Automating agent-based modeling : data-driven generation and application of innovation diffusion models

Simulation modeling is useful to understand the mechanisms of the diffusion of innovations, which can be used for forecasting the future of innovations. This study aims to make the identification of such mechanisms less costly in time and labor. We present an approach that automates the generation of diffusion models by: (1) preprocessing of empirical data on the diffusion of a specific innovation, taken out by the user; (2) testing variations of agent-based models for their capability of explaining the data; (3) assessing interventions for their potential to influence the spreading of the innovation. We present a working software implementation of this procedure and apply it to the diffusion of water-saving showerheads. The presented procedure successfully generated simulation models that explained diffusion data. This progresses agent-based modeling methodologically by enabling detailed modeling at relative simplicity for users. This widens the circle of persons that can use simulation to shape innovation

Knowledge sharing in smart grid pilot projects

The major role that the electrification of the energy system is projected to play in the transition to a sustainable economy increases the pressure on the electricity grid and thereby creates a demand for the implementation of smart grid technologies. The interdependencies present in the electricity system require, and have led to, the wide-scale adoption of pilot projects to develop knowledge about the application of these technologies. While the knowledge sharing that stems from these projects is one of the justifications for subsidising these projects, it has remained largely a black box. Based on the analysis of interviews with the project leaders of sixteen smart grid pilot projects, complementary secondary data sources and a survey, we studied knowledge sharing at four levels: intra-organisational, intra-project, inter-project and project-external knowledge sharing. At each level we observed specific sublevels, mechanisms and barriers, resulting in complex knowledge sharing dynamics. While the projects succeeded in developing knowledge, knowledge sharing between projects run by different consortium partners rarely occurred and project-external knowledge sharing was primarily unidirectional and involved generic knowledge. Based on the results a set of recommendations was developed that can stimulate the knowledge sharing and thereby increase the value generated by these projects

On infrastructure network design with agent-based modelling

We have developed an agent-based model to optimize green-field network design in an industrial area. We aim to capture some of the deep uncertainties surrounding infrastructure design by modelling it developing specific ant colony optimizations. Hence, we propose a variety of extensions to our existing work, first ideas on how to realize them and three cases to explicate our ideas. One case is the design of a CO2 pipeline network in Rotterdam industrial area. First simulation results have shown the relevance of the approach. Keywords-Infrastructure design, network planning, ant colony optimization, deep uncertainty, socio-technical systems

Social agents?:A systematic review of social identity formalizations

Simulating collective decision-making and behaviour is at the heart of many agent-based models (ABMs). However, the representation of social context and its influence on an agent’s behaviour remains challenging. Here, the Social Identity Approach (SIA) from social psychology, offers a promising explanation, as it describes how people behave while being part of a group, how groups interact and how these interactions and ingroup norms can change over time. SIA is valuable for various application domains while also being challenging to formalise. To address this challenge and enable modellers to learn from existing work, we took stock of ABM formalisations of SIA and present a systematic review of SIA in ABMs. Our results show a diversity of application areas and formalisations of (parts of) SIA without any converging practice towards a default formalisation. Models range from simple to (cognitively) rich, with a group of abstract models in the tradition of opinion dynamics employing SIA to specify group-based social influence. We also found some complex cognitive SIA formalisations incorporating contextual behaviour. When considering the function of SIA in the models, representing collectives, modelling group-based social influence and unpacking contextual behaviour all stood out. Our review was also an inventory of the formalisation challenge attached to using a very promising socialpsychological theory in ABMs, revealing a tendency for reference to domain-specific theories to remain vague

Editorial: Tipping points in societies

Energie and Industri

Computational models that matter during a global pandemic outbreak: A call to action

The COVID-19 pandemic is causing a dramatic loss of lives worldwide, challenging the sustainability of our health care systems, threatening economic meltdown, and putting pressure on the mental health of individuals (due to social distancing and lock-down measures). The pandemic is also posing severe challenges to the scientific community, with scholars under pressure to respond to policymakers’ demands for advice despite the absence of adequate, trusted data. Understanding the pandemic requires fine-grained data representing specific local conditions and the social reactions of individuals. While experts have built simulation models to estimate disease trajectories that may be enough to guide decision-makers to formulate policy measures to limit the epidemic, they do not cover the full behavioural and social complexity of societies under pandemic crisis. Modelling that has such a large potential impact upon people’s lives is a great responsibility. This paper calls on the scientific community to improve the transparency, access, and rigour of their models. It also calls on stakeholders to improve the rapidity with which data from trusted sources are released to the community (in a fully responsible manner). Responding to the pandemic is a stress test of our collaborative capacity and the social/economic value of research

“We don't need no (higher) education” - How the gig economy challenges the education-income paradigm

The empirical relationship between educational attainment and pay levels has been widely acknowledged in the labour-economic and labour-sociology literatures. While the causalities underlying this relationship are not conclusively established, researchers broadly agree that higher educational attainment leads to higher income levels in dependent employment, temporary hiring, and freelancing alike. The ‘gig economy’, where workers complete jobs mediated by online platforms, challenges this paradigm as gig workers can access jobs without any educational certificates. Building a theoretical framework based on agency-driven accounts, we investigate whether we can empirically observe a relationship between educational attainment and wage levels in the gig economy. Our OLS regression analyses of 1607 gig workers in 14 Western economies illustrate no statistically significant correlation. Instead, the platform's review system as well as the gig workers' level of previous job experience serve as the major signalling mechanisms that help to reduce information asymmetry. Qualitative insights gained from in-depth interviews explain this finding by revealing how gig workers gain the necessary qualifications for their jobs, most importantly, through self-study, learning-by-doing, and trial-and-error processes. Our findings therefore point out that advanced educational credentials are only of limited use for gig workers