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Annually, the EU produces more than 100 million tonnes of urban biowaste, which is largely under-valorized and in some cases even still landfilled without any energy or material recovery. If Europe wants to be ready for the future, it will need to make better use of this large biomass potential within a circular economy approach. The research project funded by the European Commission under the Horizon 2020 programme entitled 'VOLATILE—Biowaste derived volatile fatty acid platform for biopolymers, bioactive compounds and chemical building blocks' aimed to produce volatile fatty acids (VFAs) from biowaste for reprocessing into products, materials or substances to close the material loop. During the project, the partners were able to obtain average volatile fatty acid yields of 627 g COD/kg organic matter (OM) for food waste, 448 g COD/kg OM for separately collected vegetable, garden and fruit waste (VGF) and 384 g COD/kg OM for the organic fraction of municipal solid waste (OF-MSW) at concentrations ranging from 12 to 48 g/L, 6 to 40 g/L and 13 to 26 g/L, respectively. A membrane filtration cascade consisting of micro-, ultra- and nano-filtration followed by reverse osmosis was identified as a feasible way to purify and concentrate the VFA effluent, making them a suitable carbon source for further fermentation processes. Besides technical optimization, socio-economic and legal aspects associated with this platform technology were also studied and show that although this technology is still in development, it is providing an answer to changing societal and market expectations both regarding organic waste treatment and bio-based production strategies. Based on the current technological, economic and market evolutions, it is expected that the VFAP will play an important role in organic waste treatment in the coming years. [ABSTRACT FROM AUTHOR]

Innovation is fundamental for development and provides a competitive advantage for societies. It is the process of creating more complex technologies, ideas, or protocols from existing ones. While innovation may be created by single agents (i.e. individuals or organisations), it is often a result of social interactions between agents exchanging and combining complementary expertise and perspectives. The structure of social networks impacts this knowledge exchange process. To study the role of social network structures on the creation of new technologies, we design an evolutionary mechanistic model combining self-creation and social learning. We find that social heterogeneity allows agents to leverage the benefits of diversity and to develop technologies of higher complexity. Social heterogeneity, however, reduces the group ability to innovate. Not only the social structure but also the openness of agents to collaborate affect innovation. We find that interdisciplinary interactions lead to more complex technologies benefiting the entire group but also increase the inequality in the innovation output. Lower openness to interdisciplinary collaborations may be compensated by a higher ability to collaborate with multiple peers, but low openness also neutralises the intrinsic benefits of network heterogeneity. Our findings indicate that social network heterogeneity has contrasting effects on microscopic (local) and macroscopic (group) levels, suggesting that the emergence of innovation leaders may suppress the overall group performance.

Understanding the evolution and spread of opinions within social groups gives important insight into areas such as public elections and marketing. We are specifically interested in how psychological theories of interpersonal influence may affect how individuals change their opinion through interactions with their peers, and apply Agent-Based Modelling to explore the factors that may affect the emergence of consensus. We investigate the co-evolution of opinion and location by extending the Deffuant–Weisbuch bounded confidence opinion model to include mobility inspired by the psychological theories of homophily and dissonance, where agents are attracted or repelled by their neighbours based on the agreement of their opinions. Based on wide experimentation, we characterize the time it takes to converge to a steady state and the local diversity of opinions that results, finding that homophily leads to drastic differences in the nature of consensus. We further extend our mobility model and add noise in order to check the model's robustness, finding that a number of opinion clusters survive even with high levels of noise. [ABSTRACT FROM AUTHOR]

In this paper institutions are treated as stabilized sets of expectations, an approach that encourages investigation of how cultural formations, political regimes, global financial arrangements, and other institutions can be both reliable and yet also subject to sudden and sometimes catastrophic transformations. We examine conditions that make political cascades, or tipping, more or less likely. We report findings from computer-assisted agent-based modelling experiments designed to test Timur Kuran's preference falsification model for explaining the possibility, but rare occurrence of, revolutionary political cascades. Since it run on a computer, our operationalized model of Kuran's theory is a necessarily precise and elaborated refinement of the incompletely specified version presented by Kuran. Our purpose is to go beyond his explanation for why political cascades can occur, albeit rarely, to explore the conditions that make them more or less likely. Our specific focus is on the impact of the amount of knowledge about the state of the entire system possessed by citizens with stronger or weaker inclinations to publicly express their anti-regime sentiments. The "zone of knowledge" of individuals is an unexamined variable whose importance is unrecognized but implied by Kuran's analysis. We find that with some reasonable but crucial refinements Kuran's preference falsification theory works to explain the pattern observed in the political world of rare but sweeping cascades of; that the amount of knowledge individuals have about the behavior of the population is crucial to shaping the probability of a cascade; and that variation in the myopia of "early followers" is considerably more important for determining the likelihood and comprehensiveness of sudden political transformations than the influence of first movers or the contribution of other plausible factors. [ABSTRACT FROM AUTHOR]

