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). EUROSIS

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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.

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