A simple, dynamic extension of temporal motivation theory

Christopher R. Dishop

The Journal of Mathematical Sociology, 2020, vol. 44, issue 3, 147-162

Abstract: Temporal motivation theory (TMT) has been criticized for its static representation and neglect of the environment. In this paper, I develop goal sampling theory (GST) to appease these criticisms and extend our understanding of goal choices beyond momentary preferences and into dynamic updating and sampling behavior across time. GST draws from temporal motivational theory, sampling models of impression formation, and organizational theory on how the environment constrains behavior and situates aspects of each into a formal model of goal sampling. Doing so addresses the limitations of our prior thinking, introduces new concepts and predictions, and provides a mathematical framework that lends itself to computational modeling.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/0022250X.2019.1666268 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:gmasxx:v:44:y:2020:i:3:p:147-162

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/gmas20

DOI: 10.1080/0022250X.2019.1666268

Access Statistics for this article

The Journal of Mathematical Sociology is currently edited by Noah Friedkin

More articles in The Journal of Mathematical Sociology from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().