Wolf Ketter's Publications

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Product Pricing using Adaptive Real-Time Probability of Acceptance Estimations based on Economic Regimes

Alexander Hogenboom, Wolfgang Ketter, Jan van Dalen, Uzay Kaymak, John Collins, and Alok Gupta. Product Pricing using Adaptive Real-Time Probability of Acceptance Estimations based on Economic Regimes. In Eleventh International Conference on Electronic Commerce (ICEC 2009), pp. 176–185, ACM, August 2009. Best Paper Award Nomination.

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Abstract

In today's complex supply chains, product pricing is a vital, yet non-trivial task. We propose a product pricing approach using adaptive real-time probability of acceptance estimations based on economic regimes. Radial Basis Function Networks are trained to estimate parameters for double-bounded log-logistic distributions assumed to be underlying daily offer prices, using information available real-time. The relation between data and parameters is dynamically modeled using economic regimes (characterizing market conditions) and error terms (accounting for customer feedback). Given the parametric approximations of price distributions, acceptance probabilities are estimated using a closed-form mathematical expression, which is used to determine the price yielding a desired quota. The approach is implemented in the MinneTAC agent and tested against a price-following product pricing method in the TAC SCM game. Performance significantly improves; more customer orders are obtained against higher prices and profits more than double.

@inproceedings{AHogenboom09ICEC,
  author =       {Alexander Hogenboom and Wolfgang Ketter and Jan van Dalen and Uzay Kaymak and John Collins and Alok Gupta},
  title =        {{Product Pricing using Adaptive Real-Time Probability of Acceptance Estimations based on Economic Regimes}},
  booktitle =    {{Eleventh International Conference on Electronic Commerce (ICEC 2009)}},
  year =         {2009},
  pages =        {176--185},
  month =        {August},
  publisher =    {{ACM}},
  note =         {Best Paper Award Nomination.},
  abstract =     {{In today's complex supply chains, product pricing is a
  vital, yet non-trivial task. We propose a product pricing approach using
  adaptive real-time probability of acceptance estimations based on economic
  regimes. Radial Basis Function Networks are trained to estimate parameters
  for double-bounded log-logistic distributions assumed to be underlying daily
  offer prices, using information available real-time. The relation between
  data and parameters is dynamically modeled using economic regimes
  (characterizing market conditions) and error terms (accounting for customer
  feedback). Given the parametric approximations of price distributions,
  acceptance probabilities are estimated using a closed-form mathematical
  expression, which is used to determine the price yielding a desired quota.
  The approach is implemented in the MinneTAC agent and tested against a
  price-following product pricing method in the TAC SCM game. Performance
  significantly improves; more customer orders are obtained against higher
  prices and profits more than double.}},
  keywords =     {{Dynamic pricing, economic regimes, machine learning, supply chain management, TAC SCM}},
  bib2html_pubtype = {Refereed Conference},
  bib2html_rescat = {Trading Agents: Supply-Chain Management},
}

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