This paper describes an agent-based software infrastructure for agile industrial production. This production is done on special devices called equiplets. A grid of these equiplets connected by a fast network is capable of producing a variety of different products in parallel. The multi-agent-based underlying systems uses two kinds of agents: an agent representing the product and an agent representing the equiplet.
MULTIFILE
Efficiency of city logistics activities suffers due to conflicting personal preferences and distributed decision making by multiple city logistics stakeholders. This is exacerbated by interdependency of city logistics activities, decision making with limited information and stakeholders’ preference for personal objectives over system efficiency. Accordingly, the key to understanding the causes of inefficiency in the city logistics domain is understanding the interaction between heterogeneous stakeholders of the system. With the capabilities of representing a system in a natural and flexible way, agent based modelling (ABM) is a promising alternative for the city logistics domain. This research focuses on developing a framework for the successful implementation of the ABM approach for the city logistics domain. The framework includes various elements – a multi-perspective semantic data model (i.e. ontology) and its validation, the development of an agent base model using this ontology, and a validation approach for the agent-based model. Conclusively, the framework shows that a rigorous course can be taken to successfully implement agent based modelling approach for the city logistics domain.
Algorithmic curation is a helpful solution for the massive amount of content on the web. The term is used to describe how platforms automate the recommendation of content to users. News outlets, social networks and search engines widely use recommendation systems. Such automation has led to worries about selective exposure and its side effects. Echo chambers occur when we are over-exposed to the news we like or agree with, distorting our perception of reality (1). Filter bubbles arise where the information we dislike or disagree with is automatically filtered out – narrowing what we know (2). While the idea of Filter Bubbles makes logical sense, the magnitude of the "filter bubble effect", reducing diversity, has been questioned [3]. Most empirical studies indicate that algorithmic recommendations have not locked large audience segments into bubbles [4]. However, little attention has been paid to the interplay between technological, social, and cognitive filters. We proposed an Agent-based Modelling to simulate users' emergent behaviour and track their opinions when getting news from news outlets and social networks. The model aims to understand under which circumstances algorithmic filtering and social network dynamics affect users' innate opinions and which interventions can mitigate the effect. Agent-based models simulate the behaviour of multiple individual agents forming a larger society. The behaviour of the individual agents can be elementary, yet the population's behaviour can be much more than the sum of its parts. We have designed different scenarios to analyse the contributing factors to the emergence of filter bubbles. It includes different recommendation algorithms and social network dynamics. Cognitive filters are based on the Triple Filter Bubble model [5].References1.Richard Fletcher, 20202.Eli Pariser, 20123.Chitra & Musco, 20204. Möller et al., 20185. Daniel Geschke et al, 2018
The Dutch main water systems face pressing environmental, economic and societal challenges due to climatic changes and increased human pressure. There is a growing awareness that nature-based solutions (NBS) provide cost-effective solutions that simultaneously provide environmental, social and economic benefits and help building resilience. In spite of being carefully designed and tested, many projects tend to fail along the way or never get implemented in the first place, wasting resources and undermining trust and confidence of practitioners in NBS. Why do so many projects lose momentum even after a proof of concept is delivered? Usually, failure can be attributed to a combination of eroding political will, societal opposition and economic uncertainties. While ecological and geological processes are often well understood, there is almost no understanding around societal and economic processes related to NBS. Therefore, there is an urgent need to carefully evaluate the societal, economic, and ecological impacts and to identify design principles fostering societal support and economic viability of NBS. We address these critical knowledge gaps in this research proposal, using the largest river restoration project of the Netherlands, the Border Meuse (Grensmaas), as a Living Lab. With a transdisciplinary consortium, stakeholders have a key role a recipient and provider of information, where the broader public is involved through citizen science. Our research is scientifically innovative by using mixed methods, combining novel qualitative methods (e.g. continuous participatory narrative inquiry) and quantitative methods (e.g. economic choice experiments to elicit tradeoffs and risk preferences, agent-based modeling). The ultimate aim is to create an integral learning environment (workbench) as a decision support tool for NBS. The workbench gathers data, prepares and verifies data sets, to help stakeholders (companies, government agencies, NGOs) to quantify impacts and visualize tradeoffs of decisions regarding NBS.
