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Active Management of Operational Risk in the Regimes of the “Unknown”: What Can Machine Learning or Heuristics Deliver?
16 references·0 citing·42 second hop
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References

  • 2009Power-Law Distributions in Empirical Data8,536 cited
  • 2018Power-law distributions in empirical data6,710 cited
  • 2010Heuristic Decision Making4,028 cited
  • 2014Algorithm Aversion: People Erroneously Avoid Algorithms after Seeing Them Err3,203 cited
  • 2009Homo Heuristicus: Why Biased Minds Make Better Inferences1,799 cited
  • 2015Risk assessment and risk management: Review of recent advances on their foundation1,710 cited
  • 2016Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them1,225 cited
  • 2010ISO 31000:2009—Setting a New Standard for Risk Management475 cited
  • 2011Heuristics467 cited
  • 2006Quantitative models for operational risk: Extremes, dependence and aggregation265 cited
  • 2004The Modelling of Operational Risk: Experience with the Analysis of the Data Collected by the Basel Committee251 cited
  • 2006Infinite-mean models and the LDA for operational risk195 cited
  • 2007The Quantitative Modeling of Operational Risk: Between G-and-H and EVT125 cited
  • 2014The generalized Pareto process; with a view towards application and simulation124 cited
  • 2008A review of different perspectives on uncertainty and risk and an alternative modeling paradigm

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  • 2005Implications of Alternative Operational Risk Modeling Techniques76 cited
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