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Social Dynamics of Littering and Adaptive Cleaning Strategies Explored Using Agent-Based Modelling

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In this paper we explore how social influence may cause a non-linear transition from a clean to a littered environment, and what strategies are effective in keeping a street clean. To study this, we first implement the Goal Framing Theory of Lindenberg and Steg (2007) in an agent based model. Next, using empirical data from a field study we parameterise the model so we can replicate the results from a field study. Following that, we explore how different cleaning strategies perform. The results indicate that an adaptive/dynamical cleaning regime is more effective and cheaper than pre-defined cleaning schedules.

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  • Ruggero Rangoni & Wander Jager, 2017. "Social Dynamics of Littering and Adaptive Cleaning Strategies Explored Using Agent-Based Modelling," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(2), pages 1-1.
    • Handle: RePEc:jas:jasssj:2015-67-3
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    1. Robert Dur & Ben Vollaard, 2012. "The Power of a Bad Example - A Field Experiment in Household Garbage Disposal," Tinbergen Institute Discussion Papers 12-061/1, Tinbergen Institute, revised 10 Apr 2014.
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    1. Keiron P. Roberts & Sui C. Phang & John B. Williams & David J. Hutchinson & Simon E. Kolstoe & Jasper Bie & Ian D. Williams & Anne M. Stringfellow, 2022. "Increased personal protective equipment litter as a result of COVID-19 measures," Nature Sustainability, Nature, vol. 5(3), pages 272-279, March.
    2. Niamir, Leila & Filatova, Tatiana & Voinov, Alexey & Bressers, Hans, 2018. "Transition to low-carbon economy: Assessing cumulative impacts of individual behavioral changes," Energy Policy, Elsevier, vol. 118(C), pages 325-345.

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