Abstract
One of the main interdisciplinary challenges today is to understand and change the dominant social perceptions and values that support and perpetuate unsustainable practices. Social computational simulations have been conceived in recent years to understand emergent results from complex systems. These dynamic social models are of interest to sustainability researchers because they provide a means to implement hypotheses and explore scenarios that could help extend our understanding of the future role of change agency in society. Change agents are individuals who directly or indirectly enable sustainable behaviors or inhibit practices that damage the environment and large social groups. Evidence-based strategies, guidelines and methods are necessary in order to manage creative change agency more effectively. This paper presents work with computational simulations, known as cellular automata, in order to explore the role of timing in triggering social change through uncoordinated, autonomous individual action. The paper identifies a number of issues related to creative change agency and proposes associated guidelines for practitioners. As a means of early validation, these findings are portrayed against empirical studies in the literature.
Highlights
Relevance to Practice: The computational social models discussed in this paper provide actionable insights that could be useful to creative practitioners who aim to trigger change towards sustainability in their professional contexts
The main advantages of computational simulations include: the requirement of explicitness, as every modeling detail needs to be defined algorithmically; the exploration of scenarios, as the system can be executed iteratively by traversing every variable range and assessing the output; the replication of studies enabled by its experimental nature; and the possibility of conducting studies on controversial issues, or situations that are impossible to control with human subjects [2,3]
This paper presents insights drawn from extensive experimentation with cellular automata (CA)
Summary
Computational simulation has had a significant impact in the social sciences in the last two decades. The main advantages of computational simulations include: the requirement of explicitness, as every modeling detail needs to be defined algorithmically; the exploration of scenarios, as the system can be executed iteratively by traversing every variable range and assessing the output; the replication of studies enabled by its experimental nature; and the possibility of conducting studies on controversial issues, or situations that are impossible to control with human subjects [2,3] Beyond these methodological aspects, the main modeling advantage is the possibility of capturing the causal link between complex emergent system outcomes and the interaction of simple constituents over time [4,5]. The insights on future change agency presented here are fuzzy by nature, but highly important as they help increase our understanding and formulate research questions for the future
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