary behaviour depends on payoffs, game length, and mutation rate

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