When the Wisdom of Crowd is Able to Overturn an Unpopular Norm? Lessons Learned from an Agent-Based Simulation

Kashif Zia, Umar Farooq, Alois Ferscha

Research output: Chapter in Book/Report/Conference proceedingConference proceedingspeer-review

Abstract

A model of bystanders' effect on volunteering (in case of an observed crime or critical situation) is extended to incorporate the possibility of a sense of guilt (after nonintervention). Based on sound theoretical and experimental foundations, an existing model of the spread of unpopular norms is used to allow dispersion of an unpopular norm (a mild crime) so that a population of agents may follow or accept it. The question asked is why a society (as a whole) is not able to overturn an unpopular norm through interventions. An agent-based model is proposed which captures all necessary ingredients to explore this question. Several what-if questions are asked by varying simulation parameters. The model and simulation reveal that a sense of guilt of bystanders improves the volunteering tendency. The simulation results also provide pieces of evidence of clear differentiation between the individual and societal perception of a crime. Through the simulation results, we were able to conclude that guilt, not only, improves the volunteering tendency, but also, very clearly differentiates between the individual and societal perception of an unpopular norm. Overall, it was learned that people intervene only when they are able to overcome the inhibitions of the crowd. However, even interventions do not guarantee to overturn an unpopular norm. In fact, irrespective of the state of interventions, there is no neutralization without guilt.
Original languageEnglish
Title of host publicationSIGSIM-PADS '21: Proceedings of the 2021 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation
PublisherACM DL
Pages69-79
Number of pages11
DOIs
Publication statusPublished - May 2021

Fields of science

  • 202017 Embedded systems
  • 102 Computer Sciences
  • 102009 Computer simulation
  • 102013 Human-computer interaction
  • 102019 Machine learning
  • 102020 Medical informatics
  • 102021 Pervasive computing
  • 102022 Software development
  • 102025 Distributed systems
  • 211902 Assistive technologies
  • 211912 Product design

JKU Focus areas

  • Digital Transformation

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