Mitic, Peter;
(2021)
Reputation auto-correlation: Implications for simulation.
In: Armenia, Stefano and Geril, Philippe, (eds.)
Proceedings of the 35th Annual European Simulation and Modelling Conference 2021, ESM 2021.
(pp. pp. 224-231).
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Abstract
Previous work has established that the distribution of daily reputation scores is best modelled by a bi-partite pair of exponential distributions. Simulations developed from that distributional model did not account for autocorrelations in the data. We now extend the bi-partite model in two ways. Candidate auto-correlation methods are assessed in order to incorporate the auto-correlation structure of the data in a simulation. Negative reputational shocks are then modelled using a chi-square distribution, so that they can then adequately model runs of successive days of either positive or negative sentiment. Auto-correlation goodness-of-fit tests show that the optimal auto-correlation model uses the fitted auto-regression components of the original data, and that goodness-of-fit can be improved by inflating them by about 1%. This optimised model is successful in at least 88% of simulations where auto-correlation in the original data does not extend beyond 10 lags. In other cases (mainly due to severe reputational shock), 80% success can be expected. Examples of shock simulations for large corporate organisations are shown, and the implications for reputational analysis are discussed.
Type: | Proceedings paper |
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Title: | Reputation auto-correlation: Implications for simulation |
Event: | 35th Annual European Simulation and Modelling Conference 2021, ESM 2021 |
ISBN-13: | 9789492859181 |
Open access status: | An open access version is available from UCL Discovery |
Publisher version: | https://www.eurosis.org/conf/esm/2021/index.html |
Language: | English |
Additional information: | This version is the version of record. For information on re-use, please refer to the publisher's terms and conditions. |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/10163338 |
1. | Russian Federation | 3 |
2. | United States | 2 |
3. | Ethiopia | 1 |
4. | South Africa | 1 |
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