Abstract

The mining industry has difficulties predicting changes in the level of community acceptance of its projects over time. These changes are due to changes in the society and individual perceptions around these mines as a result of the mines' environmental and social impacts. Agent-based modeling can be used to facilitate better understanding of how community acceptance changes with changing mine environmental impacts. This work investigates the sensitivity of an agent-based model (ABM) for predicting changes in community acceptance of a mining project due to information diffusion to key input parameters. Specifically, this study investigates the responsiveness of the ABM to average degree (total number of friends) of the social network, close neighbour ratio (a measure of homophily in the social network) and number of early adopters ("innovators"). A two-level full factorial experiment was used to investigate the sensitivity of the model to these parameters. The primary (main), secondary and tertiary effects of each parameter were estimated to assess the model's sensitivity. The results show that the model is more responsive to close neighbour ratio and number of early adopters than average degree. Consequently, uncertainty surrounding the inferences drawn from simulation experiments using the agent-based model will be minimized by obtaining more reliable estimates of close neighbour ratio and number of early adopters. While it is possible to reliably estimate the level of early adopters from the literature, the degree of homophily (close neighbour ratio) has to be estimated from surveys that can be expensive and unreliable. Further, work is required to find economic ways to document relevant degrees of homophily in social networks in mining communities.

Department(s)

Mining and Nuclear Engineering

Keywords and Phrases

Agent-based modeling; Diffusion; Mining; Mining community; Sensitivity analysis

International Standard Serial Number (ISSN)

1460-7425

Document Type

Article - Journal

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2017 University of Surrey, All rights reserved.

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