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Evolving Greenhouses: An Agent-Based Model of Universal Darwinism in Greenhouse Horticulture

J. Kasmire, Igor Nikolic and Gerard Dijkema
Journal of Artificial Societies and Social Simulation 16 (4) 7

Abstract: To explore the space between the theories of the Diffusion of Innovations and Universal Darwinism, we first examine a case study of the history of the greenhouse horticulture sector of the Netherlands, comparing and contrasting the narrow focus of Diffusion of Innovations and the wider focus of Universal Darwinism. We then build an agent-based model using elements of both in order to test how well the Diffusion of Innovations theory holds up when some of its simplifications are removed. Results show that the single, simple pattern prominent in Diffusions of Innovations theory does emerge, but that it is only one of several patterns and that it does not behave precisely as expected. Results also show agent properties, such as stubbornness or innovativeness, can be surprisingly complex, as when stubbornness shows an advantage in the long term, while innovativeness was beneficial to the network but not to the innovator. While the Diffusion of Innovations theory is simple and can easily guide policy decisions, this paper shows that adding complexity to place diffusions inside a larger evolutionary context results in more realistic analysis and can help policy-makers to achieve challenging goals amidst modern economic and political challenges.

Structuring Qualitative Data for Agent-Based Modelling

Amineh Ghorbani, Gerard Dijkema and Noortje Schrauwen
Journal of Artificial Societies and Social Simulation 18 (1) 2

Abstract: Using ethnography to build agent-based models may result in more empirically grounded simulations. Our study on innovation practice and culture in the Westland horticulture sector served to explore what information and data from ethnographic analysis could be used in models and how. MAIA, a framework for agent-based model development of social systems, is our starting point for structuring and translating said knowledge into a model. The data that was collected through an ethnographic process served as input to the agent-based model. We also used the theoretical analysis performed on the data to define outcome variables for the simulation. We conclude by proposing an initial methodology that describes the use of ethnography in modelling.

The Value of Values and Norms in Social Simulation

Rijk Mercuur, Virginia Dignum and Catholijn Jonker
Journal of Artificial Societies and Social Simulation 22 (1) 9

Abstract: Social simulations gain strength when agent behaviour can (1) represent human behaviour and (2) be explained in understandable terms. Agents with values and norms lead to simulation results that meet human needs for explanations, but have not been tested on their ability to reproduce human behaviour. This paper compares empirical data on human behaviour to simulated data on agents with values and norms in a psychological experiment on dividing money: the ultimatum game. We find that our agent model with values and norms produces aggregate behaviour that falls within the 95% confidence interval wherein human behaviour lies more often than other tested agent models. A main insight is that values serve as a static component in agent behaviour, whereas norms serve as a dynamic component.

Sustaining Collective Action in Urban Community Gardens

Arthur Feinberg, Elena Hooijschuur, Nicole Rogge, Amineh Ghorbani and Paulien Herder
Journal of Artificial Societies and Social Simulation 24 (3) 3

Abstract: This paper presents an agent-based model that explores the conditions for ongoing participation in community gardening projects. We tested the effects of Ostrom's well-known Design Principles for collective action and used an extensive database collected in 123 cases in Germany and two case studies in the Netherlands to validate it. The model used the Institutional Analysis and Development (IAD) framework and integrated decision mechanisms derived from the Theory of Reasoned Action (TRA). This allowed us to analyse volunteer participation in urban community gardens over time, based on the garden's institutions (Design Principles) and the volunteer's intention to join gardening. This intention was influenced by the volunteer's expectations and past experiences in the garden (TRA). We found that not all Design Principles lead to higher levels of participation but rather, participation depends on specific combinations of the Design Principles. We highlight the need to update the assumption about sanctioning in such systems: sanctioning is not always beneficial, and may be counter-productive in certain contexts.

Long-Term Dynamics of Institutions: Using ABM as a Complementary Tool to Support Theory Development in Historical Studies

Molood Ale Ebrahim Dehkordi, Amineh Ghorbani, Giangiacomo Bravo, Mike Farjam, René van Weeren, Anders Forsman and Tine De Moor
Journal of Artificial Societies and Social Simulation 24 (4) 7

Abstract: Historical data are valuable resources for providing insights into social patterns in the past. However, these data often inform us at the macro-level of analysis but not about the role of individuals’ behaviours in the emergence of long-term patterns. Therefore, it is difficult to infer ‘how’ and ‘why’ certain patterns emerged in the past. Historians use various methods to draw hypotheses about the underlying reasons for emerging patterns and trends, but since the patterns are the results of hundreds if not thousands of years of human behaviour, these hypotheses can never be tested in reality. Our proposition is that simulation models and specifically, agent-based models (ABMs) can be used as complementary tools in historical studies to support hypothesis building. The approach that we propose and test in this paper is to design and configure models in such a way as to generate historical patterns, consequently aiming to find individual-level explanations for the emerging pattern. In this work, we use an existing, empirically validated, agent-based model of common pool resource management to test hypotheses formulated based on a historical dataset. We first investigate whether the model can replicate various patterns observed in the dataset, and second, whether it can contribute to a better understanding of the underlying mechanism that led to the observed empirical trends. We showcase how ABM can be used as a complementary tool to support theory development in historical studies. Finally, we provide some guidelines for using ABM as a tool to test historical hypotheses.

How Culture Influences the Management of a Pandemic: A Simulation of the COVID-19 Crisis

Kurt Kreulen, Bart de Bruin, Amineh Ghorbani, René Mellema, Christian Kammler, Lois Vanhée, Virginia Dignum and Frank Dignum
Journal of Artificial Societies and Social Simulation 25 (3) 6

Abstract: Since its first appearance in Wuhan (China), countries have been employing, to varying degrees of success, a series of non-pharmaceutical interventions aimed at limiting the spread of SARS-CoV-2 within their populations. In this article, we build on scientific work that demonstrates that culture is part of the explanation for the observed variability between countries in their ability to effectively control the transmission of SARS-CoV-2. We present a theoretical framework of how culture influences decision-making at the level of the individual. This conceptualization is formalized in an agent-based model that simulates how cultural factors can combine to produce differences across populations in terms of the behavioral responses of individuals to the COVID-19 crisis. We illustrate that, within our simulated environment, the culturally-dependent willingness of people to comply with public health related measures might constitute an important determinant of differences in infection dynamics across populations. Our model generates the highest rates of non-compliance within cultures marked as individualist, progressive and egalitarian. Our model illustrates the potential role of culture as a population-level predictor of infections associated with COVID-19. In doing so, the model, and theoretical framework on which it is based, may inform future studies aimed at incorporating the effect of culture on individual decision-making processes during a pandemic within social simulation models.