Demand Responsive Transport (DRT), covering the first/last mile of a journey, plays a pivotal role in the delivery of a seamless integrated door-to-door service, which is a fundamental requirement for the implementation of Mobility as a Service (MaaS). Business models currently in use do not deliver sustainable and durable DRT in urban areas. This can be minimised using transport modelling tools ahead of the operation phase. However, transport models are not fit for purpose when it comes to model on-demand shared mobility services and the integration of these services in a complex public transport ecosystem. This paper focuses on how to model demand for ride-shared mobility services and how to plan for these services when running in integration with mass transit. An Agent Based Model (ABM), built in the open-source Multi-Agent Transport Simulation (MatSim) platform for Bristol (UK), has used an activity-based approach to model demand for two New Mobility Services (NMS). This was then generated using anonymised and aggregated Mobile phone Network Dataset (MND), both as a trip-based and trip chains dataset to assess the capabilities of MND. Results show that the simulations built using the trip chains MND datasets (722,752 agents generated) lead to better insights in users' travel patterns. An advanced method using additional data sources covering land-use (location of business, services and transport facilities) was used to infer purpose and mode of transport during the multimodal journeys. The output of the ABM predicts demand for two flexible on-demand services, identifying best routes to maximise the number of users served and quantifying the benefits in the integration with public transport services and in modal shift from private cars. This is expected to be useful either for Local Authorities for transport planning purposes, and for operators looking at financially sustainable DRT. [ABSTRACT FROM AUTHOR]

Friendship formation between a pair of individuals (dyads) and its dynamics is a complex phenomenon that has been extensively studied in the literature. Assortative, relational, and proximity mechanisms are the recognized social processes that are responsible for the formation of these dyadic ties. In this paper, we develop an agent-based model that derives its essence from social processes, fuzzy game theory, and social network analysis. The process of dyadic friendship formation depends on the agent's (participant's) spatial configuration, attributes affecting friendship, and interaction with other agents. To model these interactions, we use fuzzy iterated prisoner's dilemma (Fuzzy-IPD) utilizing the decision factors contributing to similar social processes. These strategies have been independently discussed in previously published studies. We, however, use them in combination to design a descriptive model with different experimental settings in an ad-hoc simulation framework. Using an integrationist approach, we blend multiple individual components of friendship formation to construct a comprehensive model. The analysis of the model thus developed reveals a possible interaction pattern responsible for the evolution of the layered associations. This paper further validates various levels of friendship ties (strong ties, medium ties, and weak ties) in social networks as reported in the published sociological studies.

In the near future, various types of low-carbon technologies (LCTs) are expected to be widely employed throughout the United Kingdom. However, the effect that these technologies will have at a household level on the existing low voltage (LV) network is still an area of extensive research. We propose an agent based model that estimates the growth of LCTs within local neighbourhoods, where social influence is imposed. Real-life data from an LV network is used that comprises of many socially diverse neighbourhoods. Both electric vehicle uptake and the combined scenario of electric vehicle and photovoltaic adoption are investigated with this data. A probabilistic approach is outlined, which determines lower and upper bounds for the model response at every neighbourhood. This technique is used to assess the implications of modifying model assumptions and introducing new model features. Moreover, we discuss how the calculation of these bounds can inform future network planning decisions.

Urban Photovoltaic (PV) systems can provide large fractions of the residential electric demand at socket parity (i.e., a cost below the household consumer price). This is obtained without necessarily installing electric storage or exploiting tax funded incentives. The benefits of aggregating the electric demand and renewable output of multiple households are known and established; in fact, regulations and pilot energy communities are being implemented worldwide. Financing and managing a shared urban PV system remains an unsolved issue, even when the profitability of the system as a whole is demonstrable. For this reason, an agent-based modelling environment has been developed and is presented in this study. It is assumed that an optimal system (optimized for self-sufficiency) is shared between 48 households in a local grid of a positive energy district. Different scenarios are explored and discussed, each varying in number of owners (agents who own a PV system) and their pricing behaviour. It has been found that a smaller number of investors (i.e., someone refuse to join) provokes an increase of the earnings for the remaining investors (from 8 to 74% of the baseline). Furthermore, the pricing strategy of an agent shows improvement potential without knowledge of the demand of others, and thus it has no privacy violations.

