Co-learning segmentation in marketplaces


Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

3 Citations (Scopus)


We present the problem of automatic co-niching in which potential suppliers of some product or service need to determine which offers to make to the marketplace at the same time as potential buyers need to determine which offers (if any) to purchase. Because both groups typically face incomplete or uncertain information needed for these decisions, participants in repeated market interactions engage in a learning process, making tentative decisions and adjusting these in the light of experiences they gain. Perhaps surprisingly, real markets typically then exhibit a form of parallel clustering: buyers cluster into segments of similar preferences and buyers into segments of similar offers. For computer scientists, the interesting question is whether such co-niching behaviours can be automated. We report on the first simulation experiments showing automated co-niching is possible using reinforcement learning in a multi-attribute product model. The work is of relevance to designers of online marketplaces, of computational resource allocation systems, and of automated software trading agents. © 2012 Springer-Verlag Berlin Heidelberg.
Original languageEnglish
Title of host publicationAdaptive and Learning Agents : AAMAS 2011 International Workshop, ALA 2011, Taipei, Taiwan, May 2, 2011, Revised Selected Papers
EditorsPeter VRANCX, Matthew KNUDSON, Marek GRZEŚ
PublisherSpringer Berlin Heidelberg
Number of pages20
ISBN (Electronic)9783642284991
ISBN (Print)9783642284984
Publication statusPublished - 2012
Externally publishedYes
Event2011 International Workshop on Adaptive and Learning Agents, ALA 2011 - Taipei, Taiwan, Province of China
Duration: 2 May 20112 May 2011

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin, Heidelberg
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Workshop2011 International Workshop on Adaptive and Learning Agents, ALA 2011
Country/TerritoryTaiwan, Province of China


  • Reinforcement Learning
  • Potential Customer
  • Resource Type
  • Potential Buyer
  • Limit Price


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