Elektrisch rijden staat aan de vooravond van een schaalsprong. De ambitie van zowel de Nederlandse overheid als internationale overheden is om binnen nu en 12 jaar alleen nog maar elektrische auto’s nieuw op de markt toe te laten. De elektrisch vervoer (EV) keten staat voor de grote uitdaging om deze schaalsprong op tijd met voldoende laadinfrastructuur te faciliteren. Nederlandse ketenpartners willen, net als de afgelopen jaren, koploper blijven op het gebied van EV-laadinfrastructuur en daarom goed voorbereid zijn op deze schaalsprong. De centrale praktijkvraag van de EV-ketenpartners is “Hoe kan de toekomstige laadbehoefte voor elektrische voertuigen in een snel groeiende markt met nieuwe gebruikersgroepen goed worden ingevuld?” Het doel van Future Charging is om bij te dragen aan de doorbraak van elektrisch rijden door kennis over de laadbehoefte van nieuwe gebruikersgroepen te ontwikkelen en toekomstig laadgedrag in een agent-based model te simuleren. Simulaties geven EV-ketenpartners concrete inzichten in effecten van toekomstscenario’s op het gebruik van laadinfrastructuur, de impact op het elektriciteitsnet en openbare ruimte. Deze kennis ondersteunt EV-ketenpartners bij de uitrol van toekomstbestendige laadinfrastructuur. In totaal brengt dit project 17 consortiumpartners bij elkaar waarmee de volledige EV-keten voor laadinfrastructuur vertegenwoordigd is: gemeenten, netbeheerders, laadpaal-exploitanten, energiebedrijven en gebruikers. De partners bieden hiermee een rijke praktijkomgeving waar continu kan worden geleerd over de veranderende laadbehoefte van verschillende gebruikersgroepen en in verschillende ruimtelijke settings: van grootstedelijk tot “laden in de regio”. Sinds 2014 beheert en monitort de Hogeschool van Amsterdam de laaddata voor G4/MRA-E. Meer dan 8,5 miljoen laadsessies zijn opgeslagen in een professioneel datawarehouse en middels beveiligde accounts toegankelijk voor onderzoek. Future Charging slaat de brug tussen theorie over laadbehoefte, laadgedrag en agent-based simuleren en de praktijk van laadinfrastructuur. Het resultaat is een praktisch toepasbaar simulatiemodel waarmee ontwerpstudies en praktijkcases worden doorgerekend.
Socio-economic pressures on coastal zones are on the rise worldwide, leaving increasingly less room for natural coastal change without affecting humans. The challenge is to find ways for social and natural systems to co-exist, co-develop and create synergies. The recent implementation of multi-functional, nature-based solutions (NBS) on the sandy Dutch coast seem to offer great potential in that respect. Surprisingly, the studies evaluating these innovative solutions paid little attention to how the social and natural systems interact in the NBS-modified coastal landscapes and if these interactions strengthen or weaken the primary functions of the NBS. It is not clear whether the objectives to improve coastal resilience and spatial quality will be met throughout the lifetime of the intervention. In the proposed project we will investigate the socio-bio-physical dynamics of anthropogenic sandy shores applying a Living Lab approach, documenting and analyzing interactions between evolving anthropogenic shores (Sand Motor and Hondsbossche Duinen, Fig.1) and people that use and manage these NBS-modified landscapes. Socio-bio-physical interactions will be investigated at various scales, and consequences for the long-term functionality of the NBS will be assessed, by coupling an agent-based social model and a cellular automata landscape model. By studying the behavior of the coupled system we aim to identify limits to, and optima in, multi-functionality of the NBS design, and will study how various stakeholders can influence the development of the NBS in desired directions with respect to primary NBS functions, including social and ecological goals. Together with consortium partners from public and private sectors we will co-create guidelines for management and maintenance of multifunctional NBS and design procedures and visualization tools for intervention design.
