IDEAS home Printed from
MyIDEAS: Log in (now much improved!) to save this article

Learning and affective responses in location-choice dynamics

Listed author(s):
  • Qi Han
  • Theo Arentze
  • Harry J P Timmermans
Registered author(s):

    In this paper we discuss the development of a dynamic agent-based model which simulates how agents search and explore in nonstationary environments and ultimately develop habitual, context-dependent, activity–travel patterns. Conceptually, the creation of a choice set is context dependent. Individuals are assumed to have aspiration levels associated with location attributes that, in combination with evaluation results, determine whether the agent will start exploring or persist in habitual behavior. An awareness level of each location determines whether or not it is included in the awareness set in the next time step. An activation level of each location determines whether or not it is qualified as a habitual choice, and an evaluation (utility) function allows individuals to evaluate each location given current beliefs. By implementing choices, agents may observe the differences between actual experience and expectation, which may give rise to negative or positive emotions that influence the awareness of locations and the evaluation, and hence trigger choice change. Principles of reinforcement and Bayesian belief learning are used to simulate the dynamics. The result of these behavior mechanisms is the evolution of choice sets and choice patterns, reflecting emergent behavior in relation to nonstationary environments. We report the results of a case study, implemented in an agent-based microsimulation system, of dynamic decision making of avoiding higher uncertainty in location choice, distinguishing habitual, exploitation, and exploration modes of choice behavior. Simulations indicate that solutions generated by the model are sensitive to rational and emotional considerations in decision making in well-interpretable ways. The suggested approach is scalable in the sense that it is applicable to study areas of large size (eg, region wide). Keywords: habit formation, emotional value, location choice, learning

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

    File URL:
    File Function: abstract
    Download Restriction: Fulltext access restricted to subscribers, see for details

    File URL:
    File Function: main text
    Download Restriction: Fulltext access restricted to subscribers, see for details

    As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

    Article provided by Pion Ltd, London in its journal Environment and Planning B: Planning and Design.

    Volume (Year): 40 (2013)
    Issue (Month): 1 (January)
    Pages: 78-94

    in new window

    Handle: RePEc:pio:envirb:v:40:y:2013:i:1:p:78-94
    Contact details of provider: Web page:

    No references listed on IDEAS
    You can help add them by filling out this form.

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    When requesting a correction, please mention this item's handle: RePEc:pio:envirb:v:40:y:2013:i:1:p:78-94. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Neil Hammond)

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.