Year: 2019
Type: preprint
Abstract: In many real life decisions, options are distributed in space and time, making itnecessary to sequentially search through them, often without a chance to return to arejected option. The optimal strategy in these tasks is to choose the first option that isabove a threshold that depends on the current position in the sequence. The implicitdecision making strategies by humans vary but largely diverge from this optimalstrategy; the reasons for this divergence remain unknown. We present a new model ofhuman stopping decisions in sequential decision making tasks based on a linearthreshold heuristic. We show that the new model outperforms existing models forsequential decision making. Moreover, it accurately predicts participants’ search length,and how they adapt it to different environments. It thus provides an important steptowards understanding human sequential decision making.
Authors: Christiane Baumann, Samuel J. Gershman, Henrik Singmann, Bettina von Helversen
Institutions: Harvard University, University of Zurich
Language: English
Subfield: General Decision Sciences
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W4253138699