No imitation - on local and group interaction, learning and reciprocity in prisoners\

This study disentangles experimentally imitation, reinforcement, and reciprocity in repeated prisoners' dilemmas. We compare a simple situation in which players interact only with their neighbours (local interaction) with one where players interact with all members of the population (group interaction). We observe choices under different information conditions and estimate parameters of a learning model. We find that imitation, while assumed to be a driving force in many models of spatial evolution, is often a negligible factor in the experiment. Behaviour is predominantly driven by reinforcement learning.Local interaction,experiments,prisoner's dilemma,learning,reinforcement,repeated games

Statistical Time Series Models of Pilot Control with Applications to Instrument Discrimination

A general description of the methodology used in obtaining the transfer function models and verification of model fidelity, frequency domain plots of the modeled transfer functions, numerical results obtained from an analysis of poles and zeroes obtained from z plane to s-plane conversions of the transfer functions, and the results of a study on the sequential introduction of other variables, both exogenous and endogenous into the loop are contained

Den europeiske energiomleggingen : økonomiske effekter for nordiske energimarkedsaktører

This thesis assesses the economic impacts that the European energy transition towards net-zero carbon emissions has on Nordic power system stakeholders. The impacts of climate policies, the role of power system flexibility, and the role of Norway's future power supply and demand are analyzed. This thesis aims to improve the understanding of how Nordic stakeholders will be affected by the energy transition, what opportunities and impediments exist, and what potential conflicts policy makers should be aware of. To answer these research objectives, the partial equilibrium Balmorel energy system model was applied alone, extended by a demand response module, coupled with a general equilibrium model, and linked with an expert survey. The results show that the increasingly ambitious European climate targets will most likely benefit Nordic renewable energy producer revenues and market values in 2030 and beyond. The benefits, however, will depend on the European and national policy choices and producers’ technology characteristics. Model results show that flexibility will be critical for the cost-effective decarbonization of the power sector, with the value of flexibility options for reducing system costs increasing exponentially with more ambitious climate targets. In deep decarbonization scenarios for 2030, transmission and sector coupling with the district heating system are particularly important for energy system efficiency. With less ambitious climate targets, demand side management increases system efficiency most. Another finding is that it will also be important to address ambiguous consumer and producer welfare impacts of the energy transition and infrastructure investment. An optimal decision from a societal point of view may, for example, decrease Nordic consumer welfare in some market areas and thereby increase the likelihood of social opposition if not adequately addressed.Denne avhandlingen analyserer hvordan den den europeiske energiomstillingen påvirker de økonomiske rammebetingelsene for norske aktører innen fornybar energi. Avhandlingen ser spesifikt på effektene av europeisk klimapolitikk, behov og lønnsomhet i å tilby energisystemfleksibilitet, og inntektsutsiktene for ulike fornybarteknologier. Avhandlingen har som mål å forbedre forståelsen av hvordan nordiske interessenter påvirkes av den europeiske energiomstillingen, belyse muligheter og utfordringer, og analysere hvilke potensielle konflikter beslutningstakere bør være oppmerksomme på. Disse problemstillingene er analysert ved hjelp av den den nord europeiske energisystemmodellen Balmorel. I avhandlingen er Balmorel utvidet med en modul for etterspørselsrespons, koblet med en generell likevektsmodell for EU’s økonomi og i en av studien bruker vi også resultater fra en spørreundersøkelse blant energimarkedseksperter som input i modellen. Resultatene viser at de stadig mer ambisiøse europeiske klimamålene mest sannsynlig vil øke inntektene og markedsverdiene til nordiske fornybarressurser mot 2030 og videre. Effektene vil imidlertid avhenge EU og nasjonenes virkemidler i energiomstillingen, og produksjonsteknologiens egenskaper. Modellresultatene viser at løsninger som kan bidra med energisystemfleksibilitet vil være viktig for å oppnå kostnadseffektive utslippskutt i kraftsektoren. Verdien av fleksibilitet øker eksponentielt med mer ambisiøse klimamål. I scenarier med omfattende utslippskutt er utvekslingskabler mellom land og regioner, og sektorkobling med fjernvarmesystemet, spesielt viktig for energisystemets effektivitet. Med mindre ambisiøse klimamål vil styring av etterspørselssiden øke systemeffektiviteten mest, ifølge modellresultatene i denne avhandlingen. Et annet funn er at det også vil være viktig å adressere fordelingseffekter for forbruker- og produsentvelferd ved investeringer i infrastruktur. En optimal beslutning fra et samfunnsperspektiv kan, for eksempel, redusere konsumentoverskuddet i enkelte markedsområder og dermed øke sannsynligheten for sosial motstand mot energiomstillingen

Local and group interaction in prisoners' dilemma experiments

This study investigates experimentally the effects of locality on learning and strategic behaviour in a repeated prisoners’ dilemma. We compare a simple situation that models space (players interact only with their neighbours) with one that does not (players interact with all members of the population). Within this context different information conditions are studied. We find that imitation, while assumed to be a driving force in many models of spatial evolution, is a negligible factor in the experiment. Behaviour is driven by reinforcement learning and, under specific conditions, strategic behaviour

Learning and cooperation in network experiments

In this paper we study learning and cooperation in repeated prisoners' dilemmas experiments. We compare interaction neighbourhoods of different sizes and structure and we observe choices under different information conditions and estimate parameters of a learning model. We test robustness of the eastimator. We find that imitation, albeit a driving force in many models of spatial evolution, is often a negligible factor in the experiment. As a result, we find less cooperation