This paper tries to show the various roles agent-based modeling and simulation (ABMS) can play in technology and policy assessment of energy systems. We examine the advantages of ABMS methods using three case studies of electricity market models as example (AMIRIS, EMLab-Generation and PowerACE). In particular, we argue why ABMS might serve as framework for many future energy system models that integrate many different algorithms. We then discuss practical and theoretical problems in the development, validation and assessment of energy-system-analytical ABMS and conclude with an outlook and recommendations for energy system modellers who consider incorporating ABMS into their modelling toolbox. [ABSTRACT FROM AUTHOR]

The energy transition in Germany takes part in decentral structures. With the ongoing integration of Renewable Energy Sources (RES) into the electricity supply system, supply-side is therefore becoming increasingly decentral and volatile due to the specific generation characteristics. A rather inflexible demand-side, on the other hand, increases the effort to gain the necessary equilibrium between generation and consumption. This paper discusses how consumer behaviour can be influenced by real-time pricing to align demand with generation. Therefore, a combination of two different approaches is used, (I) The Cellular Approach (CA) and (II) Agent Based Modelling (ABM). A model is set up considering a regional energy market, where regional electricity products can be traded peer-to-peer regarding each consumer’s preferences. The observation is made for a whole distribution grid including all types of consumers. The investigations show that energy purchases can be stimulated individually by a flexible pricing mechanism and met preferences. Moreover, benefits occur for the whole region and potentials arise to smooth the exchange balance to the superordinate grid level. Running the model for one entire year in a conservative generation scenario, hours of oversupply could be reduced by 18% and the consumption of green electricity generated regionally could be increased by over 125 MWh within the region itself, in comparison to a base scenario.

Agent based modelling has been widely accepted as a promising tool for urban planning purposes thanks to its capability to provide sophisticated insights into the social behaviours and the interdependencies that characterise urban systems. In this paper, we report on an agent based model, called TransMob, which explicitly simulates the mutual dynamics between demographic evolution, transport demands, housing needs and the eventual change in the average satisfaction of the residents of an urban area. The ability to reproduce such dynamics is a unique feature that has not been found in many of the like agent based models in the literature. TransMob, is constituted by six major modules: synthetic population, perceived liveability, travel diary assignment, traffic micro-simulator, residential location choice, and travel mode choice. TransMob is used to simulate the dynamics of a metropolitan area in South East of Sydney, Australia, in 2006 and 2011, with demographic evolution. The results are favourably compared against survey data for the area in 2011, therefore validating the capability of TransMob to reproduce the observed complexity of an urban area. We also report on the application of TransMob to simulate various hypothetical scenarios of urban planning policies. We conclude with discussions on current limitations of TransMob, which serve as suggestions for future developments.

Optimal functioning of a market oriented company, in particular the service providing company, is an important example of optimisation of actions within the context of complex environment. In this article we discuss the prospective approach to represent reliably the quality dynamics of such a company, in order to contribute to possible future its quality management. The agent-based modelling is extracted, among the set of modelling methods, to serve as a frame for representing the generic service providing company and to analyse its dynamics with emphasis on extracting the quality dynamics.

Abstract Metropolitan cities in India are emerging as major economic hubs with an unprecedented land use changes and decline of environmental resources. Globalisation and consequent relaxations of Indian markets to global players has given impetus to rapid urbanisation process. Urbanisation being irreversible and rapid coupled with fast growth of population during the last century, contributed to serious ecological and environmental consequences. This necessitates monitoring and advance visualisation of spatial patterns of landscape dynamics for evolving appropriate management strategies towards sustainable development approaches. This study visualises the growth of Indian mega cities Delhi, Mumbai, Pune, Chennai and Coimbatore, through Cellular Automata Markov model considering the influence of agent(s) of urban growth through soft computing techniques. CA Markov model is considered to be one of most effective algorithm to visualise the growth of urban spatial structures. Prediction of growth using agent based modelling considering the spatial patterns of urbanisation during the past four decades has provided insights to the urban dynamics. The industrial, infrastructural, socio-economic factors significantly influence the urban growth compared to the biophysical factors. Visualisation of urban growth suggest agents driven growth in the cities and its surroundings with large land use transformations in urban corridors and upcoming Industrial and ear marked developmental zones. Integrating local agents of urban growth help in identifying specific regions of intense growth, likely challenges and provide opportunities for evolving appropriate management strategies towards sustainable cities during the 21st century. [ABSTRACT FROM AUTHOR]