No imitation : on local and group interaction, learning and reciprocity in prisoners' dilemma experiments

This study disentangles experimentally imitation, reinforcement, and reciprocity in repeated prisoners' dilemmas. We compare a simple situation in which players interact only with their neighbours (local interaction) with one where players interact with all members of the population (group interaction). We observe choices under different information conditions and estimate parameters of a learning model. We find that imitation, while assumed to be a driving force in many models of spatial evolution, is often a negligible factor in the experiment. Behaviour is predominantly driven by reinforcement learning

Repeated game strategies in local and group prisoner's dilemma

We investigate and compare different approaches to derive strategies from observed data in spatial and spaceless prisoners' dilemmas experiments. We start with a model where players choose a fixed action that remains constant for all repetitions of a stage game. As an extension we then allow players to choose simple repeated game strategies that, however, remain fixed over the course of the game. We then discuss a method how to identify changing repeated game strategies. This method is used to study a simple reinforcement model. We find that in a spatial structure reinforcement plays a more important role than in a spaceless structure

Reinforcement, repeated games, and local interaction

We investigate and compare different approaches to derive strategies from laboratory data in prisoners' dilemmas experiments. While theory suggests more cooperation in spatial structures than in spaceless ones, we find in our experiments either the opposite or no difference. In this paper we investigate to which degree learning and reinforcement explains this dependence on structure and information. Starting from a very simple model we gradually develop a setup where players use repeated game strategies and choose among these strategies using a simple reinforcement rule. We then measure to which degree this model explain players' behaviour

Keep Your Stakeholders Engaged: Interactive Vision Videos in Requirements Engineering

One of the most important issues in requirements engineering (RE) is the alignment of stakeholders' mental models. Making sure that all stakeholders share the same vision of a changing system is crucial to the success of any project. Misaligned mental models of stakeholders can lead to conflicting requirements. A promising approach to this problem is the use of video showing a system vision, so-called vision videos, which help stakeholders to disclose, discuss, and align their mental models of the future system. However, videos have the drawback of allowing viewers to adopt a passive role, as has been shown in research on e-learning. In this role, viewers tend to be inactive, unfocused and bored while watching a video. In this paper, we learn and adopt findings from scientific literature in the field of e-learning on how to mitigate this passive role while watching vision videos in requirements engineering. In this way, we developed concepts that incorporate interactive elements into vision videos to help viewers stay focused. These elements include questions that are asked during the video and ways for viewers to decide what happens next in the video. In a preliminary evaluation with twelve participants, we found statistically significant differences when comparing the interactive vision videos with their traditional form. Using an interactive vision videos, viewers are noticeably more engaged and gather more information on the shown system.© 2021 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 works

Efficient query processing in managed runtimes

This thesis presents strategies to improve the query evaluation performance over huge volumes of relational-like data that is stored in the memory space of managed applications. Storing and processing application data in the memory space of managed applications is motivated by the convergence of two recent trends in data management. First, dropping DRAM prices have led to memory capacities that allow the entire working set of an application to fit into main memory and to the emergence of in-memory database systems (IMDBs). Second, language-integrated query transparently integrates query processing syntax into programming languages and, therefore, allows complex queries to be composed in the application. IMDBs typically serve as data stores to applications written in an object-oriented language running on a managed runtime. In this thesis, we propose a deeper integration of the two by storing all application data in the memory space of the application and using language-integrated query, combined with query compilation techniques, to provide fast query processing. As a starting point, we look into storing data as runtime-managed objects in collection types provided by the programming language. Queries are formulated using language-integrated query and dynamically compiled to specialized functions that produce the result of the query in a more efficient way by leveraging query compilation techniques similar to those used in modern database systems. We show that the generated query functions significantly improve query processing performance compared to the default execution model for language-integrated query. However, we also identify additional inefficiencies that can only be addressed by processing queries using low-level techniques which cannot be applied to runtime-managed objects. To address this, we introduce a staging phase in the generated code that makes query-relevant managed data accessible to low-level query code. Our experiments in .NET show an improvement in query evaluation performance of up to an order of magnitude over the default language-integrated query implementation. Motivated by additional inefficiencies caused by automatic garbage collection, we introduce a new collection type, the black-box collection. Black-box collections integrate the in-memory storage layer of a relational database system to store data and hide the internal storage layout from the application by employing existing object-relational mapping techniques (hence, the name black-box). Our experiments show that black-box collections provide better query performance than runtime-managed collections by allowing the generated query code to directly access the underlying relational in-memory data store using low-level techniques. Black-box collections also outperform a modern commercial database system. By removing huge volumes of collection data from the managed heap, black-box collections further improve the overall performance and response time of the application and improve the application’s scalability when facing huge volumes of collection data. To enable a deeper integration of the data store with the application, we introduce self-managed collections. Self-managed collections are a new type of collection for managed applications that, in contrast to black-box collections, store objects. As the data elements stored in the collection are objects, they are directly accessible from the application using references which allows for better integration of the data store with the application. Self-managed collections manually manage the memory of objects stored within them in a private heap that is excluded from garbage collection. We introduce a special collection syntax and a novel type-safe manual memory management system for this purpose. As was the case for black-box collections, self-managed collections improve query performance by utilizing a database-inspired data layout and allowing the use of low-level techniques. By also supporting references between collection objects, they outperform black-box collections