The aim of this study is to explore the applicability of Agent Based Modelling (ABM) for the simulation of rainfall runoff and soil erosion used in a watershed monitoring activity. The study utilizes Landsat 8 imagery for Land Use Land Cover (LULC) map generation, ASTER DEM for obtaining elevation information and Climate Forecast System Reanalysis (CFSR) 36 year weather data of Asan watershed, Uttarakhand, India. In the proposed model, four major agents (raindrops, soil, elevation and water amount) have been defined for estimating the soil erosion in the region. Moreover, the direct runoff has been simulated using the Soil Conservation Service (SCS) method. The analysis of the entire time series using this approach shows that there have been substantial changes in the rainfall runoff pattern primarily due to the varying environmental conditions of the study area since the late 1980s. Furthermore, a rough estimate of the soil erosion and deposition in the area have been computed which is aligned with the theory of sediment transport and deposition. In order to automate the entire model workflow, an open source cross platform tool has been developed using Python, R and NetLogo libraries. The Open Agent Based Runoff and Erosion Simulation (OARES) tool incorporates a generic interface for analysing large spatio-temporal datasets in watershed studies. The overall analysis concludes that the results obtained using ABM are comparable to that of the conventional hydrological models, and henceforth, ABM could be utilized as a future potential hydrological modelling paradigm. [ABSTRACT FROM AUTHOR]

Successfully implemented payment for ecosystem services (PES) programs can provide both conservation of nature and financial support to rural communities. In this paper, we explore how PES programs can be designed so as to maximize the amount of additional ecosystem services provided for a given budget. We also provide a brief summary of the use of auction mechanisms in real world PES programs. To explore the potential additionality of different PES program designs we use a conceptual agent based simulation model where payments are either fixed, or set through a uniform or discriminatory auction. The program can also be designed to target payments to land-owners based on their provision of ecosystem services. Theoretically, auctions should be the most effective design, especially if payments are differentiated and targeted by ecosystem service provision. However, what we find is that the context in which the PES program is implemented—baseline compliance with program standards among the participants, correlation between opportunity costs and ecosystem services in the landscape, heterogeneity in costs and budget size—has a determining impact on the relative effectiveness of the different payment designs, with fixed payments schemes being much more effective than auctions in certain settings. Our findings suggest that context should be taken into serious consideration when a PES program design is chosen. [ABSTRACT FROM AUTHOR]

Mathematical modelling provides mechanistic insight of an infirmity by emulating the course of disease on individual or group level under various interventions and hence makes pragmatic contribution to complement conventional biomedical research modalities. Subsequently, real-world effectiveness is observed in the prediction of experimental outcome that leads to optimized clinical therapies. This article addresses with the aid of mathematical modelling, the drug pharmacokinetic-pharmacodynamic along with treatment responses. This article uses an agent based model to discuss the effects of chemotherapy on angiogenesis as well as tumor microenvironment and establishes pertinency between numerical and experimental results. This study supports the emerging "discrete analysis " which, in the near future, is anticipated to be a promising major tool, for designing rational dosage regimes and effective dosage forms. [ABSTRACT FROM AUTHOR]

Typical approaches to wind turbines placement problem take into account the wind distribution and wake effects to maximise the total aggregate farm’s energy production in a centralised top–down optimisation problem. An alternative approach, however, is yet to be addressed as the problem can be instead modelled in a decentralised bottom–up manner emulating a system of self-adaptive agents. The potential advantages of this is that it offers easier scalability for high dimension problems as well as it enables an easier adaptation to the complex structure of the design problem. This paper contributes to this and presents an evolutionary algorithm to model and solve the wind farm layout design problem as a system of interrelated agents. The framework is applied to problems with different complexities where the quality of the results is examined. The convergence and scalability of the suggested technique indicate promising results for small to large scale wind farms, which, in turn, encourage the application of such an evolutionary based algorithm for real world wind farm design problem. [ABSTRACT FROM AUTHOR]